impacts evaluation plan - eliptic project d3.1 impacts... · figure 23 - cluster 1, common kpis for...

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 636012.

D. 3.1 – Impacts Evaluation Plan

Impacts Evaluation Plan

Deliverable 3.1

Authors Maria Vittoria Corazza Antonio Musso

Status (D: draft; F: final) F

Document’s privacy

(Public: PU; Private: PR) PU

Reviewed by

Ref. Ares(2016)2898079 - 22/06/2016

30 Nov-15 Version 2.2 / Drafted by UNIROMA1 | 1


SUMMARY SHEET Programme Horizon 2020

Contract N. 636012

Project Title Electrification of public transport in cities

Acronym ELIPTIC

Coordinator Free Hanseatic City Of Bremen

Web-site http://www.eliptic-project.eu/

Starting date 1 June 2015

Number of months 36 months

Deliverable N. 3.1

Deliverable Title Impacts Evaluation Plan

Milestones

Version 1.0

Date of issue November 30th, 2015

Distribution [Internal/External]

Dissemination level [Public/ Confidential] Public

Abstract This report deals with the activities, within the ELIPTIC evaluation process, to plan the assessment of impacts due to the outcomes of the ELIPTIC demonstrators and feasibility studies. Chapter 1 describes the methodology adopted to assess such impacts. It plans to carry out a “conventional full evaluation” for the ELIPTIC demonstrators and a “technological viability evaluation” for the ELIPTIC feasibility studies. In both cases evaluation categories are defined and the impact areas the performance variations are expected to affect. Such variations are measured by a set of Key Performance Indicators (KPIs), each associated to a specific evaluation category and impact area. A set of Context Parameters (CPs) enables a full description of the main technical features of the context of implementation of the ELIPTIC measures and integrates the KPIs. The lists of proposed KPIs and CPs are reported in Annexes 1 and 2, respectively. Chapter 2 extensively reports the current selection of CPs and KPIs for each demonstrator and feasibility study, along with proposed additional KPIs, data collection schedules and test scenarios. Accordingly, Chapter 3 summarizes the main research issues which will have to be tackled in the further development of the assessment. More specifically the need to enlarge the current set of selected KPIs and to settle those which are still uncertain to improve the accuracy of the overall performance evaluation, to cover less-thus-far-favored but important impact areas, and progress with the definition of the test scenarios is remarked in Chapter 4.

Keywords: Evaluation, Key Performance Indicators – KPIs, Context Parameters – CPs, ELIPTIC Use Cases - EUC

Critical risks

This report is subject to a disclaimer and copyright. This report has been carried out under a contract awarded by the European Commission, contract number: 636012

http://www.eliptic-project.eu/



DOCUMENT CHANGE LOG Version Date Main area of changes Organization Comments

1.0 October 29th, 2015 UNIROMA1 Creation of structure, draft and main content

2.0 November 16th, 2015 Chapter 3 UNIROMA1 Revision according to the outcomes of the partners meeting held in Leipzig, on November 11-12, 2015.

2.1 November 27th, 2015 Section 3.4.2 and Chapter 4

UNIROMA1 Update of the KPIs selection for Barcelona C3 and consequent revision of the relevant text and tables in Chapter 4

2.2 January, 28th, 2016 All RUPPRECHT Only minor changes based on review and quality assurance



PARTNER CONTRIBUTION Company Sections Description of the partner contribution

UNIROMA1 All Text, Tables and pictures

BSAG and Berends

3.1 Contribution of data, KPIs and information for EUC Bremen

TfL 3.2 Contribution of data, KPIs and information for EUC London

STIB and VUB 3.3 Contribution of data, KPIs and information for EUC Brussels

TMB, UPC, CENIT and BSM

3.4 Contribution of data, KPIs and information for EUC Barcelona

LVB and Fraunhofer

3.6 Contribution of data, KPIs and information for EUC Leipzig

STOAG and Berends

3.7 Contribution of data, KPIs and information for EUC Oberhausen

UG 3.8 Contribution of data, KPIs and information for EUC Gdynia

Fraunhofer 3.9 Contribution of data, KPIs and information for EUC Eberswalde

USZ 3.10 Contribution of data, KPIs and information for EUC Szeged

FAS 3.11 Contribution of data, KPIs and information for EUC Lanciano

RWTH 2, Annex Contribution to the development of the list of KPIs

RUPPRECHT All Review and quality assurance



Table of Contents 1. Executive Summary 8 2. The Methodology for the Evaluation of the ELIPTIC Use Cases 9

2.1 Task 3.1 - Impacts Evaluation Plan contents 9 2.2 The Thematic Technological Pillars requirements and the identification of impacts 10

2.2.1 Impact levels 13 2.3 The selection of the Key Performance Indicators 14

2.3.1 Additional indicators: the Context Parameters 17 2.3.2 Homogenization of the EUCs 18

2.4 Definition of the evaluation scenarios 19 2.4.1 Conventional Full Evaluation 19 2.4.2 Technological Viability Evaluation 20 2.5 Data collection criteria 20

3. The Preliminary Selection of KPIs for the ELIPTIC Use Cases 22 3.1 Bremen 22

3.1.1. Bremen A1. Operation-optimized system of opportunity charging at bus depots 22 3.1.2. Bremen B1. Recuperation of braking energy from trams: refurbishment of a flywheel energy storage system 26 3.1.3. Bremen C1. From uniqueness to system: extension of existing multimodal mobility hub station 30

3.2 London 32 3.2.1. London A2. Opportunity (re)charging of ebuses and/or plug-in hybrid buses (using metro infrastructure) 32 3.2.2. London C2. Use of metro sub-station for(re)charging TfL fleet vehicles (e-cars and e-vans) and zero-emission capable taxis 39

3.3 Brussels 41 3.3.1. Brussels A3. Progressive electrification of hybrid bus network, using existing tram and underground electric infrastructure 42 3.3.2. Brussels B2. Optimised braking energy recovery in light rail network 48

3.4 Barcelona 54 3.4.1. Barcelona A4. Opportunity (re)charging of electric buses based on metro infrastructure 54 3.4.2. Barcelona C3. Use of metro/tram infrastructure for recharging e-cars 66

3.5 Warsaw 70 3.6 Leipzig 70

3.6.1. Leipzig A6. (Re)charging of e-buses using tram Infrastructure 70 3.6.2. Leipzig C4. Use of tram network substations for (re)charging e-vehicles 74

3.7 Oberhausen 75 3.7.1. Oberhausen A7. Use of tram infrastructure (catenary and sub-station) for (re)charging e-buses 76 3.7.2. Oberhausen C5. Fast-charging stations for e-cars powered from the tram network 79



3.8 Gdynia 80

3.8.1. Gdynia A8. Opportunity of (re)charging of e-buses connecting Tri-city agglomeration based on trolleybus infrastructure 80 3.8.2. Gdynia A9. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles 83

3.9 Eberswalde 87 3.9.1. Eberswalde A10. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles 87

3.10 Szeged 91 3.10.1. Szeged A11. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles 91 3.10.2. Szeged C6. Multipurpose use of infrastructure for (re)charging trolley-hybrids and e-vehicles 97

3.11 Lanciano 101 3.11.1. Lanciano B3. Light (rail) tram operation for rural rail track 101

4. Remarks and inputs for the next Tasks activities 105 4.1 Impacts areas: recurring and missing fields of investigation 106 4.2 Common KPIs 110 4.3 Further Developments 119

Annexes 122

Annex 1 – List of Key Performance Indicators 123 Annex 2 – List of Context Parameters 141



List of Figures Figure 1 – Task 3.1 in the frame of WP3 9 Figure 2 – KPIs per Evaluation Categories and Impact Area 16 Figure 3 – Context Parameters per Area of Investigation and Field of application 17 Figure 4 – A selection of CPs as an example 18 Figure 5 – Evaluation scenarios for the EUCs 19 Figure 6 – The spreadsheet for the KPIs selection 21 Figure 7 – London A2 scheme 32 Figure 8 – A3 Brussels lines 42 Figure 9 – B2 Brussels lines 48 Figure 10 – B2 Brussels operational scheme for tram braking energy recovery 51 Figure 11 – A4 Barcelona test vehicles 58 Figure 12 – A4 Barcelona demonstration routes 66 Figure 13 – A8 Gdynia route 80 Figure 14 – A11 Szeged test area 92 Figure 15 – C6 Szeged test area 97 Figure 16 – B3 Lanciano railway network 101 Figure 17 – Selection of KPIs among all the EUCs 105 Figure 18 – Evaluation Category Operations: most favored impact areas 106 Figure 19 – Evaluation Category Economy: most favored impact areas 107 Figure 20 – Evaluation Category Energy: most favored impact areas 108 Figure 21 – Evaluation Category Environment: most favored impact areas 109 Figure 22 – Evaluation Category People: most favored impact areas 110 Figure 23 - Cluster 1, Common KPIs for Operations 113 Figure 24 - Cluster 3, Common KPIs for Operations 113 Figure 25 - Cluster 3, Common KPIs for Costs 116 Figure 26 - Cluster 1, Common KPIs for Energy 117 Figure 27 - Cluster 1, Common KPIs for Environment 119 List of Tables Table 1 – EUCs per Thematic Pillars 11 Table 2 – Main Evaluation Categories and Impact Areas 13 Table 3 – KPIs definition for the «Operation» Evaluation Category and «Maintenance» Impact Area as an example 15 Table 4 – A1 Bremen Context Parameters 23 Table 5 – A1 Bremen KPIs 25 Table 6 – B1 Bremen Context Parameters 26 Table 7 – B1 Bremen KPIs 28 Table 8 – C1 Bremen KPIs 31 Table 9 – A.2.1 London Context Parameters 33 Table 10 – A.2.1 London KPIs 34 Table 11 – A.2.2 London Context Parameters 35 Table 12 – A.2.2 London KPIs 37 Table 13 – C2 London Context Parameters 40 Table 14 – C2 London KPIs 41 Table 15 – A3 Brussels Context Parameters 42 Table 16 – A3 Brussels KPIs 46 Table 17 – B2 Brussels Context Parameters 49 Table 18 – B2 Brussels KPIs 52 Table 19 – A4 Barcelona Context Parameters 55 Table 20 – A4 Barcelona KPIs 59



Table 21 – A4 Barcelona test scenarios features 65 Table 22 – C3 Barcelona KPIs 67 Table 23 – A6 Leipzig Context Parameters 71 Table 24 – A6 Leipzig KPIs 72 Table 25 – C4 Leipzig Context Parameters 74 Table 26 – A7 Oberhausen Context Parameters 76 Table 27 – A7 Oberhausen KPIs 78 Table 28 – C5 Oberhausen KPIs 79 Table 29 – A8 Gdynia Context Parameters 81 Table 30 – A8 Gdynia KPIs 82 Table 31 – A9 Gdynia Context Parameters 83 Table 32 – A9 Gdynia KPIs 85 Table 33 – A10 Eberswalde Context Parameters 87 Table 34 – A11 Szeged Context Parameters 93 Table 35 – A10 Szeged KPIs 95 Table 36 – C6 Szeged Context Parameters 97 Table 37 – C6Szeged KPIs 99 Table 38 – B3 Lanciano KPIs 103 Table 39 – Common KPIs within the Evaluation Category Operations 111 Table 40 – Common KPIs within the Evaluation Category Economy 114 Table 41 – Common KPIs within the Evaluation Category Economy 116 Table 42 – Common KPIs within the Evaluation Category Environment 118 Table 43 – Common KPIs within the Evaluation Category People 119



1. Executive Summary This report deals with the activities, within the ELIPTIC evaluation process, to plan the assessment of impacts due to the outcomes of the ELIPTIC demonstrators and feasibility studies.

Chapter 1 describes the methodology adopted to assess such impacts, capitalizing on assessment methodologies applied in previous, similar EC-funded research projects and adapted to meet the specific ELIPTIC research requirements. The methodology adopted plans to carry out a “conventional full evaluation” for the ELIPTIC demonstrators and a “technological viability evaluation” for the ELIPTIC feasibility studies. The former relies on a quantitative performance comparison between the NO-ELIPTIC (before/no demonstration) and ELIPTIC (during the demonstration) scenarios, whereas the latter on a qualitative assessment of the feasibility studies starting from the NO-ELIPTIC scenario description of potentials. In both cases specific, evaluation categories were defined as well as the impact areas the performance variations due to the ELIPTIC demonstrators and feasibility studies are expected to affect. In both cases performance variations are measured by a set of Key Performance Indicators (KPIs), each associated to a specific evaluation category and impact area. To improve the accuracy of the assessment, the KPIs are integrated with a set of Context Parameters (CPs) which enables a full description of the main technical features of the context of implementation of the ELIPTIC measures. All of the above and the expected results will serve as a basis to develop the transferability study to assess the theoretical exportability of what tested within ELIPTIC across Europe. The lists of proposed KPIs and CPs are reported in Annexes 1 and 2, respectively.

Chapter 2 extensively reports the current selection of CPs and KPIs for each demonstrator and feasibility study, thus defining the preliminary list of impact areas which are likely to be affected by each ELIPTIC measure. Reported local additional KPIs, data collection schedules and test scenarios, according to the methodological directions, complete the information set needed for the preliminary assessment activities.

According to what reported in Chapter 2, Chapter 3 summarizes the main research issues which will have to be tackled in the further development of the assessment actvities. As the selection of KPIs is both a demanding task and an iterative process, and as expected after this first, inception “choice”, the need to enlarge the current set of selected KPIs and to settle those which are still uncertain are the main steps to undertake to improve the accuracy of the local overall perfomance assessment, enable a sound cross case comparison, cover less-thus-far-favored but important impact areas, and progress with the definition of the test scenarios, in sight of the next task, i.e. the assessment of the NO-ELIPTIC scenario, as remarked in Chapter 4.



2. The Methodology for the Evaluation of the ELIPTIC Use Cases

This deliverable reports the main activities performed during Task 3.1 – Impacts Evaluation Plan, within WP3 – Evaluation of the ELIPTIC Use Cases, according to what required in the Description of Work (DoW). To this aim, in this Chapter, the adopted methodology will be described, whereas the results of its application to the ELIPTIC Use Cases (EUCs) are reported in Chapter 3.

2.1 Task 3.1 - Impacts Evaluation Plan contents According to the DoW1,Task 3.1 is aimed at developing a suitable methodology to assess the main technical, social, environmental, and economic impacts of what tested in the ELIPTIC Use Cases (EUCs). Therefore, the ELIPTIC Evaluation Methodology defines both the criteria the overall assessment activities rely upon (Task 3.1) along with the steps of the evaluation process itself (Tasks 3.3 to 3.6), as synthesized in Figure 1.

Figure 1 – Task 3.1 in the frame of WP3

1 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), pp. 40-41

30 Nov-15 Version 2.2 / Drafted by UNIROMA1 | 1 0


The methodology developed in Task 3.1 is also synergic with the activities performed in Task 3.2 – Process Evaluation Plan, where a specific methodology to assess the consistency and the effectiveness of whole process (from planning to implementation, including specific operational tasks and the role of communication, information and participation) is developed, as well. The extensive scientific and grey literature in the field of evaluation processes 2 provided specific directions to develop a working method for ELIPTIC. Therefore, the assessment framework is based on sound scientific knowledge and effective procedures from past experiences achieved within research projects funded by the European Commission and adapted to address the specific ELIPTIC research issues. The ELIPTIC Evaluation Methodology in general, and more specifically the methodological criteria adopted for Task 3.1, being called to set the criteria which will steer the evaluation itself, have to address the following issues:

• individuation of the levels of assessment • definition of the three Pillars requirements to individuate the main evaluation

categories • homogenization of the EUCs • definition of the evaluation scenarios • identification of impacts • selection of Key Performance Indicators (KPIs), • data collection criteria to feed the KPIs

the most important of which are definition of the three Thematic Technological Pillars requirements, the identification of impacts and the consequent selection of the KPIs. In the following sections all of the above will be analyzed and described.

2.2 The Thematic Technological Pillars requirements and the identification of impacts The EUCs, which can be based both on actual demonstrators under real operational conditions and on feasibility studies, are divided into three main research and application areas, the Thematic Technological Pillars (Table 1), i.e.:

A - safe integration of e-buses into existing electric Public Transport (PT) infrastructure B - upgrading and/or regenerating electric PT systems C - multipurpose use of electric PT infrastructure.

2 Examples of evaluation procedures from previous EC-funded projects were studied; among these: MAESTRO (TTR et al. 2003. Monitoring Assessment and Evaluation of Transport Policy Options in Europe); METEOR (Schelling, A. et al. 2004. METEOR Final Evaluation Guidelines; NEA et al. 2003. METEOR Assessment Framework and Evaluation Guidelines for Data Collection); MIRACLES (Musso, A. et al. ed. 2004. D 4.1 Evaluation report); EBSF (Karlsson, M.A., ed., 2010. EBSF Deliverable 4.2.1 - Assessment Framework)



Demonstrator3

Tematic Pillar A

safe integration of e-buses into existing electric PT infrastructure

Tematic Pillar B

upgrading and/or regenerating electric PT systems

Tematic Pillar C

multipurpose use of electric PT infrastructure

Bremen A.1: Operation-optimized system of opportunity charging at bus depots

B.1: Recuperation of braking energy from trams: refurbishment of a flywheel energy storage system

C.1: From uniqueness to system: extension of existing multimodal mobility hub station

London A.2: Opportunity (re)charging of ebuses and/or plug-in hybrid buses (using metro infrastructure)

C.2: Use of metro sub-station for(re)charging TfL fleet vehicles (e-cars and e-vans) and zero-emission capable taxis

Brussels A.3: Progressive electrification of hybrid bus

B.2: Optimized braking energy recovery in light rail network

Barcelona A.4: Opportunity fast (re)charging and slow overnight charging of electric buses based on metro infrastructure

C.3: Use of metro/tram infrastructure for recharging e-cars (municipal fleet and private e-cars)

Warsaw A.5: Use of /tram infrastructure for recharging e-buses

Leipzig A.6: Opportunity (re)charging of e-buses (using tram infrastructure)

C.4: Use of tram network sub-station for (re)charging e-vehicles

Oberhausen A.7: Opportunity (re)charging of e-buses (tram catenaries and sub-stations)

C.5: Fast-charging stations for e-cars powered from the tram network

Gdynia A.8: Opportunity (re)charging of e-buses connecting Tri-city agglomeration based on trolleybus infrastructure

A.9: Replacing of diesel bus lines by extending trolleybus network with trolley-hybrids

Eberswalde A.10: Replacing diesel bus lines by extending trolleybus network with trolley-hybrids (incl. demo of automatic (de)wiring)

Szeged A.11: Replacing diesel bus lines by extending trolleybus network with trolley-hybrids

C.6: Multipurpose use of infrastructure for (re)charging trolley-hybrids & e-vehicles

Lanciano B.3: Light rail (tram) operation for rural rail track

Table 1 – EUCs per Thematic Pillars

3 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 7, and for further details to Del.s 2.1.1 – 12.1.1 Demonstrator Set –up Reports (in progress, due by Month 6).



Pillar A is targeted to analyze the potential of existing electric PT systems for a safe and efficient integration of electric buses (therefore the research requirement is to verify the feasibility of such integration); Pillar B is dedicated to analyze smart energy management concepts for upgrading existing electric public transport infrastructure (therefore the research requirement is to verify whether such upgrade is viable); and Pillar C is aimed at analyzing the potential of existing electric PT infrastructure to become a backbone for smart e-mobility applications (therefore the research requirement is to verify whether e-mobility applications may be compatible with current PT operations supplied by electric-powered vehicles). The evaluation process has, therefore, to ascertain:

1) Whether the performance demanded of each specific demonstrator (i.e. the requirements), within the ELIPTIC application frame, is appropriate to achieve the above-mentioned goals

2) Whether the effects of each demonstrator on the implementation environment (i.e. the impacts) are in line with the above-mentioned goals.

Although the goals might seem primarily operational, i.e. to achieve or assess the possibility to have upgraded service conditions thanks to a major exploitation of the electric options, the research requirements pave the way for a multiscope analysis. Therefore, the EUCs are clearly expected to affect many fields, as the economic side of the operations, the overall PT energy management, along with the possibilities to improve environmental and social benefits and change the perception of the stakeholders in favor or more sustainable travel options. At the same time, requirements called to verify the appropriateness of each performance to achieve any given goal due to the implementation of a given ELIPTIC demonstrator must be coherent with the areas that demonstrator will be likely to affect. Consequently the requirements of each Thematic Pillar and the expected impacts give rise to common key areas for improvement or change, i.e. the Evaluation Categories of the assessment process. Each Evaluation Category will be divided into more sub categories (or Impact Areas) to cover different fields of investigation within a common “umbrella” (for example, changed service conditions may affect both supply and demand, each corresponding to a specific impact area within the common “Operations” Evaluation Category). As said, scientific and grey literature provide clear directions in the selection of the Evaluation Categories and lessons learnt from successful projects in this field, namely those within the CIVITAS Initiative, EBSF, 3iBS and ZeEUs, suggest to focus on the following areas of interest: customer satisfaction (passengers’ perception), technical reliability (operational appropriateness), cost-effectiveness (operational affordability and feasibility) and externalities (sustainability). However, the complexity of many EUCs demonstrators (very different performance to assess) and the multiplicity of achievable goals called for a larger number of very detailed Impacts Areas, which resulted into the fields of interest reported in Table 2. Any Evaluation Category or Impact Area may serve to assess performance from more demonstrators of all the three Thematic Pillars.



Evaluation Category Impact Area Performance to investigate due to implementation of the EUCs demonstrators

Operations

Staff Changes in the amount of personnel

Supply Changes in the operating fleet

Maintenance Changes in the workload due to maintenance operations

Service Changes in the service workload

Safety Changes in the amount of risky events

Consistency Compatibility of the electric option with the current operations

Demand Changes in the amount of passengers

Economy

Costs Changes in the amount of expenditures

Revenues Changes in the amount of incomes

Incentives Amount of subsidy granted

Energy Consumption Quantity of energy/fuel used to operate

Supply Need of energy/fuel to operate

Environment

Concentrations Changes in the amount of pollutants in the air

Noise Changes in the noise emission/perception

Other nuisance Changes in the vibrations emission/perception

Emissions Changes in the amount of pollutants emitted by the fleet

Waste Changes in the amount of waste matter/products

People Passengers Changes in the passengers’ overall perception

Drivers Changes in the drivers’ overall perception

Table 2 – Main Evaluation Categories and Impact Areas

2.2.1 Impact levels The EUCs demonstrators, although assessed by a common set of Impact Areas, can be dissimilar in the extent of the related impacts, due to the level of changes the very demonstration activities will determine. For what concerns EUCs with demonstrators under real operational conditions, it is expected to have direct impacts in all the areas where performance changes will occur, although it is more likely that this will be more tangible for Impact Areas associated to “Operations”, “Energy” and “Economy” Evaluation Categories, especially as long as the ELIPTIC testing activities will take place and in the very near future (short-term impacts). For what concerns EUCs with feasibility studies, no



direct changes area expected, being no real operations involved. In this case impacts will be qualitatively evaluated, and will simulate possible short/long time effects. The extent of the impacts will be also affected by the duration of the test activities, and the size of the resources (fleets, vehicles, range of operations, etc.) involved; reduced testing periods and/or small amount of involved resources produce performance changes with no long-term/large scale impacts and difficult to assess in general, beyond the ELIPTIC timeframe. It is also important to stress that, no matter the demonstration program, impacts on “People”, especially in the “Passenger” Area, and “Environment”, especially to assess “Concentrations”, can be assessed “from real” only after very long-time periods of implementation. Expected impacts for each EUC, according to the contributions provided, will be described in Chapter 3.

2.3 The selection of the Key Performance Indicators After the definition of the Thematic Pillar requirements and the expected range of impacts, the next research question has to address the possibility to measure the performance changes occurring when implementing the EUCs demonstrators. In other words, the research question could be: “how did the demonstrator performance affect (change) that given impact area?”, and the answer is provided by an independent measurement of the change by appropriate indicators, or KPIs. The KPIs selection for the EUCs was based on a very large palette of indicators, according to the lesson learnt in previous assessment process, so to:

• Allow each EUC to have an appropriate choice • Cover all the issues (Impact Areas) related to the different Evaluation

Categories and the three Technological pillars • Allow a sound assessment of the changes due by the implementation of each

demonstrator • Prepare an appropriate amount to KPIs for further cross-case analyses.

Efforts were made to provide unambiguous and comprehensible KPIs, to avoid prospective difficulties in the provision of the feeding data. Moreover, to avoid uncertainties in the interpretation of the results, each KPI was assigned to only one Evaluation Category and Impact Area. As a result, each KPI is identified by:

• an alphanumeric code (first uppercase letter individuates the Evaluation Category, second lowercase letters individuate the Impact Area, number indicates the progression; for example: Osu3 is the third KPI among those listed for “Supply” Impact Area within the “Operations” Evaluation Category.

• a name • a definition (to explain its core and target) • a specific collection method or sources for gleaning the feeding data • the units of measurement • a reference period (the data collection and the units of measurement are

related to).

http://www.thefreedictionary.com/unambiguous



Definitions, collection methods and units of measurements were determined according to the scientific and grey literature in this field4, and adapted to provide a comprehensive description of the EUCs demonstrators’ performance. Table 3 reports an example for the KPIs definition for the “Maintenance” Impact Area within the “Operation” Evaluation Category. Evaluation Category: Operations

Impact Area KPI# KPI name KPI definition

Collection method/sources

Unit of measurement

Reference period

Mai

nten

ance

Oma1 Vehicles failures

Monthly events recorded per vehicle and per travelled km

The events recorded for the vehicle divided by the km traveled by the vehicle in a month

events/traveled km

month (possibly year to improve accuracy)

Oma2

Days in workshop (or MTTR)

Average time required to repair a vehicle due to failed component or device in workshop (to be specified per component)

The corrective maintenance time at workshops divided by the total number of corrective maintenance actions in a month h/action


Oma3

Maintenance of the bus components (or MTBF)

Recorded time between two failures for a repairable component

Sum of the operational periods divided by the number of observed failures

days/failure per component


Oma4

Technical maintenance of the bus

Recorded time between consecutive failures of a vehicle in operation

Arithmetic mean time between recorded failures of a vehicle days


Oma5

Failures of non-repairable components (or MTTF)

Recorded time between for a non-repairable component to fail

Sum of the operational periods prior the failure days


Oma6 Durability of components

Lifetime of a given mechanical component

Expected lifetime according to manufacturer specifications/actual operational lifetime years

Eliptic demo timeframe

Table 3 – KPIs definition for the «Operations» Evaluation Category and «Maintenance» Impact Area as an example The complete list of the ELIPTIC 101 KPIs is reported in Annex 1. 4 Along with the literature mentioned in footnote 2, more references came from the selection of KPIs of the EBSF, 3iBS and ZeEUs projects, OECD indicators database, and the most advanced scientific literature in this field.



As said, the ELIPTIC selection of KPIs covers all the Impact Areas related to the different Evaluation Categories and the three Technological pillars according to a variable number of indicators (Figure 2). This is due to the need to cover in an appropriate way all the specificities of the demonstrators. The bulkiest categories are “Economy” (33 KPIs) and “Operations” (30 KPIs), also in light of the consideration that the results of the application of the demonstrators will be assessed in terms of cost-effectiveness, through a Cost-Benefit Analysis (CBA)5; moreover, this is also consistent with one of the targets of WP4, which is to support the EUCs in transforming the demonstrators into actual Business Cases and derive schemes for development programs.

Figure 2 – KPIs per Evaluation Categories and Impact Area

5 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 42, description of Task 3.4.

0 5 10 15 20 25 30

Staff

Supply

Maintenance

Service

Safety

Consistency

Demand

Costs

Revenues

Incentives

Consumption

Supply

Concentrations

Noise

Other nuisance

Emissions

Waste

Passengers

Drivers

Ope

ratio

nsEc

onom

yEn

ergy

Envi

ronm

ent

Peop

le

6

5

6

10

2

1

1

30

3

2

10

6

3

1

2

4

2

5

2



2.3.1 Additional indicators: the Context Parameters To improve the global knowledge on the test environment, a list of 62 Context Parameters (CPs) integrated the KPIs palette. CPs reported in Annex 2 are aimed at describing the context of application of the ELIPTIC demonstrators by simple indicators divided into three main areas of investigation (operation, sustainability and context) and six fields of application (Figure 3) to report the general PT operational features. Therefore, CPs differ from KPIs as they are not targeted to describe specific performance of a given ELIPTIC demonstrator, but the general performance of regular PT operations prior the ELIPTIC application.

Figure 3 – Context Parameters per Area of Investigation and Field of application

The synergy between CPs and KPIs provides:

- more detailed reference backgrounds for both the ELIPTIC actual demonstrators and the feasibility studies

- improved accuracy in the interpretation of data - improved consistency with the local KPIs selection.

Similarly to the KPIs, CPs are defined according to an ID number, a name, the required unit of measurement and the reference term. Figure 4 reports an example of a selection of CPs. Although exhaustive, both the KPIs and the CPs selections left room for improvement. This is the reason why, for both sets of indicators, EUCs could add more assessment parameters within a specific slot (named “Other”), to describe specificities pertaining to local PT operations. This is particularly relevant for Pillars B and C measures, being these very innovative and therefore less studied, in general, than those from Pillar A.

20

6

10

20

3 3

0

5

10

15

20

25

vehicles charge kinematicsand dynamics

power andenergy

route environment

operations sustainability contextarea of investigation and fields of application



Figure 4 – A selection of CPs as an example

2.3.2 Homogenization of the EUCs The importance of having a large number of both common KPIs and CPs, univocally defined, to steer the selection towards an appropriate choice of indicators for each EUC relies on more consideration, the first of which is that the contexts of application (area size, amount of vehicles involved, etc.) and the ELIPTIC measures (demonstrators vs feasibility studies) are very different. Demonstrations activities at each EUC will be run locally, and related outputs if not codified via common indicators will be difficult to compare cross-case. Moreover, although different, test outputs have to be considered within the common Thematic Pillar the demonstrators were grouped. This means that results have to be harmonized according to the associated Pillar requirements which, as already stated in section 2.2, pave the way for a multiscope analysis. This can be done only through a large amount of indicators to cover the different areas of analysis. Last but not least, large amount of indicators help to overcome typical issues of the evaluation processes, such as test errors which fail to provide some of the required data or make results partly inaccurate (by compensating with others provided by other performance), mono-focused

Area of investigation and application field Parameter ID Parameter

Units of measurement

Parameter reference

Operations1 Fleet composition Unit2 Operational vehicles Unit3 passenger capacity sum of standing vehicle4 Total range km vehicle5 Battery-only range km vehicle6 Diesel-only range km vehicle7 Vehicles operational h day……..21 Total daily time to h vehicle22 Daily time to recharge h vehicle…………………27 Allowed max speed km/h route28 Maximum starting m/s2 vehicle29 Mean acceleration 0 - m/s2 vehicle30 Mean acceleration 0 - m/s2 vehicle………………

Sustainability37 Diesel engine power kW vehicle38 HVAC power kW vehicle39 Other auxiliaries kW vehicle40 Total energy stored in kWh vehicle…………..

Context57 Route description58 Elevation diagram route59 Lenght km route60 Bus stops # route61 Ambient temperature °C daily average 62 Road conditions narrative

Environment

Vehicles

Charge

Kinematics and dynamics

Power and energy

Route

narrative



assessment, unexpected results. Therefore the approach used for ELIPTIC will be to assess results to verify whether

• they can affect more areas of investigation/evaluation categories/impact areas;

• they are in line with Thematic Pillars requirements • they are consistent with the context of application

Moreover, the consistency assessment leads to perform the cross-case analysis according to common terms of comparisons, which will be:

• Size of the EUC • Size of the resources involved in the ELIPTIC activities • Kind of tested measures (demonstrators vs feasibility study)

so to homogenize, as much as possible, results among common groups of EUCs.

2.4 Definition of the evaluation scenarios The ELIPTIC measures are divided into two main groups: demonstrators and feasibility studies. The former will be evaluated by a typical quantitative comparison (Conventional Full Evaluation) and the latter through a qualitative assessment of potential impacts (Technological Viability Evaluation) as in Figure 5.

Figure 5 – Evaluation scenarios for the EUCs

2.4.1 Conventional Full Evaluation The Conventional Full Evaluation will be based on the comparison of performance according to two different situations: the NO-ELIPTIC scenario and the ELIPTIC scenario. The NO-ELIPTIC scenario describes the situation without the implementation of the ELIPTIC measure(s) and acts as a reference scenario (or baseline), whereas the ELIPTIC scenario describes the outcomes during the implementation of the ELIPTIC measure(s). Two options are available to create the NO-ELIPTIC scenario, i.e. to describe the situation without the implementation of the ELIPTIC measure:

• Option “before”: a reference or baseline according to data collected prior the implementation of the ELIPTIC measures

• Option “(during) control”: a reference or baseline according to data collected during the implementation of the ELIPTIC measures on



vehicles/fleet/components/infrastructure with no ELIPTIC equipment, then acting as control terms. The option is typically applied when no statistical data are available or, if available, unsuitable to compare.

The ELIPTIC scenario is created according to the performance observed on vehicles/fleet/components/infrastructure with the ELIPTIC equipment, then acting as the test terms. The Conventional Full Evaluation can then be based on the comparison of a set of two “samples”:

• test vs before • test vs control • test vs control and before.

Values of the KPIs describing either performance occurred before ELIPTIC or during ELIPTIC along a control term (or both terms) will be compared to values of the same KPIs describing the performance during the ELIPTIC testing terms. The Conventional Full Evaluation will be based on such quantitative comparison and a CBA, as planned in Tasks 3.3 (which will lead to the creation of the reference scenario for each EUC) and 3.4 (which will lead to the performance comparison and the CBA application).

2.4.2 Technological Viability Evaluation The Technological Viability Evaluation being applied to technological concepts and/or feasibility studies (thus with no real demonstration) will rely on a comparison between a NO ELIPTIC scenario (either based on “before” or “control” options, or both) and a ELIPTIC scenario based on expert assessment on the applicability or viability of the ELIPTIC concept in a given EUC. Values of the KPIs describing either performance occurred before ELIPTIC or during ELIPTIC along a control term (or both terms) will be used to create the reference scenario and to provide a knowledge base for the further, qualitative expert assessment, which will be based on a SWOT analysis (as planned in Task 3.5). Elements of Strength and Weakness, Opportunity and Threats for the SWOT Matrix will be derived from the assessment of the reference situation and will be defined according to the selected impact areas.

2.5 Data collection criteria As above reported, KPIs need common criteria to collect data they must be “fed” by, i.e.:

• univocal units of measurement, • coherent and sound collection timing and test size, • data delivery schedules.

To this aim KPIs were provided with specific units of measurement, along with proposed data collection method or sources. This was mainly meant to avoid uncertainties in the interpretation of the KPIs and in the way to measure the performance each KPI is associated with.



It is also important that data are collected on a “sample” (being this a “before”, or a “control” or a “test” term) relevant and for a period long enough to describe the performance in a sound way. Therefore to ensure relevance and soundness for each KPI it is necessary to have appropriate data collection periods for both the NO ELIPTIC and the ELIPTIC scenarios and have a description of the “sample” involved in both scenarios. To comply with that EUCs were provided with a template based on a spreadsheet (Figure 6) with the complete list of KPIs and CPs, including codes, names, definitions, units of measurement, data collection periods for both the NO ELIPTIC and the ELIPTIC scenarios, the sample (control, before or test) the data are referred to. EUCs were requested to select the most appropriate KPIs and CPs (or add new ones) to describe the local ELIPTIC demonstrators or feasibility studies just by ticking on the selected box, and to report the availability of data for each scenario and the related collection periods. Results of the selection of each EUC will be reported in Chapter 3.

Figure 6 – The spreadsheet for the KPIs selection

The next step in the data handling concerns the provision of data for both scenarios. To this aim EUCs will be provided with a reporting data spreadsheet, which will include:

• The selected KPIs, each with a box to fill in with the requested value(s) (no disaggregated data)

• To confirm the data period the KPIs value is collected • A box to fill in with information on the test size • A box to fill in for explanatory notes in case of data coming from simulations • A box to fill in with the person appointed to collect data.

Should an EUC need to adjust the units of measurement or to collect data in a different way to what required, specific directions will be provided in order to have outcomes comparable with those from the other EUCs.



3. The Preliminary Selection of KPIs for the ELIPTIC Use Cases

This chapter describes the preliminary results of the selection of KPIs and CPs by each EUC, as presented at the ELIPTIC Partner Meeting held in Leipzig, on November 10-12, 2015. The selection of KPIs is a demanding task but, above all, is an iterative process and this preliminary selection can be considered just its starting point. It is expected that, for each EUC, variations may occur due to possible changes in the demonstration activities or adaptation to new circumstances in the feasibility studies; at the same time, variations may be also required to overcome typical problems arising after the initial selection of performance indicators such as the need to increase the amount of parameters to consider for cross-site comparisons and transferability analyses; the dominance of KPIs from some evaluation categories or impact area vs the poor selection from others; the lack of actual/suitable data to create the reference scenarios. The selection of KPIs for each EUC is thus reported as follows.

3.1 Bremen The Bremen use case includes tests in all the three thematic Pillars (Table 1) and more specifically:

• A1. Operation-optimized system of opportunity charging at bus depots • B1. Recuperation of braking energy from trams: refurbishment of a flywheel

energy storage system • C.1: From uniqueness to system: extension of existing multimodal mobility hub

station In all the three cases activities rely on the development of feasibility studies, to which actual applications may follow but, according to the DoW, only C1 can be considered a “pure” feasibility study.6 The overall selection of indicators reflects such plans as evidenced by the tables reported and commented in the next sections.

3.1.1. Bremen A1. Operation-optimized system of opportunity charging at bus depots

The CPs list (Table 4) itemizes 20 indicators, mostly targeted to describe the operational context of application of the test and coherent with the gist of the A1 demonstrator.7

6 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 7, Table 2 7 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 26 Task 2.1 for more detail on the A1 demonstrator



Bremen A.1

Area of investigation and application field

Parameter ID Parameter Units of

measurement Parameter reference

Availability No - Eliptic

scenario

Availability Eliptic

scenario Comments

Operations

Vehicles

1 Fleet composition Unit X X

2 Operational vehicles Unit X X

3 passenger capacity (vehicle)

sum of standing and seating places (standing place = 5 pax/sqm)

vehicle X X

7 Vehicles operational time h day X X Only in test

phase

8 Distance driven (route ) km day X X

9 Distance driven (total) km year X X

10 Distance driven (from depot to route) km day X X

11 Distance driven (from route to depot) km day X X

14 Commercial speed (route) km/h day X X

15 Empty mass kg vehicle X X Provided when e-buses in operation

16 Vehicle mass (only seated pax) kg vehicle X X

20 Passenger mass kg (estimated 70 kg per pax) vehicle X X

Kinematics and dynamics 27 Allowed max speed km/h route X X

Sustainability

Power and energy 38 HVAC power kW vehicle X X Provided

when e-buses in operation

39 Other auxiliaries kW vehicle X X

Context

Route 57 Route description narrative X X

59 Length km route X X

Environment

60 Bus stops # route X X

61 Ambient temperature °C daily

average X X Not daily

62 Road conditions narrative X X Table 4 – A1 Bremen Context Parameters



Data to feed the CPs will be collected to describe the NO ELIPTIC and the ELIPTIC scenarios, thus leading to assess a full performance comparison. Same approach leads the selection of KPIs (Table 5), where 12 out of the 18 KPIs belong to the “Operations” Evaluation Category and especially to assess how performance variations will impact on service. Emphasis is also placed on assessing passengers and drivers’ overall perception of the demonstrator and among such KPIs, the selection of Ppa5 – Noise perception is of the utmost importance. It is planned to assess perception via questionnaires. None of the KPIs reported in Table 5 is expected to raise confidentiality issues. According to the current selection of KPIs, the need to assess the operational feasibility of the A1 demonstrator is a priority, but to improve the quality of the assessment, more KPIs are recommended and especially those to assess the A1 impacts under the economic point of view and in terms of energy consumption. This is also in line with what reported in the DoW, where it is stated that: “During the use case trial, a scientific monitoring of energy consumption, noise control, and the economic value of the operating e-buses (12m/18m) will be carried out to evaluate potential impacts of operating new technology more widely8”. Moreover, a larger number of KPIs will help the cross site comparison with similar demonstrators within Thematic Pillar A. Data collections will take place according to two sessions: from October 2015 to April 2016 and from May 2017 to May 2018; the former to describe the situation prior to ELIPTIC and the latter the application of the ELIPTIC demonstrator. Such time plan is common to all KPIs and is adequate to provide a sound amount of data, but May 2018 as deadline is beyond the time planned for the evaluation activities (Tasks 3.4, 3.5 and 3.6 are all due by Month 34, April 2018) which leaves 9 to 10 months available for the actual data collection for the ELIPTIC scenario. Also in this case the time plan is appropriate, but it is recommended to anticipate the provision of results for the data collection, in order to meet the requirements and the deadlines associated to the evaluation activities. This may specifically apply to the submission of questionnaire for the KPIs of the “People” Evaluation Category.

8 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 26 Task 2.1



Bremen A1

Evaluation Category Impact area KPI # KPI Name

NO ELIPTIC scenario ELIPTIC scenario

Availability of KPI/data from

control line/vehicle/

fleet during/before

the demonstration

Collection Availability of KPI/data from


fleet during the

demonstration

Collection

Start End

Frequency (D=daily,

W=weekly, M=monthly; O=one-off; Ot=Other)

Start End

Frequency (D=daily,

W=weekly, M=monthly O=one-off; Ot=Other)

Ope

ratio

ns

Staff Ost1 Driving staff X Oct'15 Apr'16 O X May'17 May'18 O

Supply Osu1 Passenger capacity (line) X Oct'15 Apr'16 O X May'17 May'18 O

Osu3 Daily supply X Oct'15 Apr'16 O X May'17 May'18 O

Maintenance Oma7 Durability of charging infrastructure X Oct'15 Apr'16 O X May'17 May'18 O

Service

Ose1 Commercial speed X Oct'15 Apr'16 O X May'17 May'18 O Ose2 Bus frequency X Oct'15 Apr'16 O X May'17 May'18 O

Ose3 Dwell time X Oct'15 Apr'16 O X May'17 May'18 O

Ose4 Bus Punctuality X Oct'15 Apr'16 O X May'17 May'18 O

Ose6 Journey time X Oct'15 Apr'16 O X May'17 May'18 O

Ose7 Round trip time X Oct'15 Apr'16 O X May'17 May'18 O

Ose8 Operation time X Oct'15 Apr'16 O X May'17 May'18 O

Demand Ode1 Passenger demand X Oct'15 Apr'16 O X May'17 May'18 O

Peop

le

Passengers

Ppa1 Awareness X Oct'15 Apr'16 O X May'17 May'18 O

Ppa2 Acceptance X Oct'15 Apr'16 O X May'17 May'18 O

Ppa3 Attractiveness X Oct'15 Apr'16 O X May'17 May'18 O

Ppa4 Travel comfort X Oct'15 Apr'16 O X May'17 May'18 O Ppa5 Noise perception X Oct'15 Apr'16 O X May'17 May'18 O

Drivers Pdr1 Driving comfort X Oct'15 Apr'16 O X May'17 May'18 O

Table 5 – A1 Bremen KPIs



3.1.2. Bremen B1. Recuperation of braking energy from trams: refurbishment of a flywheel energy storage system

The selection of CPs for Bremen B1 (Table 6) is aimed at describing the operational context, especially in terms of kinematics and dynamics features.

Bremen B.1


Parameter ID Parameter Units of measurement

Parameter reference


scenario


scenario Comments

Operations

Vehicles 7

Vehicles operational time

h day X

14 Commercial speed (route) km/h day X

Charge

24

State of charge of the battery at the end of operations

% vehicle X

25 Charging operations

events/ operational time

vehicle X


27 Allowed max speed km/h route X

30 Mean acceleration 0 - max speed

m/s2 vehicle X

32 Mean braking acceleration max speed - 0

m/s2 vehicle X

34 Acceleration space 0 - max speed

m vehicle X

36 Acceleration space max speed - 0

m vehicle X

Sustainability

Power and energy 48

Total electric energy supplied from externals sources (catenary, public energy network, etc.)

kWh 1 h X

Context

Route 57 Route

description narrative X

59 Length km route X

Environment

60 Bus stops # route X

61 Ambient temperature °C daily average X

62 Road conditions narrative X

Table 6 – B1 Bremen Context Parameters



As B1 originates from the need to (re)operate a flywheel energy storage system9 the selection is aimed at describing the existing system, coherently with what reported in the DoW. More specifically, the use case objective is in a first phase (December 2015 – December 2016) to investigate the reactivation of the system and based on the results to carry out in a second phase a simulation including the data collection. Similarly the selection of the KPIs (Table 7) reflects the need to outline possible impacts on local operations, cost structure, energy consumption and CO and NOx emissions levels. To improve the impacts assessment, five additional specific KPIs are available, i.e.:

• OBre1 - Operation Availability (%), as the availability of flywheel device, reported as the amount of days in which the flywheel operates in a year

• OBre2 - Standby energy loss (W), as the amount of hourly standby losses

• OBre3 - Power density (kW per sqm), as the rate of energy flow (power) per unit area (or volume/mass).

• OBre4 - Energy density (Wh/kg), as the amount of energy stored in the system

• OBre5 - Recharge time (sec), as the time to recharge the flywheel (which replicates/adapts KPI Ose 10)

All will be “fed” by data collected during direct measurements or analysis, which switch the focus on the collection period. This lasts one year (from January to December 2017) and will cover the collection of all the data, which will be on an one-off basis. This will require high accuracy, being no opportunity to replicate. Being B1 more a feasibility study rather than an actual demonstrator, no actual before-vs-during comparison is possible and the KPIs gleaned will be used to describe how to progress with the operations. If the one-off option and the type of KPIs selected thus far are considered, more indicators associated to the “Operations” and the “Economy” Evaluation Categories seem useful to improve the overall assessment, especially in light of the need to have cross-case comparisons. To this aim, additional KPIs to expand the assessment of impacts on operations are recommended, and especially to gauge the relevance of charging operations, along with others from the “Costs” impact area, to corroborate B1 economic viability10.

9 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), pp. 26 - 27 Task 2.1 for more detail on the B1 demonstrator 10 For example: Ost4 - Staff for recharging/refuelling operations, Oma7 - Durability of charging infrastructure, Eco22 - Recharging infrastructure, and possibly Esu5 - Recharging capacity (all adapted to the flywheel energy storage system)



BBBBB Bremen B.1



Availability of KPI/data from control line/vehicle/

fleet before/during the demonstration

Collection Availability of

KPI/data from the demo line/vehicle/

fleet during the demonstration

Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Service

Ose1 Commercial speed X Jan'17 Dec'17 O

Ose2 Bus frequency X Jan'17 Dec'17 O

Ose3 Dwell time X Jan'17 Dec'17 O

Ose6 Journey time X Jan'17 Dec'17 O

Ose7 Round trip time X Jan'17 Dec'17 O

Ose8 Operation time X Jan'17 Dec'17 O

Eco

nom

y

Costs

Eco1 Operating cost (general) X Jan'17 Dec'17 O

Eco2 Investment for the network X Jan'17 Dec'17 O

Eco4 Maintenance operational costs X Jan'17 Dec'17 O

Eco23 Electricity costs for vehicles X Jan'17 Dec'17 O

Revenues Ere1 Economic surplus X Jan'17 Dec'17 O

Ene

rgy

Consumption Ecn 9 Electricity consumption

X Jan'17 Dec'17 O

Table 7 – B1 Bremen KPIs



Table 7 – B1 Bremen KPIs (cont.)

BBBBB Bremen B.1



Availability of KPI/data from control line/vehicle/

fleet before/during the demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Env

ironm

ent

Emissions

Eem2 CO average emission X Jan'17 Dec'17 O

Eem3 NOx average emission

X Jan'17 Dec'17 O

O

ther

OBre1 Operation Availability X Jan'17 Dec'17 O

OBre2 Standby energy loss X Jan'17 Dec'17 O

OBre3 Power density X Jan'17 Dec'17 O

OBre4 Energy density X Jan'17 Dec'17 O

OBre5 Recharge time X Jan'17 Dec'17 O



3.1.3. Bremen C1. From uniqueness to system: extension of existing multimodal mobility hub station

This feasibility study is targeted to identify appropriate locations for the facilities (or hub sites) included in the local multimodal supply. The proposed selection of KPIs (all specific for C1) seems therefore targeted to assess potential sites for the facilities development through a site analysis (Table 8) in terms of land use. To this aim, the following KPIs were proposed by the C1 local demo leader:

• OBre6 - Integration with surroundings, as the conformity with existing surroundings (minimal land use conflicts incl. noise, air quality, other disruptions)

• OBre7 - Transfer connectivity, as the quality of connections at the mobility hub site to allow passengers easily transfer between different modes of transport

• OBre8 - Pedestrian accessibility, as the easiness to reach the mobility hub site • OBre9 - Bike accessibility, as the easiness to reach the mobility hub site by

bike • OBre10 - Implementation time, as the time to develop and implement the

mobility hub site • OBre11 - Transit ridership, as the potential number of transit passengers • OBre12 – Attractiveness, as the quality of the mobility hub site, nearby

buildings, landscaping, area livability (environmental and social quality in that area), community cohesion (quantity/ quality of positive interactions among people in that area)

For all it is planned to collect information and or data by specific investigations or analyses and to score the performance each KPI is associated to. The current selection addresses partly the research question about “where to place such stations” 11 and raises uncertainties in the soundness of the measurements methods, data collection proposed. For what concerns OBre6, this indicator actually includes more specific KPIs, conventionally used to assess disruptions such as space consumption (ratio between the facility overall impervious surface and the surface covered by the building/plant ), noise perception (already included in KPI Eno1), and air quality (already included in KPIs Eco 1, Eco2 and Eco3), all of which provide quantitative outputs. If the goal is to assess the Integration with surroundings qualitatively, then it is suggested to use and adapt KPIs PPa 1, PPa2 and PP3. The latter is also fully adaptable to cover the issues included within OBre 12. Impacts associated to OBre8 and OBre9 can be either assessed qualitatively (for instance through walkability lists) or quantitatively through common indicators measuring the density of links and nodes, the pedestrian catchment area of the

11 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 27 Task 2.1 for more details on the C1 demonstrator



facility or its road connectivity, which can be used also for OBre 7. Such quantitative indicators are all very simple and can be desk calculated. OBre10 is not a real indicator, but it can be assumed as a specific context parameter for C1. Eventually, OBre 11 represents the potential demand which can be modeled or qualitatively assessed if data concerning Ode1 - Passenger demand for a similar facility are available. Alternatively, a quantitative assessment of the potential demand (although rather general) can be derived by considering census data. It is strongly recommended a revision of such proposed KPIs to develop the impact assessment for C1 either qualitatively (i.e. by using KPIs PPa 1, PPa2 and PP3 and adapting the questionnaires accordingly) or quantitatively by clarifying survey or analysis methods according to the directions provided. More KPIs are also needed to compensate the lack of CPs. It is also recommended to increase the number of KPIs by including parameters useful to assess the economic side of the feasibility, so to make possible a cross case comparison with the other Pillar C demonstrators. A larger number of (quantitative) KPIs is also consistent with the test scenarios selected by the C.1 demo leader, i. e. the possibility to assess the before vs during impact variations.

BBBBB Bremen C.1


Evaluation Category

Impact area KPI #


control line/vehicle/ fleet before

the demonstration


the demo line/vehicle/


Collection

Start End

Frequency D=daily,

W=weekly, M=monthly O=one-off; Ot=Other

Start End

Frequency D=daily,


Oth

er

OBre6 Integration with surroundings X X

OBre7 Transfer connectivity X X

OBre8 Pedestrian accessibility X X

OBre9 Bike accessibility X X

OBre10 Implementation time X X

OBre11 Transit ridership X X

OBre12 Attractiveness X X Table 8 – C1 Bremen KPIs



3.2 London The London use case is based on two tests, respectively in thematic Pillars A and C (Table 1) and more specifically:

• A2. Opportunity (re)charging of ebuses and/or plug-in hybrid buses (using metro infrastructure)

• C.2: Use of metro sub-station for(re)charging TfL fleet vehicles (e-cars and e-vans) and zero-emission capable taxis

Although, according to the DoW, A2 is reported as a demonstrator 12, this is actually divided into a feasibility study and the actual demonstrator, as described in section 3.2.1. C.2 is a pure feasibility study.

3.2.1. London A2. Opportunity (re)charging of ebuses and/or plug-in hybrid buses (using metro infrastructure)

London A2 deals with the use of London Underground (LU) High Voltage electricity network to charge electric buses; as above mentioned A2 is then divided into an initial feasibility study (A.2.1) and an operational demonstration (A.2.2). A.2.1 originates from the need to have a preliminary study of the LU energy system and local bus networks, identifying interchanges suitable for possible sharing of LU’s high-voltage electrical power network to recharge electric buses (Figure 7) .

Figure 7 – London A2 scheme

This will require a detailed analysis of the demand patterns and available capacity at these nodes, together with an assessment of the suitability of bus route conversion to plug-in vehicles. More specifically, TfL operates 10 electric buses which are foreseen to potentially be part of ELIPTIC. Those recharged overnight would complement LU’s typical energy demand patterns. Opportunity charging implies a more sustained demand for power throughout the day. These different cases will form part of the

12 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 7, Table 2 and p. 28 Task 2.2 for more details on the A2 demonstrator



study. Therefore the selection of CPs for A.2.1 (Table 9) focuses on the charge and energy domains coherently with such test operational requirements

London A.2.1





scenario


scenario Comments

Operations

Charge 25 Charging

operations


vehicle X X

is a version of this adjusted to the feasibility study

26 Full charge kg vehicle X X Sustainability

Power and energy

46

Ratio between energy supplied and energy charged

# vehicle X X

To some extent outside the control of the project; it depends on the vehicles the operators decide to buy but we may be able to isolate the infrastructure side of the balance.

48

Total electric energy supplied from external sources (catenary, public energy network, etc.)

kWh 1 h X X Will be a modelled rather than actual delivery

Context

Route

57 Route description narrative X X

58 Elevation diagram route X X

59 Length km route X X Environment 60 Bus stops # route X X

Table 9 – A.2.1 London Context Parameters

The subsequent selection of KPIs for London A.2.1 (Table 10) relies on specific indicators only, i.e.:

• OLon1 - Technical Viability, as the number of locations identified where suitable sites (for example bus garages) are in reasonable proximity of a point to connect to the LU power network to allow a connection to be made. This will be obtained by GIS mapping followed by a validation exercise with key stakeholders in the LU power and bus teams



• OLon2 - Financial Viability, as the number of sites where the use of the LU network as the power source has been shown to be cost effective. The measure is a simple count of the number of sites where connecting bus charging infrastructure to the LU power network is financially viable.

• OLon3 - Demand Mapping, to understand the demand for charging infrastructure across the bus network. For the measurement the feasibility study will consider the demand for charging infrastructure from the bus network. This measure will be the percentage of routes studied.

• OLon4 - Charging capacity, i.e. the charging capacity available measured in A/day. This is a quantification of the electricity available to support charging at each viable site. As the ability of the LU network to supply electricity will vary throughout the day this measure will be the daily aggregate.

• OLon5 - Coverage gaps, meant as the locations on the bus network where provision of charging infrastructure is either not technically possible or not financially viable.

• OLon6 - Demonstration site identified, i.e. a location, such as a bus garage identified where there is demand for charging infrastructure, sufficient space for the installation and operation of the charging equipment and a viable connection possibility to the LU power network exists.

London A.2.1

Evaluation Category

Impact area

KPI #

KPI Name

No ELIPTIC scenario ELIPTIC scenario



fleet before or during the

demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Oth

er

OLon1 Technical Viability X

OLon2 Financial Viability

X

OLon3 Demand Mapping

X

OLon4 Charging capacity

X

OLon5 Coverage gaps X

OLon6 Demonstration site identified

X

Table 10 – A.2.1 London KPIs

Aside from OLon 3 and 4 which provide quantitative parameters, the other indicators are qualitative outcomes of this desk-based study, which will use spatial data sources for the LU power and bus networks. The results will be both narrative and mapping / GIS-based representation of possible co-locations of e-bus charging infrastructure, and more specifically OLon 5 and 6 will represent the final outcomes (yes or not) of the whole feasibility assessment for such locations.



No actual test scenarios can be considered for A.2.1 but all the KPIs will be calculated during the first period of ELIPTIC. Depending on the initial feasibility study, TfL plans to undertake a demonstration in the second period of ELIPTIC. A pilot electrical connection would be made from the LU power network to a co-located bus station or depot where energy demand from e-buses will be monitored. The demonstrator will involve the physical energy monitoring and impact assessment of demand and whether supply can be mediated with acceptable impact on the LU network (power draw, power quality) and service (available power for the Tube). The selection of CPs for A.2.2 (Table 11) includes some parameters already used for A.2.1 (25, 46, 48, 57, 58, 59 and 60) along with others; for two of them (21 and 46) uncertainties are reported in the comment box of Table 11.

London A.2.2





scenario

Availability Eliptic scenario

Comments

Operations

Charge

21 Total daily time to recharge h vehicle X X

maybe useful but as it refer to the time it takes an individual vehicle to recharge only partially indicative of the performance of the demonstration project. Our projects are about charging infrastructure, individual vehicle charge times will be down to the specification of the vehicle connecting to our system

22 Daily time to recharge (route, fast chargers)

h vehicle X X

23 Daily time to recharge (depot) X X

24 State of charge of the battery at the end of operations

% vehicle X X



vehicle X X

Table 11 – A.2.2 London Context Parameters



Table 11 – A.2.2 London Context Parameters (cont.)

London A.2.2





scenario


Comments

Sustainability

Power and energy

45 Energy flash charging (10 seconds)

kWh vehicle X X

46 Ratio between energy supplied and energy charged

# vehicle X X


48


kWh 1 h X X

Context

Route

57 Route description narrative X X 58 Elevation diagram route X X 59 Length km route X X

Environment 60 Bus stops # route X X

The selection of KPIs for A.2.2 enables to assess a good mix of performance and assess impacts in terms of operations, energy, economy, and environment. KPIs associated to the “Energy” and “Operations” Evaluation Categories will be aimed at validating the demonstrator main goal, i.e. to utilize the existing TfL energy infrastructure to charge e-buses and expand the operations of e-buses in London. Environmental KPIs will be used to validate the objective to improve air quality and reduce CO2 emissions, but it will be of the outmost importance the assessment of EM radiations (Eot2), being this nuisance seldom monitored during transportation research projects. The economy impact will be assessed in terms of the investment for the network (Eco2), although the demonstrator leader states that it may not be possible to fully collect the data for this as much of the required information may be commercially sensitive and belong to the bus operating companies (but it will be possible to quantify the TfL investment).



London A.2.2




control line/vehicle/ fleet before or

during the demonstration

Collection Availability of KPI/data

from the demo line/vehicle/ fleet


Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,

W=weekly, M=monthly

O=one-off; Ot=Other

Ope

ratio

ns Service Ose10 Charging time X

Safety Osa1 Staff accidents X Consistency Oco1 External effect X

Econ

omy

Costs Eco2 Investment for the network

X

Ener

gy

Supply Esu5 Recharging capacity

X

Envi

ronm

ent

Concentrations

Eco1 CO concentrations X Eco2 NOx concentrations X Eco3 PM10 concentrations X

Other nuisances Eot2 EM Radiation X Table 12 – A.2.2 London KPIs



Table 12 – A.2.2 London KPIs (cont.)

London A.2.2







from the demo line/vehicle/ fleet


Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,

W=weekly, M=monthly

O=one-off; Ot=Other

Envi

ronm

ent

Emissions

Eem1 CO2 emissions X Eem2 CO average emission X Eem3 NOx average emission X

Eem4 PM10 average emission

X

Oth

er

Olon7 Practical issues X Olon8 Buses in service X Olon9 Payments X



The KPIs palette includes also three specific KPIs for A.2.2, i.e.:

• Olon7 - Practical issues, which is a check to ensure potential legal / policy / contractual / planning / environments etc. problems are identified and mitigated. Issues will be identified as work progresses and monitored through the monthly TfL use case strategic oversight meetings. A list of issues and mitigation measures will be produced.

• Olon8 - Buses in service (units), as the number of hybrid and/or fully electric buses the demonstration installation can meet the charging requirements under their normal duty cycle. This will be based on the monitoring of the performance of the charging infrastructure in delivering charge and the ability of buses to operate from that supplied charge.

• Olon9 – Payments, by monitoring of the operation and revenue flows of the demonstration charging system, a narrative will report how to create a sustainable pricing model and ways of paying for the electricity supplied to privately operated buses through this demonstration system.

All the KPIs will be processed within a “during” scenario (data collection to be defined) and no comparisons with the NO ELIPTIC situation have been so far planned, but it is recommended to include ex ante comparisons at least for KPIs on emissions and concentrations. Although A.2.2 is complementary to A.2.1, and therefore the amount of CPs and KPIs so far selected must be considered as a whole for a general outline of A2, more KPIs especially for the “Economy” and “Operations” Evaluation Categories can certainly improve the cross-site comparison, both for A.2.1 and A.2.2.

3.2.2. London C2. Use of metro sub-station for(re)charging TfL fleet vehicles (e-cars and e-vans) and zero-emission capable taxis

C2 is a feasibility study to investigate the potential for using the LU power network for charging electric cars and commercial vehicles, such as TfL support fleet vehicles which may be garaged at common LU and Surface Transport depots. More specifically, within ELIPTIC, directions to identify possible network locations where capacity could exist to support such a rapid charging hub network will be investigated13. To this aim, CPs selected so far are associated to the “Energy” field of application (Table 13). However, for the CP 46 uncertainties already reported for A.2 are re-itered (comment box in Table 13), which leaves the definition of the context relying only on the description of the output from the modeled supply of electric energy. More specific CPs are recommended, for instance to describe the TfL e-fleet to serve and the planned operational features to be compared to the regular ones.

13 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 28 Task 2.2 for more details on the C2 feasibility study



London C.2





scenario


scenario Comments

Sustainability

Power and energy

46

Ratio between energy supplied and energy charged

# vehicle X X


48


kWh 1 h X X

Will be modelled rather than actual delivery

Table 13 – C2 London Context Parameters The list of KPIs for C2 is based virtually on specific KPIs only (Table 14), which are:

• OLon10 - Viable Sites (units), i.e. a count of the number of sites with sufficient space for the installation and operation of high speed charging infrastructures where a cost effective connection to the LU power network could be made. As for Olon1 this will be based on GIS mapping and analysis

• OLon11 - Charging profiles, meant as the evaluation of the likely charging profiles the proposed user base will demand, which is the estimation of the energy consumption (thus KPI Ecn9 can be adapted). This will be investigated by a research into vehicle technical features and usage profiles by the target groups.

• OLon12 - Payments, i.e. the creation of a sustainable pricing model and ways of paying for the electricity supplied to the planned system users, to be defined by the description of possible payment systems which meet the needs of TfL and the user groups.



• OLon13 - Charging capacity, i.e. a measure of the total (fixed and transportable) electrical charging and energy storage capacity the identified locations and systems could support. This will be based on a description of the available supply of electricity from the LU network and the identified means of storing and distributing this energy to end users.

Being OLon 11, 12 and 13 qualitative parameters, the same conclusions previously drawn for the selection of CPs and for A.2 on the need to enlarge the amount of (quantitative) KPIs is valid in this case too.

Table 14 – C2 London KPIs

3.3 Brussels The Brussels use case relies on two feasibility studies within two thematic Pillars (Table 1) and more specifically:

• A3. Progressive electrification of hybrid bus network, using existing tram and underground electric infrastructure

• B2. Optimized braking energy recovery in light rail network described in the next sections14.

14 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 29 - 30 Task 2.3 for more details on the A3 and B2 feasibility studies

London C.2

Evaluation Category

Impact area KPI # KPI Name

No Eliptic scenario Eliptic scenario


control line/vehicle/ fleet before or during the

demonstration




Collection

Start End

Frequency D=daily,

W=weekly, M=monthly O=one-off; Ot=Other,

Start End

Frequency D=daily,


Ener

gy

Supply Esu5* Recharging capacity

X

Oth

er

OLon10 Viable Sites X OLon11 Charging

profiles X OLon12 Payments X OLon13 Charging

capacity X *theoretical measure



3.3.1. Brussels A3. Progressive electrification of hybrid bus network, using existing tram and underground electric infrastructure

A3 will deal with the conversion of three lines, with a special focus on two of them reported in Figure 8.

Figure 8 – A3 Brussels lines

This explains the need to have a large set of CPs (Table 15) to define the context for the feasibility study in terms of operations and energy requirements, both for the NO ELIPTIC and the ELIPTIC scenarios.

Brussels A.3

Area of investigation and application field Parameter ID Parameter Units of



scenario


scenario Comments

Operations

Vehicles

1 Fleet composition Unit X X 2 Operational vehicles Unit X X



vehicle X X

4 Total range km vehicle X X 5 Battery-only range km vehicle X 6 Diesel-only range km vehicle X 8 Distance driven

(route ) km day X X Table 15 – A3 Brussels Context Parameters



Table 15 – A3 Brussels Context Parameters (cont.)

Brussels A.3




scenario


scenario Comments

Operations

Vehicles


10 Distance driven (from depot to route)

km day X X

11 Distance driven (from route to depot)

km day X X

13 Distance driven in electric mode (route)

km day X


15 Empty mass kg vehicle X X 16 Vehicle mass (only

seated pax) kg vehicle X X 17 Battery mass kg vehicle X 18 Cells unit vehicle X

19

Total mass (diesel + transmission + auxiliaries + electric motor)

kg vehicle X X


Charge

21 Total daily time to recharge h vehicle X


h vehicle X

23 Daily time to recharge (depot) X


% vehicle X

25 Charging operations events/ operational time

vehicle X




Brussels A.3




scenario


scenario Comments

Operations


27 Allowed max speed km/h route X X

28 Maximum starting acceleration m/s2 vehicle X X

29 Mean acceleration 0 - 50 km/h m/s2 vehicle X X

30 Mean acceleration 0 - max speed m/s2 vehicle X X

31 Mean braking acceleration 50 km/h - 0

m/s2 vehicle X X

33 Acceleration space 0 - 50 km/h m vehicle X X

34 Acceleration space 0 - max speed m vehicle X X

35 Braking space 50 km/h - 0 m vehicle X X

36 Acceleration space max speed - 0 m vehicle X X

Sustainability

Power and energy

37 Diesel engine power kW vehicle X X 38 HVAC power kW vehicle X X 39 Other auxiliaries kW vehicle X X

40 Total energy stored in batteries kWh vehicle X

41 Batteries nominal capacity Ah vehicle X

42 Energy supplied from batteries 0 - max speed

kWh vehicle X

43 Energy supplied from batteries 0 - 50 km/h

kWh vehicle X

44 Recoverable energy from braking (batteries)

kWh vehicle X


kWh vehicle X




Brussels A.3




scenario


scenario Comments

Sustainability

Power and energy


# vehicle X


# route X

48


kWh 1 h X X

49 Total energy absorbed by diesel engine

kWh/km route X X

50 Energy produced by diesel engine kWh/km route X X

51 Diagrams Traction - speed (electric) vehicle X X

52 Diagrams Traction - speed (diesel engine) vehicle X X

53 Diagrams total efficiency - speed (diesel engine) vehicle X X

54 Diagrams total efficiency - speed (electric) vehicle X

55 Daily energy charged (fast chargers)

kWh/day fast charger X

56 Daily energy charged (depot) kWh/day charging

facility X

Context

Route


Environment

60 Bus stops # route X X 61 Ambient

temperature °C daily average X X

62 Road conditions narrative X X The selection of KPIs similar in quality and quantity (Table 16).



Brussels A.3


NO ELIPTIC scenario ELIPTIC scenario Availability of KPI/data from



demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Staff Ost1 Driving staff X O Ost2 Drivers workload X O Ost5 Maintenance workload X O

Supply

Osu1 Passenger capacity (line) X O

Osu2 Service coverage X O Osu3 Daily supply X O Osu5 Peak vehicles requirement X O

Maintenance

Oma1 Vehicles failures X O Oma2 Days in workshop (or MTTR) X O Oma8 Durability of traction battery X O Oma9 Durability of vehicles X O

Service

Ose2 Bus frequency X Nov’15 Dec’15 D Ose3 Dwell time X Nov’15 Dec’15 D Ose6 Journey time X Nov’15 Dec’15 D Ose7 Round trip time X Nov’15 Dec’15 D Ose8 Operation time X Nov’15 Dec’15 D Ose10 Charging time X O

Table 16 – A3 Brussels KPIs



Table 16 – A3 Brussels KPIs (cont.)

Brussels A.3


NO ELIPTIC scenario ELIPTIC scenario Availability of KPI/data from



demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Econ

omy

Costs

Eco23 Electricity costs for vehicles X O Eco24 Electricity costs for traction X O Eco25 Electricity costs for non traction X O Eco26 Electricity costs for facilities X O Eco27 Fuel costs X O

Ener

gy

Consumption

Ecn 1 Vehicle fuel efficiency X Nov’15 Dec’15 D Ecn 2 Fuel Mix X O Ecn 4 Fuel consumption X Nov’15 Dec’15 D Ecn 5 Fossil fuel (liquid) consumption X Nov’15 Dec’15 D Ecn 9 Electricity consumption X O

Supply Esu3 Energy supplied by batteries at constant speed (50 km/h) X O

Envi

ronm

ent

Emissions

Eem1 CO2 emissions X O Eem2 CO average emission X O Eem3 NOx average emission X O Eem4 PM10 average emission X O



Such appropriate selection will enable to extensively assess impacts on operations, on the costs structure, on the energy demand and on the emissions package. Being A3 a feasibility study, a data collection session will occur at an early stage of the study (more specifically in November – December 2015 for the data which require more field measurements) to provide the necessary inputs for the simulation activities. Results will then be based on the outputs of the models run by STIB. Modeling will require to pay special attention to a number of issues (as already envisaged by the demo leader) as: an appropriate field measurement procedure, the real measurement of the driving cycles for the two lines; the real measurement of the energy (fuel) consumption according to different load conditions; the measurement of auxiliary loads.

3.3.2. Brussels B2. Optimised braking energy recovery in light rail network As for A3, the B2 feasibility study will include more lines (Figure 9) with a special focus on some of them whose selection will be based on two key issues:

• Energy recovery potential • Substation extension opportunity.

Figure 9 – B2 Brussels lines

As B2 has two main validation objectives, i.e. to reduce the energy consumption due to tram operations and therefore contribute to reduce CO2 emissions, the selection of both CPs and KPIs is appropriate to and coherent with such goals. For what concerns CPs (Table 17), 16 additional ones are proposed to characterize substation operations, the energy consumption features specifically related to braking operations, and the related energy savings. The relevance of such parameters in the whole B2 assessment and the availability of data for both the NO ELIPTIC and ELIPTIC scenarios make them eligible to become specific KPIs for B2.



Brussels B.2




scenario


scenario Comments

Operations

Vehicles

1 Fleet composition Unit X X 2 Operational

vehicles Unit X X



vehicle X X

4 Total range km vehicle X X 8 Distance driven

(route ) km day X X



km day X X


km day X X


15 Empty mass kg vehicle X X 16 Vehicle mass (only

seated pax) kg vehicle X X








m/s2 vehicle X X

33 Acceleration space 0 - 50 km/h m vehicle X X


m vehicle X X


36 Acceleration space max speed - 0

m vehicle X X

Table 17 – B2 Brussels Context Parameters



Table 17 – B2 Brussels Context Parameters (cont.)

Brussels B.2




scenario


scenario Comments

Sustainability

Power and energy

38 HVAC power kW vehicle X X 39 Other auxiliaries kW vehicle X X

48


kWh 1 h X X


Context

Route


Environment 60 Bus stops # route X X Other

Other

CPBru1 Number of line substations units X X

CPBru2 Substations per

kilometer unit/km X X

CPBru3 Substations Open Circuit Voltage Volts X X

CPBru4 Substation power

Rating kW X X

CPBru5 Substations monthly consumption

kWh/month X X

CPBru6 Vehicles Energy used for traction kWh/day X X

Energy consumed at pantograph level

CPBru7 Vehicles Braking energy recovered kWh/day X X

Braking Energy sent back to the catenary at pantograph level

CPBru8 Vehicle Energy dissipated in braking resistors

kWh/day U

CPBru9 Ratio 69/68 [%] X X CPBru10 Ratio 70/68 [%] X X



Table 17 – B2 Brussels Context Parameters (cont.)

Brussels B.2




scenario


scenario Comments

Others

Others

CPBru11 Energy recovery system type narrative X

Reversible substation or Energy storage system ()

CPBru12 Number of energy recovery systems per km

unit/km X

CPBru13 Power rating of energy recovery system

kW X

CPBru14 Energy Storage size kWh U

CPBru15 Energy saved per system per month kWh/month X

CPBru16 Energy savings of whole solution kWh/month X

U=uncertainly The selection of KPIs is aimed at describing impacts from the operational performance synthesized in Figure 10, according to the results of simulations.

Figure 10 – B2 Brussels operational scheme for tram braking energy recovery

As expected, such selection (Table 18) therefore focuses on KPIs to report possible operational and energetic variations due to the introduction of the B2 energy recovery system, and its consequences in terms of emission levels.



Brussels B.2




control line/vehicle/fleet

before the demonstration


KPI/data from the demo

line/vehicle/fleet during the

demonstration

Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Staff Ost1 Driving staff X O Ost2 Drivers workload X O

Supply

Osu1 Passenger capacity (line) X O Osu2 Service coverage X O Osu3 Daily supply X O Osu5 Peak vehicles requirement X O

Maintenance Oma9 Durability of vehicles x O

Service

Ose2 Bus frequency X Nov’15 Dec’15 D Ose3 Dwell time X Nov’15 Dec’15 D Ose6 Journey time X Nov’15 Dec’15 D Ose7 Round trip time X Nov’15 Dec’15 D Ose8 Operation time X Nov’15 Dec’15 D

Demand Ode1 Passenger demand X

Table 18 – B2 Brussels KPIs



Table 18 – B2 Brussels KPIs (cont.)

Brussels B.2




control line/vehicle/flee

t before the demonstration

Collection Availability

Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Econ

omy

Costs

Eco6 Vehicle capital costs (for all different vehicles: E-bus / diesel bus, 12m / 18m version etc.)

X O

Eco24 Electricity costs for traction X O Eco25 Electricity costs for non traction X O Eco26 Electricity costs for facilities X O Eco27 Fuel costs X O

Ener

gy

Consumption

Ecn 1 Vehicle fuel efficiency X Nov’15 Dec’15 D Ecn 4 Fuel consumption X Nov’15 Dec’15 D Ecn 9 Electricity consumption X Nov’15 Dec’15 O



Data collection will be alike that of A3 and also in this case B2 will rely on a NO ELIPTIC situation which will act as baseline for the feasibility study.

3.4 Barcelona Barcelona comprehends two study cases both based on the synergy of a feasibility study and a demonstrator, within Thematic Pillars A and C (Table 1) and more specifically:

• A4. Opportunity (re)charging of electric buses based on metro infrastructure • C3. Use of metro/tram infrastructure for recharging e-cars (municipal fleet and

private e-cars) although the latter focuses mostly on the definition of its viability rather than its actual demonstration, as described in the next sections15.

3.4.1. Barcelona A4. Opportunity (re)charging of electric buses based on metro infrastructure

The possibility to use energy from the metro system for charging e-buses, as well as the effects of overnight charging / maintenance activities (when the local metro service is not operational) and the assessment of potential cost reduction due to the opportunity to use the energy system at off-peak hours represent the core of the feasibility study which could pave the way for the actual demonstration. This demonstration, on its turn, is linked to the ZeEUS project, where the cost of charging infrastructures is already addressed, and will complement it by the test on operating the charger connection to the metro substation using the grid. Thanks to the possibility to capitalize on the ZeEUS experience the lists of CPs and KPIs cover all the evaluation categories and fields of investigation proposed, thus enabling a multiscope impact assessment. More specifically, for what concerns the context description, all the proposed CPs will be used (Table19), a very restricted number of which still uncertain.

15 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 30 -31 Task 2.4 for more details on the A4 and C3 use cases



Barcelona A.4





scenario


scenario Comments

Operations

Vehicles

1 Fleet composition Unit X X The analysis will by conducted per bus route.


Fully-electric vs othersin a bus route



vehicle X X

4 Total range km vehicle X X

Taking into account charging operations on-street and at a bus garage

5 Battery-only range km vehicle X X 6 Diesel-only range km vehicle X X

7 Vehicles operational time h day X X

Differentiating operational time by fully-electric vehicles and diesel/gas vehicles




km day X X


km day X X

12 Distance driven in electric mode (to/from depot)

km day X X


km day X X


Data gathered from AVL systems deployed in the vehicles (LOC files)

Table 19 – A4 Barcelona Context Parameters



Table 19 – A4 Barcelona Context Parameters (cont.)

Barcelona A.4





scenario


scenario Comments

Vehicles

15 Empty mass kg vehicle X X Data provided by manufacturer


Considering an occupancy of 80% of capacity. One passenger counts for 70kg.

17 Battery mass kg vehicle X X Data provided by manufacturer

18 Cells unit vehicle U U

19 Total mass (diesel + transmission + auxiliaries + electric motor)

kg vehicle X X


Charge

21 Total daily time to recharge h vehicle X X


h vehicle X X

23 Daily time to recharge (depot) X X


% vehicle X X Data gathered at depot


events/operational time vehicle X X

26 Full charge kg vehicle X X



Regulations of city council and technical data from manufacturer


Data from manufacturer and off-route pilot test carried out by IDDIADA




m/s2 vehicle X X

32 Mean braking acceleration max speed - 0

m/s2 vehicle X X

33 Acceleration space 0 - 50 km/h m vehicle X X Data from

manufacturer and off-route pilot test carried out by IDDIADA


m vehicle X X





Barcelona A.4





scenario


scenario Comments

Kinematics and dynamics 36

Acceleration space max speed - 0

m vehicle X X

Sustainability

Power and energy

37 Diesel engine power kW vehicle x x

38 HVAC power kW vehicle x x 39 Other auxiliaries kW vehicle x x

40 Total energy stored in batteries kWh vehicle x x

41 Batteries nominal capacity Ah vehicle x x


kWh vehicle U

Confidential data to be provided by manufacturer. Measurements during the pilot will not be available


kWh vehicle U


kWh vehicle U


kWh vehicle U


# vehicle X X


# route X X

48


kWh 1 h X X


kWh/km route X No measurements during the Eliptic pilot test


52 Diagrams Traction - speed (diesel engine) vehicle X X

53 Diagrams total efficiency - speed (diesel engine) vehicle X X

54 Diagrams total efficiency - speed (electric) vehicle X X




Barcelona A.4





scenario


scenario Comments

Power and energy 55

Daily energy charged (fast chargers)



facility X X Context


To be provided by CENIT

58 Elevation diagram route X X 59 Length km route X X

Environment



average X X

62 Road conditions narrative X X

U=uncertainly For what concerns the list of selected KPIs (Table 20), these are aimed at describing the testing operations which will involve two new 18m ebuses for the (fast) charging operations at a station at terminals connected to the metro infrastructure (Figure11, left); the same vehicles along with two 12m ebuses (Figure 11, right) will be used to test slow charging operations.

Figure 11 – A4 Barcelona test vehicles

As for the CPs, also in this case A4 plans to use virtually all the proposed KPIs for a total of nearly 100 items. Slight variations will occur for a small amount of KPIs and more specifically:



Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Staff

Ost1 Driving staff X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ost2 Drivers workload X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ost3 Maintenance staff X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ost4 Staff for recharging/refuelling operations X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ost5 Maintenance workload X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ost6 Management workload X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Supply

Osu1 Passenger capacity (line) X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Osu2 Service coverage X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Osu3 Daily supply X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Osu4 Regularity X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Osu5 Peak vehicles requirement X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Maintenance

Oma1 Vehicles failures X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Oma2 Days in workshop (or MTTR) X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma3 Maintenance of the bus components (or MTBF) X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma4 Technical maintenance of the bus X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma5 Failures of non-repairable components (or MTTF) X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma6 Durability of components X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma7 Durability of charging infrastructure X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma8 Durability of traction battery X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma9 Durability of vehicles X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma10 Ratio of non working vehicles X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Table 20 – A4 Barcelona KPIs



Table 20 – A4 Barcelona KPIs (cont.)

Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Service

Ose1 Commercial speed X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ose2 Bus frequency X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ose3 Dwell time X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ose5 Bus Reliability X Jun’15 Dec’16

3-monthly

X Jan’16 Dec’17

3-monthly

Ose6 Journey time X Jun’15 Dec’16 X Jan’16 Dec’17

Ose7 Round trip time X Jun’15 Dec’16 X Jan’16 Dec’17

Ose8 Operation time X Jun’15 Dec’16 X Jan’16 Dec’17

Ose9 Not planned operations X Jun’15 Dec’16 X Jan’16 Dec’17

Ose10 Charging time X Jun’15 Dec’16

3-monthly

X Jan’16 Dec’17

3-monthly Safety Osa1 Staff accidents X Jun’15 Dec’16 X Jan’16 Dec’17

Osa2 Driver accidents X Jun’15 Dec’16 X Jan’16 Dec’17

Consistency Oco1 External effect X Jun’15 Dec’16 X Jan’16 Dec’17

Demand Ode1 Passenger demand X Jun’15 Dec’16 M X Jan’16 Dec’17 M




Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Econ

omy Costs

Eco1 Operating cost (general) X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco2 Investment for the network X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Eco 3 Training operational costs X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco4 Maintenance operational costs X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco5 Drivers operational costs X Jun’15 Dec’16 M X Jan’16 Dec’17 M


X O X O

Eco7 Vehicle capital costs without battery O X O

Eco8 Battery capital cost O X O

Eco13 Components saved U Eco14 Maintenance facility U Eco15 Disposal costs U Eco16 Cash flow X




Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Econ

omy Costs

Eco17 Debt service coverage X Eco18 Residual value of vehicles (10-years) X X Eco19 Residual value of vehicles (15-years) X X Eco20 Residual value of battery X X Eco21 Depot facilities X X Eco22 Recharging infrastructure X X Eco23 Electricity costs for vehicles X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco24 Electricity costs for traction X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco25 Electricity costs for non traction X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco26 Electricity costs for facilities X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco27 Fuel costs X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco28 Grid connection X X Eco29 Interest rate X X Eco30 Electricity costs development X X Eco31 Affordability X X

Revenues

Ere1 Economic surplus X X Ere2 Economic efficiency X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ere3 Revenues per passenger X Jun’15 Dec’16 M X Jan’16 Dec’17 M




Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Economy Incentives Ein 1 Incentives for fuel/energy X X Ein 2 Incentives for vehicle procurement X X

Ener

gy

Consumption

Ecn 1 Vehicle fuel efficiency X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 2 Fuel Mix X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 3 Usage of clean vehicles X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 4 Fuel consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 5 Fossil fuel (liquid) consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 6 Fossil fuel (gas) consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 7 Biofuel (liquid) consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 8 Biofuel (gas) consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 9 Electricity consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ecn 10 Electricity from renewable sources consumption

X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Supply

Esu1 Energy supplied by batteries from 0 to 50 km/h X U

Esu2 Energy required by diesel engine from 0 to 50 km/h

X U

Esu3 Energy supplied by batteries at constant speed (50 km/h)

X U

Esu4 Energy required by diesel engine at constant speed (50 km/h)

X U

Esu5 Recharging capacity X Jun’15 Dec’16 M X Jan’16 Dec’17 M




Barcelona A.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Energy Supply Esu6 Rationalizing energy consumption X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Envi

ronm

ent

Concentrations Eco1 CO concentrations X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Eco2 NOx concentrations X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Eco3 PM10 concentrations X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Noise Eno1 Noise exposure X

Emissions

Eem1 CO2 emissions X X Eem2 CO average emission X X Eem3 NOx average emission X X Eem4 PM10 average emission X X

Peop

le Passengers

Ppa1 Awareness X X Ppa2 Acceptance X X Ppa3 Attractiveness X X Ppa4 Travel comfort X X Ppa5 Noise perception X X

Drivers Pdr1 Driving comfort X X Pdr2 Acceptance X X

U=uncertainly



• Ost 1, Ost2, Ost3: KPIs values referring to the NO ELIPTIC scenario during the demonstration will be gathered from measurement on a control bus route

• Oma2, Oma 4: data quality will depend on the rate of failures and types • Ose2 will be calculated according to data by the local AVL systems (data will be

provided by CENIT) • Ose3 will be calculated according to indirect estimations from data provided by the

local AVL systems • Ose5 will rely on headway adherence rather than punctuality at stops (i.e.

adherence to schedules) • Ode1 will rely on passenger flows at each line and the mileage covered by the fleet. • Esu1, Esu2, Esu3, Esu4 will be based on off-street pilot test (data will be provided

by IDDIADA) • Eco1, Eco2, Eco3, Eno1 Ode1 will rely on estimations of diesel buses conducted in

2014 • Ppa1, Ppa2, Ppa3, Ppa4, Ppa5, Pdr1, Pdr2 will rely on ad hoc survey to be carried

out during the pilot test and on recurrent Passenger Satisfaction Index Surveys. The demonstration activities will enable a full before vs during performance comparison and the test scenarios will be articulated according to more sub-scenarios, more specifically: i) Test A, involving the 12 m length e-buses and ii) Test B, involving the 18 m length e- buses. The main features are summarized in Table 21 and the demonstration routes are described in Figure 12.

Tests Subscenario 0 Subscenario 1 Subscenario 2

A

12 m length e-buses

Test in service Line 20 and 34 before electric bus. Testing period is being planned.

Test in service Line 20 and 34 with electric bus. Echarging in depot. Electrical supply of depot (400 kW). Testing period is being planned. Number buses to test:2

Test in service Line 20 and 34 with electric bus. Echarging in depot. Electrical supply of METRO (Electricity supply company) (2.000 kW). Testing period: Unknown. Number buses to test: 2

B

18 m length e-buses

Test in service Line H16 before electric bus operations. Testing period is being planned.

Test in service Line H16 with electric bus, e-charging in depot. Electrical supply of depot (400 kW) and one opportunity echarging (400 kW). Testing period is being planned. Number buses to test: 2

Test in service Line H-16 with electric bus. E-charging in depot. Electrical supply of METRO (2.000 kW) and two Opportunity e-charging of METRO (400 kW each one). Testing period: Unknown. Number buses to test: 2

Table 21 – A4 Barcelona test scenarios features



Figure 12 – A4 Barcelona demonstration routes

3.4.2. Barcelona C3. Use of metro/tram infrastructure for recharging e-cars This feasibility study is targeted to analyze the potential of using energy from the local metro infrastructure to charge vehicles at parking lots. This calls to investigate two main issues: i) energy needs and amount of rapid charging slots required, and ii) the most appropriate locations. This could give rise to a real demonstration to take place at the new business district of the city. The three validation objectives underpinning C3 are to:

• Increase of the use of public electric vehicles • Improve the quality of the air of the city by cutting down the emissions • Assess urban resilience

The KPIs selection relies on a mix of regular and specific KPIs (Table 22).



Barcelona C.3





demonstration




Collection

Start End

Frequency D=daily,


specify

Start End

Frequency D=daily,

W=weekly, M=monthlyO=one-off; Ot=Other,

specify

Ope

ratio

ns

Staff Ost6 Management workload X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Maintenance Oma6 Durability of components X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Oma7 Durability of charging infrastructure X Jun’15 Dec’16 Y X Jan’16 Dec’17 Y

Service Ose10 Charging time X Jun’15 Dec’16 trimonthly X Jan’16 Dec’17 trimonthly

Safety Osa1 Staff accidents X Jun’15 Dec’16 trimonthly X Jan’16 Dec’17 trimonthly

Consistency Oco1 External effect U Jun’15 Dec’16 trimonthly X Jan’16 Dec’17 trimonthly

Econ

omy

Costs

Eco16 Cash flow X Eco22 Recharging infrastructure X X Eco23 Electricity costs for vehicles X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Eco28 Grid connection X X

Revenues Ere1 Economic surplus X X Ere2 Economic efficiency X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Ere3 Revenues per demand X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Incentives Ein 1 Incentives for fuel/energy X X

Envi

ronm

ent

Other Nuisance Eot2 EM Radiation X X

Waste Ewa1 Hazardous waste X X Ewa2 Non - hazardous waste X X

Table 22 – C3 Barcelona KPIs



Table 22 – C3 Barcelona KPIs (cont.)

Barcelona C.3





demonstration




Collection

Start End

Frequency D=daily,


specify

Start End

Frequency D=daily,


specify

Oth

er

OBar1 General demand X X OBar2 Municipal fleet demand X X OBar3 Network coverage X X OBar4 Proximity X X OBar5 Electric spots X X OBar6 Spot capacity X X OBar7 Effective occupation X X OBar8 Time with 100% occupation X X OBar9 Energy demand X X

OBar10 Energy demand for the municipal fleet

OBar11 CO₂ emissions saved X X OBar12 Noₓ emissions saved X X Obar 13 PM emission saved X X

OBar14 Noise on-street U

Obar15 Noise off-street U

OBar16 Resilience X X OBar17 Maintenance of civil infrastructure X Jun’15 Dec’16 M X Jan’16 Dec’17 M

Obar 18 Maintenance of electric machinery U

U=uncertainly



If the set of general KPIs is aimed at assessing such goals and especially in terms of operations and costs, the specific KPIs contribute to detail the performance to survey. More specifically, to assess the increase of the use of public electric vehicles, such KPIs are:

• OBar1 – General demand (vehicles/day) as the overall demand of electric cars (public and private) in the city and OBar2 – Municipal fleet demand, i.e. the demand related to the municipality, surveyed as Obar1, will define the local demand of e-vehicles

• OBar3 - Network coverage (as the average distance between electric spots groups); OBar4 – Proximity (as the percentage of population close less than X meters to an electric spot); OBar5 - Electric spots, in relation to the yearly share of all the public spots across the city (both on and off-street) and OBar6 – Spots capacity (as the percentage of fulfillment of demand needs in terms of parking spots deployed) will define the local supply.

• OBar7 - Effective occupation charging of the spots deployed (occupancy time/day) and OBar8 - Time with 100% occupation (overall and per group of spots, reported in absolute percentage and in %/spot) will define the occupancy levels per year

This will require a control on the related energy demand, which will correspond to the amount of energy required by the vehicles demand surveyed in Obar1 and Obar 2, i.e.:

• OBar9 - Energy demand (KJ/day) as the overall demand of energy for all the electric cars (public and private) in the city and Obar 10 - Energy demand for municipal fleets (KJ/day) as the similar demand for the municipal fleets

The validation objective to reduce emissions will be assessed in terms of savings, i.e: • OBar11 – CO2 equivalent emission saved per year (in kg/MJ) as the ratio between

kg CO2 equivalent saved / MJ recharged; OBar12 - Noₓ emissions saved yearly (in kg/MJ), calculated similarly to that of the pollutant above; and OBar13 - PM emissions saved yearly (in kg/MJ), calculated similarly to that of the pollutants above

all modeled, and assuming that one EV replaces one traditional car, and considering standard values of emissions. But the assessment of impacts on the local environment will benefitted also by the measurements of:

• OBar 14 - Noise on-street (dB(A)), as the measured noise level at an on-street recharge facility, and OBar 15 - Noise off-street (dB(A)) similarly for an off-street one.

It is also important to stress that C3 places emphasis on the need to survey nuisance as radiations and wastes. The assessment of the urban resilience will rely on:

• OBar 16 – Resilience (as the Use of metro energy / Use of total energy, in terms of time of charge, vehicles, kW, etc) to define the C3 operational and energetic flexibility.

Eventually, complementary to the KPIs on costs are: • Obar 17 - Maintenance of civil infrastructure, as the annual expenditure due to civil

infrastructure maintenance staff (kEURO/vehicle), which reports the sum of expenditure for maintenance staff payment recorded in a year, and adaptation of



Eco4; and Obar 18 - Maintenance of electric machinery, as the annual expenditure due to investment of electric machinery per parking spot (kEURO/spot) yearly recorded, similar to Eco2.

For all of the above more details on units of measurements, data collection process and scenarios have to be defined.

3.5 Warsaw For Warsaw A5 - Use of /tram infrastructure for recharging e-buses no CPs and KPIs were selected, nor evaluation alternatives provided thus far, therefore no impacts evaluation can be presently planned.

3.6 Leipzig The Leipzig study cases belong to Thematic Pillars A and C (Table 1) and more specifically are:

• A6. (Re)charging of e-buses using tram infrastructure • C3. Use of tram network substations for (re)charging e-vehicles.

The former is a feasibility study which could progress as a demonstration whereas the latter is a pure feasibility study, as described in the next sections16.

3.6.1. Leipzig. A6. (Re)charging of e-buses using tram infrastructure Recharging operations within A6 will count on the possibility to use 12 m e-buses and the already existing tram infrastructure. As for Barcelona A4, the work capitalizes local experiences on this from two running national projects: Batterfly, to operate an electric battery bus on an inner city bus line using the available tram infrastructure, and Skorpion to investigate the potentials of lines with articulated buses for the operation of trolley-hybrid buses. The core task in ELIPTIC is to have a comprehensive overview of the electrification potential, according to the above mentioned experiences, and assess the possibility of a full conversion of the conventional bus network into a fully electric-supplied one. The analysis method is the so-called Energy Balancing Calculations (EBC), which enables to assess the energy demand of electric buses using real traffic distance-speed-patterns. To this aim, the “real data” results of both the above mentioned projects can be used to validate the EBC as well as the further development plans within ELIPTIC, according to what is stated in the DoW. This partly explains the current selection of CPs (Table 23), mostly focused on characterizing local demand and supply under the energetic point of view. Such selection also is coherent with the A6 other goal to assess the possibility for recharging the buses (at the bus depot and via opportunity charging). However, the inclusion of more basic CPs to describe the supply and the service (fleet composition, operational times, commercial

16 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 32 Task 2.6 for more details on the A6 and C4 use cases



speed, etc.) could improve the analysis of results from the EBC calculations. Moreover, such data should be easy to collect since EBC processes traffic distance-speed data. However, the CPs selection is coherent with the above mentioned feasibility study goals and the request for more CPs is meant to improve accuracy and understanding of the results from the EBC analysis.

Leipzig A.6





scenario


scenario Comments

Operations

Vehicles 5 Battery-only range km vehicle

X

Data from the national project Batterfly

Charge

21 Total daily time to recharge h vehicle

X


h vehicle

X

23 Daily time to recharge (depot)

X

Sustainability

Power and energy

40 Total energy stored in batteries kWh vehicle

X

48


kWh 1 h

X


kWh/day fast charger

X


facility

X

Context

Route 57 Route description narrative X 58 Elevation diagram route X 59 Length km route X

Environment

60 Bus stops # route X 61 Ambient

temperature °C daily average

X 62 Road conditions narrative

X Table 23 – A6 Leipzig Context Parameters

Not the same can be said for the selection of KPIs (Table 24); the 21 KPIs are mostly targeted to describe impacts under the economic point of view.



Leipzig A.6









Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ope

ratio

ns

Service Ose10 Charging time X

Econ

omy

Costs

Eco1 Operating cost (general) X X Eco2 Investment for the network X Eco3 Training operational costs X


X X

Eco7 Vehicle capital costs without battery X Eco8 Battery capital cost X Eco18 Residual value of vehicles (10-years) X Eco19 Residual value of vehicles (15-years) X Eco21 Depot facilities X Eco22 Recharging infrastructure X Eco23 Electricity costs for vehicles X Eco24 Electricity costs for traction X Eco25 Electricity costs for non traction X Eco27 Fuel costs X Eco28 Grid connection X

Ener

gy

Consumption Ecn 4 Fuel consumption X Dec’15 Jul’16 M

Table 24 – A6 Leipzig KPIs



Table 24 – A6 Leipzig KPIs (cont.)

Leipzig A.6









Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Ener

gy

Consumption Ecn 9 Electricity consumption X Dec’15 Jul’16 M

Envi

ronm

ent

Emissions

Eem1 CO2 emissions X

Eem3 NOx average emission X

Eem4 PM10 average emission X s



Again, this is coherent with the typical need of a feasibility study to assess the affordability of a given option. In this case such need is corroborated by the observation that the only KPIs which can provide data to assess the variations between the NO ELIPTIC and the ELIPTIC scenarios are Eco1 and Eco6. Although very appropriate, the selection of KPIs to assess emissions (Eem1, Eem2 and Eem3), may be not sufficient to assess impacts on the environment, being such KPIs selected only for the NO ELIPTIC scenario, therefore to describe just the ELIPTIC baseline. Impacts on operations are restricted to the assessment of the charging time, which is appropriate but certainly not enough if the goal is to verify the possibility of a full conversion of the conventional bus network into a fully electric-supplied one. For instance, more KPIs within the Service, Maintenance and Staff impact areas could improve the overall evaluation and the cross site analysis. The ELIPTIC is the main scenario for data collection and process although in few cases data will be available also from reference parameters (Eco 18, Eco27, Ecn4). 3.6.2. Leipzig C4. Use of tram network substations for (re)charging e-vehicles According to the name C4 may be considered a twin case with C3 or C5, but this feasibility study strongly differs from these as it addresses the possibility to charge electric vehicles both under the operational (“simulations will be carried out to calculate load capacities (minimum and maximum level) and load management for the existing tram network based on different use case scenarios”, according to the DoW) and under the regulatory points of view. The current selection of KPIs so far gives momentum to such second option, being none of the proposed CPs or KPIs adopted, but only specific reference parameters reported (Table 25).

Leipzig C.4





demonstration




Collection

Start End

Frequency D=daily,


Start End

Frequency D=daily,


Oth

er

National

OLei1 German energy regulations

X X

OLei2 Legal effects X X

OLei3 Recommendations X X

European

OLei4 European energy regulations

X X

OLei5 Legal effects X X

OLei6 Recommendations X X Table 25 – C4 Leipzig Context Parameters



These are: • OLei1 - German energy regulations, as the verification of legal regulations for

recharging of electric vehicles using the existing tram infrastructure in Germany, more specifically the verification of national law towards support or restrictions for using the tram power network to recharge electric vehicles

• OLei2 - Legal effects, as the verification of legal effects for the use of the power supply for vehicles within the transport company or for third parties

• OLei3- Recommendations, or provision of references for the national political decision making process, thanks to the exchange of experience with other public transport companies/ project partners

• OLei4 - European energy regulations, same as OLei1 but extended to the verification of legal impacts for recharging of electric vehicles using the existing tram infrastructure in Europe

• OLei5 - Legal effects, same as OLei2 but at European level • OLei6 - Recommendations, or endorsement for the European political decision

making process As such, these parameters are aimed at qualitatively assess the overall legal background and the regulatory drivers and barriers behind the implementation of systems to recharge e-vehicles from transit facilities, and makes of C4 a “pure regulatory” measure, not comparable to any other within Pillar C, or in the other Pillars. As legal and regulatory aspects cannot be measured, nor calculated or simulated, if C4 will focus only on that, no KPIs are needed and the evaluation will be bases on the qualitative analysis of the results coming from the parameters reported in Table 24. Methods to collect information and provide references are to be defined.

3.7 Oberhausen The Oberhausen use case includes two feasibility studies within two thematic Pillars (Table 1) and more specifically:

• A7. Use of tram infrastructure (catenary and sub-station) for (re)charging e-buses • C5. Fast-charging stations for e-cars powered from the tram network

described in the next sections17.

17 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 33 Task 2.7 for more details on the A7 and C5 feasibility studies



3.7.1. Oberhausen A7. Use of tram infrastructure (catenary and sub-station) for (re)charging e-buses A7 is derived from the local operator’s plans to replace the existing bus fleet with one composed by battery-operated buses, in the medium and long term. The underpinning feasibility study involve two e-bus lines, recently introduced, travelling on the tram infrastructure. On each line a bus equipped with battery operates but on one line, the vehicle is charged from a tram substation, whereas in the other energy is supplied to the vehicle directly from the contact line. Monitoring the performance of each type of vehicle will provide “real data” for the further implementation and more in general for the related impact assessment. Coherently with these goals, the selection of CPs is wide and appropriate to describe the test environment (Table 26).

Oberhausen A.7



Parameter reference


scenario


scenario Comments

Operations

Vehicles





vehicle X X

4 Total range km vehicle X X route+depot>route>depot

5 Battery-only range km vehicle X 6 Diesel-only range km vehicle X 7 Vehicles

operational time h day X X



Total range/day x 365


km day X X


km day X X


km day X

Table 26 – A7 Oberhausen Context Parameters



Table 26 – A7 Oberhausen Context Parameters (cont.) Oberhausen A.7



Parameter reference


scenario


scenario Comments

Vehicles


km day X


15 Empty mass kg vehicle X X


18 Cells unit vehicle X 20 Passenger mass kg (estimated

70 kg per pax) vehicle X X

Charge



h vehicle X

23 Daily time to recharge (depot) h X

Kinematics and dynamics 27 Allowed max

speed km/h route X X

Sustainability

Power and energy



48


kWh 1 h X not measured yet

Context

Route 58 Elevation diagram route X X

route without gradients

59 Length km route X X Environment 60 Bus stops # route X X

The selection of KPIs (Table 27) is clearly targeted to an in-depth analysis of possible impacts of A7 on operations, main cost items to supply the electric option, and energy consumption, in line with the above mentioned goals.



Oberhausen A.7



Availability of KPI/data from control line/vehicle/ fleet before or during the

demonstration


from control line/vehicle/ fleet


Collection

Start End

Frequency (D=daily,

W=weekly, M=monthlyO=one-off; Ot=Other)

Start End

Frequency (D=daily,

W=weekly, M=monthly O=one-

off; Ot=Other)

Ope

ratio

ns

Staff Ost1 Driving staff X Jan'16 Jun'16 O X Jul'16 May'18 O

Ost2 Drivers workload X Jan'16 Jun'16 O X Jul'16 May'18 O

Supply

Osu1 Passenger capacity (line) X Jan'16 Jun'16 O X Jul'16 May'18 O

Osu2 Service coverage X Jan'16 Jun'16 O X Jul'16 May'18 O

Osu3 Daily supply X Jan'16 Jun'16 O X Jul'16 May'18 O

Osu4 Regularity X Jan'16 Jun'16 O X Jul'16 May'18 O

Osu5 Peak vehicles requirement X Jan'16 Jun'16 O X Jul'16 May'18 O

Maintenance

Oma1 Vehicles failures X Jan'16 Jun'16 O X Jul'16 May'18 O

Oma2 Days in workshop (or MTTR) X Jan'16 Jun'16 O X Jul'16 May'18 O

Oma8 Durability of traction battery X Jul'16 May'18 O

Oma9 Durability of vehicles X Jan'16 Jun'16 O X Jul'16 May'18 O

Service

Ose1 Commercial speed X Jan'16 Jun'16 O X Jul'16 May'18 O

Ose3 Dwell time X Jan'16 Jun'16 O X Jul'16 May'18 O

Ose6 Journey time X Jan'16 Jun'16 O X Jul'16 May'18 O

Ose7 Round trip time X Jan'16 Jun'16 O X Jul'16 May'18 O

Ose10 Charging time X Jan'16 Jun'16 O X Jul'16 May'18 O

Demand Ode1 Passenger demand X Jan'16 Jun'16 O X Jul'16 May'18 O

Econ

omy

Costs

Eco23 Electricity costs for vehicles X Jul'16 May'18 O

Eco24 Electricity costs for traction X Jul'16 May'18 O

Eco25 Electricity costs for non traction X Jul'16 May'18 O

Ener

gy

Consumption Ecn 9 Electricity consumption X Jul'16 May'18 O

Ecn 10 Electricity from renewable sources consumption X Jul'16 May'18 O

Table 27 – A7 Oberhausen KPIs



The assessment will be based on the operational performance variations detected between the NO ELIPTIC and ELIPTIC scenarios and on the analysis of the outcomes in terms of costs and energy consumption due to the operations with the e-buses. Data collection periods are appropriate for both scenarios, but the duration of the ELIPTIC one replicates the issue already addressed for A1 (i.e. the May 2018 deadline is beyond the time planned for the evaluation activities, all to conclude by Month 34, April 2018) which leaves 9 to 10 months available for the actual data collection for the ELIPTIC scenario. 3.7.2. Oberhausen C5. Fast-charging stations for e-cars powered from the tram network The concept underpinning this measure is the same of C3 (and C4), i.e. the use of tram infrastructure to supply energy to e-vehicles. Under the operational point of view, for C5 this means, on the one hand to assess the building opportunities and barriers to supply the fast charging station, and on the other how to cope with the current unavailability of charging stations which can be operated with input voltages of 600 volts DC (+ 20% / - 30%), and that would enable the prompt implementation of this technological solution/concept. No CPs have been selected so far, and the selection of KPIs (Table 28) is clearly mono-focused to assess the economic side of the measure, only. The only specific KPI introduced OObe1 – Charging facilities user is aimed at assessing the demand, as it is meant to report the amount of charging facility users by the data provided by the billing system of the charging facility

Oberhausen C.5

Evaluation Category






demonstration




Collection

Start End

Frequency D=daily,

W=weekly, M=monthly; O=one-off;

Ot=Other

Start End

Frequency D=daily,

W=weekly, M=monthly; O=one-off;

Ot=Other

Econ

omy Costs

Eco22 Recharging infrastructure X Nov'15 May'18 Q

Eco26

Electricity costs for charging facility

X Nov'15 May'18 Q

Revenues Ere3 Revenues per user X Oct'15 May'18 Q

Oth

er

OObe1 Charging facility user

X Nov'15 May'18 Q

Q= quarterly

Table 28 – C5 Oberhausen KPIs To improve the possibility to perform a multiscope assessment locally and a cross case comparison it is strongly recommended to include more KPIs.



The very restricted amount of KPIs affect the test scenarios features, which for the moment relies on a data collection, on a quarterly basis, from October – November 2015 to May 2018. The ending time raises the same problems of the suitability of this deadline (May 2018) already reported for A1 and A7.

3.8 Gdynia The Gdynia use case is based on a feasibility study and a feasibility study/demonstrator both within the Thematic Pillar A (Table 1), respectively:

• A8 Opportunity of (re)charging of e-buses connecting Tri-city agglomeration based on trolleybus infrastructure

• A9. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles18

described in the next sections. 3.8.1. Gdynia A8. Opportunity of (re)charging of e-buses connecting Tri-city agglomeration based on trolleybus infrastructure The study on the electrification of the public transport supply in the Tricity area (Gdynia, Gdansk and Sopot) starts from the possibility to assess potentials on a partly unwired extension of a line to the local stadium in Gdansk (Figure 13). To this aim, a trolleybus will be getting off the traction in the city of Sopot (for a distance without network of about 4-5 km) and charged under the network, to get to the stadium just on batteries. This will lead to the analysis on the creation of a charging and parking loop for any electric vehicles in the vicinity of the stadium and on the possibility of a further extension of off-traction trolleybus service to one district in Gdansk, thus closing the loop.

Figure 13 – A8 Gdynia route

The selection of CPs (Table 29) is coherent with the A8 investigation requirements (improve the knowledge on operations, energy patterns and context) and is virtually equal

18 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 35 - 36 Task 2.9 for more details on the A8 feasibility study and A9 feasibility study/demonstrator.



to the selection for the Gdynia twin measure A9. All the CPs are aimed at describing a reference scenario.

Gdynia A.8



Parameter reference


scenario


scenario Comments

Operations

Vehicles

1 Fleet composition Unit X

2 Operational vehicles Unit X

5 Battery-only range km vehicle X 7 Vehicles

operational time h day X

8 Distance driven (route ) km day X

9 Distance driven (total) km year X

Sustainability

Power and energy


only with new vehicles with measuring equipment


Context

Route 57 Route description narrative

59 Length km route X

Environment

60 Bus stops # route X


average X

62 Road conditions narrative X Table 29 – A8 Gdynia Context Parameters

Also the A8 selection of KPIs (Table 30) is consistent with the above mentioned goals as it is meant to assess impacts due to performance variations in terms of energy and operations, with a special focus respectively on the effects on the consumption levels and the maintenance and staff areas. In such latter areas, some minor adaptions for three KPIs are proposed, i.e. for:

• Ost3: provision of only average values for the whole fleet of trolleybuses • Osu2: provision general/average values for the whole fleet • Oma10: substitution with the Technical Readiness Factor

However the core assessment will be on the impacts A8 will have under the economical point of view. Also in this case some minor adaptations are required for the following KPIs:

• Eco1: for which it suggested to have the unit of measurements in 1000 EUR/ vehicle-km

• Eco18, Eco19, Eco20: for which it is suggested to report estimated value at the end of the given period.

Data will be collected from March to June 2016, to describe a NO ELIPTIC scenario for A8.



Gdynia A.8






demonstration




Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Ope

ratio

ns Staff

Ost1 Driving staff X Mar’16 Jun’16 W Ost2 Drivers workload X Mar’16 Jun’16 W Ost3 Maintenance staff X Mar’16 Jun’16 W

Supply Osu2 Service coverage X Mar’16 Jun’16 W

Maintenance Oma1 Vehicles failures X Mar’16 Jun’16 W Oma9 Durability of vehicles X Mar’16 Jun’16 W Oma10 Ratio of non working vehicles X Mar’16 Jun’16 M

Econ

omy

Costs

Eco1 Operating cost (general) X Mar’16 Jun’16 M Eco6 Vehicle capital costs (for all different vehicles X Mar’16 Jun’16 O Eco7 Vehicle capital costs without battery X Mar’16 Jun’16 O Eco8 Battery capital cost X Mar’16 Jun’16 O Eco18 Residual value of vehicles (10-years) X Eco19 Residual value of vehicles (15-years) X Eco20 Residual value of battery X Eco23 Electricity costs for vehicles X Mar’16 Jun’16 M Eco24 Electricity costs for traction X Mar’16 Jun’16 M Eco25 Electricity costs for non traction X Mar’16 Jun’16 M Eco27 Fuel costs X Mar’16 Jun’16 M

Ener

gy

Consumption Ecn 2 Fuel Mix X yearly Ecn 3 Usage of clean vehicles X yearly Ecn 10 Electricity from renewable sources consumption X Mar’16 Jun’16 yearly

Table 30 – A8 Gdynia KPIs



3.8.2. Gdynia A9. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles The main goal of this study is to analyze the local trolleybus network in order to identify potential routes for extending the existing trolleybus service with trolley-hybrid buses running independently on Li-Ion batteries. As for Barcelona A4, also in this case it is possible to capitalize on the synergy with another EC-funded projects, for this feasibility study will be partly validated on data collected within the CIVITAS-DYNAMO. In CIVITAS-DYNAMO a line servicing a central area in Gdynia without catenary (off traction extension by 2 km) is currently tested (1.05.2015 – 31.10.2015) to assess the possibility to extend trolleybus operations across the city. Results from this experience will pave the way to the possibility of a further extension of the trolleybus network or the replacement of conventional diesel buses with battery-trolleys. For what concerns the selection of local CPs (Table 31), this is similar to that of its twin measure A8, and appropriate to the assessment requirement, as previously observed.

Gdynia A.9





scenario


scenario Comment

s

Operations

Vehicles



5 Battery-only range km vehicle X X




Charge 24 State of charge of the battery at the end of operations

% vehicle X X

only with new vehicles with measuring equipment

Sustainability

Power and energy

40 Total energy stored in batteries kWh vehicle X X only with

new vehicles with measuring equipment

41 Batteries nominal capacity Ah vehicle X X

Table 31 – A9 Gdynia Context Parameters



Table 31 – A9 Gdynia Context Parameters, cont.

Gdynia A.9





scenario


scenario Comments

Context


59 Length km route X X

Environment


61 Ambient temperature °C daily average X X

62 Road conditions narrative X X Table 31 – A9 Gdynia Context Parameters

Also for what concerns the selection of KPIs (Table 32), the considerations and the adaptations remarked for A8 are valid in this case, too. In this case, however, the data collection and the proposed testing scenarios are different being the whole assessment based on a NO ELIPTIC vs ELIPTIC performance comparison. But unlike A8, here the replacement of conventional buses with cleaner modes calls for an assessment of impacts on the local environment, currently not planned. It is strongly recommended, therefore, to include in the list of KPIs indicators associated to the “Concentrations”, “Emissions” (aside from that already selected) and “Other Nuisance” impact areas with a specific focus on noise and vibration issues. The replacement of conventional vehicles with innovative ones may be also affect the public perception, and in this case it is suggested to include in the A9 selection of KPIs also indicators associated to the “People” Evaluation Category.



Gdynia A.9





demonstration




Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Ope

ratio

ns Staff

Ost1 Driving staff X Mar’16 Jun’16 W X Mar’17 Jun’17 W

Ost2 Drivers workload X Mar’16 Jun’16 W X Mar’17 Jun’17 W

Ost3 Maintenance staff X Mar’16 Jun’16 W X Mar’17 Jun’17 W

Supply Osu2 Service coverage X Mar’16 Jun’16 W X Mar’17 Jun’17 W

Maintenance Oma1 Vehicles failures X Mar’16 Jun’16 W X Mar’17 Jun’17 W

Oma9 Durability of vehicles X O X O

Oma10 Ratio of non working vehicles X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Econ

omy

Costs

Eco1 Operating cost (general) X Mar’16 Jun’16 M X Mar’17 Jun’17 M


X O X O

Eco7 Vehicle capital costs without battery X O X O

Eco8 Battery capital cost X O X O

Eco18 Residual value of vehicles (10-years) X X

Eco19 Residual value of vehicles (15-years) X X

Eco20 Residual value of battery X X

Eco23 Electricity costs for vehicles X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Eco24 Electricity costs for traction X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Eco25 Electricity costs for non traction X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Eco27 Fuel costs X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 6 Fossil fuel (gas) consumption X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 9 Electricity consumption X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 10 Electricity from renewable sources consumption X yearly base X yearly base

Table 32 – A9 Gdynia KPIs



Table 32 – A9 Gdynia KPIs (cont.)

Gdynia A.9





demonstration

Collection




Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Ener

gy

Consumption

Ecn 2 Fuel Mix X yearly Ecn 3 Usage of clean vehicles X yearly X yearly

Ecn 5 Fossil fuel (liquid) consumption X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 6 Fossil fuel (gas) consumption X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 9 Electricity consumption X Mar’16 Jun’16 M X Mar’17 Jun’17 M

Ecn 10 Electricity from renewable sources consumption X yearly base X yearly base

Envi

ronm

ent

Emissions Eem1 CO2 emissions X Mar’16 Jun’16 M X Mar’17 Jun’17 M



3.9 Eberswalde The Eberswalde use case is based on a single feasibility study/demonstrator within the Thematic Pillar A (Table 1), i.e.:

• A10. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles19

as further reported. 3.9.1. Eberswalde A10. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles For this measure no KPIs have been selected so far, whereas a large amount of CPs are available, for which values have been already provided, as reported in Table 33. Due to the lack of KPIs, no impacts evaluation can be presently planned.

Eberswalde A.10





scenario


scenario Comments

Operations

Vehicles

1 Fleet composition Unit X

Fleet Obus, No-Eliptic scenario 12 unit


For Line 910; No- Eliptic scenario 4 unit, Eliptic scenario 4 unit

4 Total range km vehicle X X

No- Eliptic scenario 31,18 km, Eliptic scenario 31,18 km

5 Battery-only range km vehicle X X

No- Eliptic scenario 0 km, Eliptic scenario 17,84 km

6 Diesel-only range km vehicle X X

No- Eliptic scenario 31,18 km, Eliptic scenario 0 km

Table 33 – A10 Eberswalde Context Parameters

19 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 36 Task 2.10 for more details on the A10 feasibility study.



Table 33 – A10 Eberswalde Context Parameters (cont.)

Eberswalde A.10





scenario


scenario Comments

Operations

Vehicles


From 04:00 to 24:00 ; No- Eliptic scenario 20 h, Eliptic scenario 20 h


29 Journeys x 31,18; No- Eliptic scenario 904,22 km, Eliptic scenario 904,22 km


220 working days + 55 WE; No- Eliptic scenario 214.643 km, Eliptic scenario 214.643 km


km day X X No- Eliptic scenario 4,29 km, Eliptic scenario 4,29 km 11

Distance driven (from route to depot)

km day X X


km day X Eliptic scenario 3,1 km


km day X

29 Journeys x 6,89 km; Eliptic scenario 199,81 km



17 Battery mass kg vehicle X Eliptic scenario 800 km




Eberswalde A.10





scenario


scenario Comments

Operations

Vehicles

19

Total mass (diesel + transmission + auxiliaries + electric motor)

kg vehicle X X

No- Eliptic scenario 19.520 kg, Eliptic scenario 19.520 kg


121 passengers x 70 kg, No- Eliptic scenario 8.470 kg, Eliptic scenario 8.470 kg

Charge


Charging while travelling Eliptic scenario 5,5 h


h vehicle X Eliptic scenario 0,76 h

23 Daily time to recharge (depot) h vehicle X

From 22:00 to 04:00 ; Eliptic scenario 6 h


% vehicle X Eliptic scenario 4,6%



vehicle X

58 cycles for 4 vehicles; Eliptic scenario 14,5 events/ operational time



No- Eliptic scenario 50,7 km/h, Eliptic scenario 50,7 km/h

28 Maximum starting acceleration

m/s² vehicle X X

No- Eliptic scenario 1,1 m/s², Eliptic scenario 1,3 m/s²




Eberswalde A.10





scenario


scenario Comments

Sustainability

Power and energy

37 Diesel engine power kW vehicle X X

No- Eliptic scenario 220 kW, Eliptic scenario 250 kW

38 HVAC power kW vehicle X Eliptic scenario 250 kW

39 Other auxiliaries kW vehicle X

36 KW Heating, 12 KW Air conditioning, 6KW L; Eliptic scenario 52 kW

40 Total energy stored in batteries

kWh vehicle X Eliptic scenario 72 kWh


Eliptic scenario 340 Ah


kWh vehicle X Eliptic scenario 0,5 kWh


kWh vehicle X Eliptic scenario 0,4 Kwh


kWh vehicle X

3,5KWh x 29/4 vehicles; Eliptic scenario 25,4 kWh


# vehicle X Eliptic scenario 0,9


# route X Eliptic scenario 0,8

48


kWh 1 h X

2 vehicles 31,18km a.2,9kWh; Eliptic scenario 180 kWh




Eberswalde A.10





scenario


Comments

Sustainability

Power and energy


kWh/km route X X No- Eliptic scenario 420 kWh/km

50 Energy produced by diesel engine

kWh/km route X X No- Eliptic scenario 6,2 kWh/km

Context

Route 59 Length km route X X


Environment

60 Bus stops # route X X No- Eliptic scenario 54, Eliptic scenario 54


average X X

Typical Central Europe, No- Eliptic scenario 8,5 °C, Eliptic scenario 8,5 °C

62 Road conditions narrative X X Asphalt, good

3.10 Szeged Szeged use case deals with two feasibility studies/demonstrators within the Thematic Pillar A and C (Table 1), i.e.:

• A11. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles

• C.6: Multipurpose use of infrastructure for (re)charging trolley-hybrids and e-vehicles20

as further reported. 3.10.1. Szeged A11. Replacing diesel bus lines by extending trolleybus network with trolley-hybrids vehicles The Szeged use case will model the replacement of some diesel bus lines by the extension of the trolley bus network, operated by trolley-hybrids. Such replacement requires no additional infrastructure. In 2013 the local operator (SZKT) purchased battery-equipped trolleybuses that will be used in the demonstration. For what concerns the

20 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 37 – 38 Task 2.11 for more details on the A11 and C7.



energy provision, charge is supplied by the existing catenary network and to cover the travel between the existing and extended network, trolley buses operate in accumulator mode. As regard to the A11 activities (the test area in Figure 14), first SZKT will start a feasibility study to explore possible/alternative routes, the effects on the local traffic, external effects, along with the identification of external partners and the definition of important indicators.

Figure 14 – A11 Szeged test area

After the results of the feasibility study, the demonstration preparation would involve the following sub tasks:

• Selection of the test route among the studied options • Definition of the transport service based on the traffic and technical parameters • Definition of the required vehicle fleet • Definition of test period and time • Authorization of the test (Partner: Municipality, Authority) • Temporary infrastructure installations (bus-stations)

Measuring device/system preparations (vehicle and catenary system) After the preparation and when the demonstrator is in the execution phase, more subtasks will be carried out, such as:

• Equipment preparation (vehicle, data measurement/collection), • Staff training (drivers, technical assistance, traffic assistance) and



• The test activities. According to all of the above the CP selection (Table 34) is appropriate to define the local testing background in terms operations and charging features and energy supply.

Szeged A.11



measurement Paramete

r reference


scenario


scenario Comment

s

Operations

Vehicles



5 Battery-only range km vehicle X



15 Empty mass kg vehicle X X 17 Battery mass kg vehicle X

Charge


h vehicle X


% vehicle X

Sustainability

Power and energy

37 Diesel engine power kW vehicle X

38 HVAC power kW vehicle X X 39 Other auxiliaries kW vehicle X X


41 Batteries nominal capacity Ah vehicle X X


kWh vehicle X

48


kWh 1 h X

Context

Route 57 Route description narrative X X 59 Length km route X X

Environment 60 Bus stops # route X X 62 Road conditions narrative X X

Table 34 – A11 Szeged Context Parameters The consequent selection of KPIs (Table 35) is aimed at assessing the impacts due to the possible replacement of the diesel bus, according to the following validation objectives:

• Improving the usage rate • Improving economic assessment



• Improving the direct energy consumption • Decreasing environmental impacts • Improving passenger satisfaction • Improving driver satisfaction

These give rise to a list of 22 KPIs which will enable to study impacts not only in terms of operational performance variations, economic outcomes and energy balance, but above all in terms of environmental benefits and passengers’ and drivers’ perception of A11. Among the latter is worth stressing the importance of assessing noise according to a “double check” (as population exposed (KPI Eno1) and as perceived by passengers ( KPI Ppa5). For two KPIs a change in the units of measurements is required, more specifically

• Eco1: instead kEURO/vehicle, suggested EURO/vkm • Ecn 9: instead of MJ/vehicle, suggested MJ/vkm

The KPIs within the People Impact area will be fed by data collected during a survey session from March 30th to March 31st 2016. This being on an one-off basis will require high accuracy, being no opportunity to replicate. The test scenarios includes both the NO ELIPTIC and the ELIPTIC phases which will provide a direct comparison of costs, fuel efficiency, noise perception, emission levels and drivers’ perception of the replacement.



Szeged A.11






demonstration


the demo line/vehicle/ fleet during

the demonstration

Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


off; Ot=Other)

Ope

ratio

ns

Supply

Osu1 Passenger capacity (line) X Dec’15 O

Osu5 Peak vehicles requirement X Dec’15 O X Mar’16 Mar’16 Corrected in the first days, as function of

passenger traffic

Econ

omy

Costs

Eco1 Operating cost (general) X Dec’15 O X Mar’16 May’16 W


X Dec’15 O X O

Eco10 Additional components capital costs X Dec’15 O X O

Eco15 Disposal costs X O X Mar’16 May’16 W

Eco22 Recharging infrastructure X O X Mar’16 May’16 W

Incentives Ein 2 Incentives for vehicle procurement X O O

Ener

gy

Consumption Ecn 1 Vehicle fuel efficiency X O X Mar’16 May’16 W

Ecn 9 Electricity consumption X Mar’16 May’16 W

Table 35 – A10 Szeged KPIs



Table 35 – A10 Szeged KPIs (cont.)

Szeged A.11






demonstration

Collection


the demo line/vehicle/ fleet during

the demonstration

Collection

Start End

Frequency

(D=daily,

W=weekly,

M=monthly

O=one-off;

Ot=Other)

Start End

Frequency (D=daily,


Envi

ronm

ent

Noise Eno1 Noise exposure X Dec’15 X Mar’16 May’16 O

Emissions

Eem1 CO2 emissions X X Jun’16 O

Eem2 CO average emission X X Jun’16 O

Eem3 NOx average emission X X Jun’16 O

Eem4 PM10 average emission X X Jun’16 O

Peop

le Passengers

Ppa1 Awareness X Mar’16 Mar’16 O

Ppa2 Acceptance X Mar’16 Mar’16 O

Ppa3 Attractiveness X Mar’16 Mar’16 O

Ppa4 Travel comfort X Mar’16 Mar’16 O

Ppa5 Noise perception X Mar’16 Mar’16 O

Drivers Pdr1 Driving comfort X X Mar’16 O

Pdr2 Acceptance X X Mar’16 O



3.10.2. Szeged C6. Multipurpose use of infrastructure for (re)charging trolley-hybrids and e-vehicles In the central area of Szeged the electric public transport infrastructure is well networked (Figure 15), with 600V DC available along the tram and trolley-bus lines. Therefore technically, the connecting more vehicles (like e-bikes, e-cars etc.) and users to the main network is not difficult.

Figure 15 – C6 Szeged test area

The list of CPs provides, therefore the basic technical information required to frame the operational, energetic and charging patterns behind C6 (Table 36).

Szeged C.6





scenario


scenario Comment

s

Operations

Vehicles


2 Operational vehicles Unit X

5 Battery-only range km vehicle X

Charge 25 Charging operations


vehicle X

Table 36 – C6 Szeged Context Parameters



Table 36 – C6 Szeged Context Parameters (cont.)

Szeged C.6





scenario


scenario Comment

s

Sustainability

Power and energy

48


kWh X




facility X

Context

Environment 61 Ambient

temperature °C daily average X X

Hungarian Meteorological Service (OMSZ) (/ onboard data recorder)

62 Road conditions narrative X X

The challenge is then on the feasibility of enforcing legal conditions to enable this opportunity, the location of the facilities, their terms of use along with the enforcement of other related measures such as pay or free parking, P+R, B+R. Economic conditions (business model), role of stakeholders, and other effects on the environment and the mobility (for instance modal split, greenhouse gas emission, payback time etc.) have to be considered as well. Therefore, the following proposed set of validation objectives

• Improving knowledge and expanding diversity of e-mobility • Verification of operational conditions • Verification of economic conditions • Improving the direct energy consumption • Decreasing environmental impact • Improving passenger satisfaction • Improving driver satisfaction

is similar to that of A10 but also includes the need to improve knowledge on the e mobility practice. Such similarity affects also the selection of KPIs (Table 37), for which the same considerations reports for A10 are valid in this case too. Also in this case some minor changes in the units of measurement are suggested, i.e:



Szeged C.6





demonstration




Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Ope

ratio

ns

Staff Ost3 Maintenance staff X M

Ost5 Maintenance workload X M

Supply Osu5 Peak vehicles requirement X D

Service Ose10 Charging time X M

Econ

omy Costs

Eco1 Operating cost (general) X M

Eco2 Investment for the network X M

Revenues Ere1 Economic surplus X M

Incentives Ein1 Incentives for fuel/energy X O

Ein2 Incentives for vehicle procurement X O

Ener

gy

Consumption Ecn9 Electricity consumption X D

Supply Esu5 Recharging capacity X D

Envi

ronm

ent Noise Eno1 Noise exposure X O

Emissions

Eem1 CO2 emissions X O

Eem2 CO average emission X O

Eem3 NOx average emission X O

Eem4 PM10 average emission X O

Table 37 – C6Szeged KPIs

30 Nov-15 Version 2.2 / Drafted by UNIROMA1 | 1 0 0


Table 37 – C6Szeged KPIs (cont.)

Szeged C.6





demonstration




Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


off; Ot=Other)

Peop

le

Passengers

Ppa1 Awareness X O

Ppa2 Acceptance X O

Ppa3 Attractiveness X O

Ppa4 Travel comfort X O

Ppa5 Noise perception X O

Drivers Pdr2 Acceptance X O

Oth

er

OSze1 Incentives for charger procurement

X O



• Osu5: instead of vehicles/route km suggested vehicles/hour • Ose10: instead % per vehicle, suggested h/vehicle (min/max/average) • Eco1: instead of kEURO/vehicle, suggested kEURO/charger station • Eco2: instead of kEURO/vehicle, suggested kEURO/charger station • Ere1: suggested kEURO/charger station • Ecn 9: instead of MJ/vehicle, suggested MJ/charger station

Uncertainty is reported for Ost2, since significant legislative changes are under way. Unlike A10, in this case KPIs will describe the ELIPTIC scenario only.

3.11 Lanciano The Lanciano use case is focused on a single feasibility study within Thematic Pillar B (Table 1) and more specifically:

• B3. Light (rail) tram operation for rural rail track, reported as follows21. 3.11.1. Lanciano B3. Light (rail) tram operation for rural rail track B3 focuses on the feasibility study for the implementation of a new tramway urban system by rehabilitating the 27 km long San Vito Marina - Lanciano – Castel Frentano line of the local railway network called Ferrovia Adriatico Sangritana (Figure 16). The rehabilitation should provide a new local tram-train service to address the traffic problems in accessing the local urban areas.

Figure 16 – B3 Lanciano railway network

The operational goals of B3 are therefore to: 21 Refer to Grant Agreement-636012-ELIPTIC, Annex 1 (part A), p. 38, Task 2.12



• improve the accessibility of local public transport and the modal integration • increase the accessibility of inland areas and facilities (hospital, educational

institutions, government offices) and improve the connections between the urban areas of San Vito, Lanciano and Castel Frentano.

• increase pedestrian mobility and discourage car use; • improve air quality reducing pollution and congestion, currently due to the large use

of passenger cars. Such goals must be also considered in terms of affordability and integration with the local land use and this explains the selection of KPIs reported in Table 38. According to such selection the feasibility study, on the one hand, is called to assess impacts typically associated with the introduction of new operations (performance variations under the supply, service and demand points of view), including the related expenditures and energy consumption levels. At the same time, the need to direct the local modal share towards collective modes requires to investigate the demand and more specifically the willingness to change (thus focusing on key concepts such as awareness, acceptance and attractiveness of the new service), and to qualitatively assess the environmental benefits which may be obtained from such modal change. The assessment of passengers, in the case of B3, is important also in light of the planned introduction of high quality rolling stock (low-floor vehicles, higher performance in acceleration and deceleration resulting into improved on-board travel comfort, higher commercial speed) and infrastructure (lack of level crossings). Although the demo leader stated the prospective selection of CPs within the “Vehicle”, “Kinematics and dynamics”, “Route” and “Environment” application fields, currently none have been selected thus far, which does not enable to assess the appropriateness of the required description of the operational background.



Lanciano B.3


NO ELIPTIC scenario ELIPTIC scenario Availability of KPI/data

from control line/vehicle/ fleet before

or during the demonstrati

on



fleet during the

demonstration

Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Ope

ratio

ns Supply

Osu1 Passenger capacity (line) X Osu2 Service coverage X Osu3 Daily supply X Osu5 Peak vehicles requirement X

Service

Ose1 Commercial speed X Ose2 Bus frequency X Ose3 Dwell time X Ose7 Round trip time X

Demand Ode1 Passenger demand X

Econ

omy

Costs

Eco1 Operating cost (general) X Eco2 Investment for the network X Ec 3 Training operational costs X Eco4 Maintenance operational costs X Eco5 Drivers operational costs X Eco6 Vehicle capital costs (for all different vehicles) X

Table 38 – B3 Lanciano KPIs



Table 38 – B3 Lanciano KPIs (cont.)

Lanciano B.3


NO ELIPTIC scenario ELIPTIC scenario Availability of KPI/data

from control line/vehicle/ fleet before

or during the demonstrati

on



fleet during the

demonstration

Collection

Start End

Frequency (D=daily,


Start End

Frequency (D=daily,


Econ

omy

Costs Eco24 Electricity costs for traction X

Revenues Ere2 Economic efficiency X

Ere3 Revenues per passenger X

Incentives Ein 2 Incentives for vehicle procurement X

Ener

gy

Consumption Ecn 9 Electricity consumption X

Envi

ronm

ent

Concentrations

Eco1 CO concentrations X

Eco2 NOx concentrations X

Eco3 PM10 concentrations X

Peop

le

Passengers

Ppa1 Awareness X

Ppa2 Acceptance X

Ppa3 Attractiveness X



4. Remarks and inputs for the next WP3 Tasks activities The current selection of KPIs among all the EUCs is synthesized in Figure 17, from which it is possible to resume some directions to steer the progress of the evaluation, and especially the onset of Task 3.3 activities. In average, with exception of Barcelona, the amount of KPI selected is around 20, mostly focused to describe impacts on operations and costs.

Figure 17 – Selection of KPIs among all the EUCs

As said, the selection of KPIs is an iterative process and after the delivery of the EUC’s Demonstrator Set –up Reports, variations in the local selections will occur. However, this can be considered a good starting point which paves the way for some research issues which will affect the next evaluation activities, as reported in the following sections. It is worth remarking that the following considerations are valid for all the EUCs, either featuring feasibility studies and actual demonstrators or both, as in any case the creation of the reference scenarios must enable an overall knowledge of the baseline from which

0 20 40 60 80 100 120

A.1 BremenB.1 Bremen

C.1 BremenA.2.2 London

A.4 BarcelonaC.3 Barcelona

A.6 LeipzigA.9 Gdynia

A.11 SzegedC.6 Szeged

A.2.1 LondonC.2 London

A.3 BrusselsB.2 Brussels

C.4 LeipzigA.7 OberhausenC.5 Oberhausen

A.8 GdyniaB.3 Lanciano

KPIs

Total

Operations

Economy

Energy

Environment

People

Other



either the full evaluation or the SWOT analysis will be grounded upon, and provide sound bases for the cross site comparisons.

4.1 Impacts areas: recurring and missing fields of investigation Figure 17 evidences that thus far the local preference for the evaluation favored mostly the “Operations” Evaluation Category and within this, the “Service” and “Supply” Impact Areas (Figure 18).

Figure 18 – Evaluation Category Operations: most favored impact areas

This is obviously coherent with the gist of the majority of the EUCs and the need to assess variations in the overall management of the local operations. But, at the same time, some gaps need to be considered, the first of which is that related to the “Demand” area and its associated KPI, Ode1 – Passenger demand (in pass km). This is particularly true for all the feasibility studies and demonstrators aimed at attracting more passengers or users (and especially for those within Pillar C targeted to assess location potentials and Pillar A where

0 5 10 15 20 25 30 35 40

A.1 Bremen

B.1 Bremen

A.2.2 London

C.3 Barcelona

A.4 Barcelona

A.6 Leipzig

A.9 Gdynia

A.11 Szeged

C.6 Szeged

A.3 Brussels

B.2 Brussels

A.7 Oberhausen

A.8 Gdynia

B.3 Lanciano

KPIs

Operations

StaffSupplyMaintenanceServiceSafetyConsistencyDemand



vehicles with different capacities are involved. Moreover Ode1 is easily adaptable to different types of measures. One more area currently neglected is “Consistency”, associated to its KPI OCo1- External effects. The need to assess the compatibility of the electric surcharge imposed by hybrid or electric bus on the already operational electric traction system is a kind of prerequisite for the further development of many measures especially in Pillar A, even if at their feasibility stage. Also “Maintenance”, especially for Pillar C, seems to be of minor interest, but the reduction of maintenance efforts could be an indisputable driver, for instance, in the further transferability analysis. Also in terms of feasibility, the knowledge of current levels of maintenance (which may be also a clue of the weight of this activity in the overall costs breakdown) could be an element of endorsement or prevention in the introduction of innovation. “Economy” is an equally-favored Evaluation Category and the selection of KPIs (Figure 19) is more or less coherent with the need to assess possible savings due to the wider introduction of electrification; however, especially for Pillars C and A, more knowledge on the possibility to generate revenues would certainly improve the quality of the outcomes of both feasibility studies and actual demonstrators.

Figure 19 – Evaluation Category Economy: most favored impact areas

0 5 10 15 20 25 30 35

B.1 Bremen

A.2.2 London

C.3 Barcelona

A.4 Barcelona

A.6 Leipzig

A.9 Gdynia

A.11 Szeged

C.6 Szeged

A.3 Brussels

B.2 Brussels

A.7 Oberhausen

C.5 Oberhausen

A.8 Gdynia

B.3 Lanciano

KPIs

Economy

Costs

Revenues

Incentives



It is also worth reminding that the majority of such parameters will flow together in the CBA run in Task 3.4 and will serve to the development of the business models in WP4. The analysis of favored impacts areas (Figure 20) within the “Energy” Evaluation Category obviously shows the interest to assess variations in the consumption levels, which is consistent with (and linked to) the interest in assessing cost variations previously observed. Impacts on supply are perceived as negligible, but the example of London and Brussels evidence how this area can be paramount when assessing the viability of the ELIPTIC innovations.

Figure 20 – Evaluation Category Energy: most favored impact areas

Evaluation Categories so far reported are shared more or less by the majority of the EUCs, but not the same can be said for the “Environment” Evaluation Category, whose KPIs have been selected just for nine out of the nineteen cases participating in the KPIs collection. Among such restricted group, the most striking aspect is not that the “lion’s share” goes to assess impacts on “Emissions” (Table 21) but the poor relevance of “Noise”. There is no need to stress the relevance to assess noise impacts when introducing new “no oil” propulsion systems, and to this aim the associated KPI (Eno1 – Noise Exposure) is

0 2 4 6 8 10 12 14 16 18

B.1 Bremen

A.2.2 London

A.4 Barcelona

A.6 Leipzig

A.9 Gdynia

A.11 Szeged

C.6 Szeged

C.2 London

A.3 Brussels

B.2 Brussels

A.7 Oberhausen

A.8 Gdynia

B.3 Lanciano

KPIs

Energy

Consumption

Supply



targeted to survey the amount of population to which a change towards more silent technologies could be beneficial (as acknowledged by EUC Szeged). Noise exposure can be a relevant parameter for cases of both Pillars A and C, especially in terms of feasibility study, where high levels of exposure to traffic noise can represent the driver for the introduction of electric buses and trolleybuses.

Figure 21 – Evaluation Category Environment: most favored impact areas

Additional elements of nuisance (associated to KPIs Eot1 -Vibrations and Eot2 - EM radiations) have been virtually neglected with the exception of London A.2.2 and Barcelona C.3. Both vibrations and EM radiations are well known but less field-studied problems and including such impact assessment could be an added value for ELIPTIC. If “Environment” resulted not to be a dominant Evaluation Category, “People” seems to be even less relevant, having been selected just by an even restricted group of EUCs (Figure 22). As for the issue on noise perception, missing such kind of information (especially in terms of people stated or revealed preference) can be detrimental to the full assessment of the opportunity to introduce an innovative transit option. It is strongly recommended to add the KPIs associated with it especially when the study is direct to assess appropriate locations (as in many cases in Pillar C) and the replacement of conventional technologies.

0 1 2 3 4 5 6 7 8 9

B.1 Bremen

A.2.2 London

C.3 Barcelona

A.4 Barcelona

A.6 Leipzig

A.9 Gdynia

A.11 Szeged

C.6 Szeged

A.3 Brussels

B.3 Lanciano

KPIs

Environment

Concentrations

Noise

Other nuisances

Emissions



Figure 22 – Evaluation Category People: most favored impact areas

According to what reported in Chapter 3, in some cases (B.1 Bremen, C.1 Bremen, A.2.2 London, C.3 Barcelona, C.6 Szeged, A.2.1 London, C.2 London, C.4 Leipzig and C.5 Oberhausen) a restricted number of specific KPIs have been introduced. These are aimed at covering the Evaluation Categories by local parameters and according to local priorities. It is not possible to envisage one or more additional field(s) of investigation as the majority of them can be adapted to the available set of KPIs or need more clarifications. The only exceptions are those provided for C.4 Leipzig and C.3 Barcelona. The former, being a “pure” regulatory measure may give rise to a new assessment area on the impact of regulation and legal aspects on the use of tram substation for recharging electric vehicles. The latter, having identified a comprehensive selection of specific KPIs to assess the possibility to create a “Network effect” of recharging points for e-vehicles, could provide a reference for more parameters to share among the other Pillar C EUCs.

4.2 Common KPIs One of the goals in the provision of a large selection of KPIs is to make available an appropriate amount of items that can lead to have some of them shared among more use cases. Also in this case, KPIs common to two or more EUC can be found. The first example is provided by Table 39, where common KPIs within the “Operations” Evaluation Category are reported.

0 1 2 3 4 5 6 7 8

A.1 Bremen

A.4 Barcelona

A.11 Szeged

C.6 Szeged

B.3 Lanciano

KPIs

People

Passengers

Drivers



Operation

A.1

Brem

en

B.1

Brem

en

C.1

Brem

en

A.2.

1 Lo

ndon

A.2.

2 Lo

ndon

C.2

Lond

on

A.3

Brus

sels

B.2

Brus

sels

A.4

Barc

elon

a

C.3

Barc

elon

a

A.6

Leip

zig

C.4

Leip

zig

A.7

Obe

rhau

sen

C.5

Obe

rhau

sen

A.8

Gdy

nia

A.9

Gdy

nia

A.11

Sze

ged

C.6

Szeg

ed

B.3

Lanc

iano

Staf

f

Ost 1-Driving staff Ost 2-Drivers workload Ost 3-Maintenance staff Ost 5-Maintenance workload Ost 6-Management workload

Supp

ly

Osu 1-Passenger capacity (line) Osu 2-Service coverage Osu 3-Daily supply Osu 4-Regularity Osu 5-Peak vehicles requirement

Mai

nten

ance

Oma 1-Vehicles failures Oma 2-Days in workshop (or MTTR) Oma6 - Durability of components Oma 7-Durability of charging infrastructure

Oma 8-Durability of traction battery Oma 9-Durability of vehicles Oma10- Ratio of non working vehicles

Serv

ice

Ose 1- Commercial speed Ose 2- Bus frequency Ose 3- Dwell time Ose 6- Journey time Ose 7- Round trip time Ose 8- Operation time Ose 10- Charging time

Safe

ty

Osa 1- Staff accidents

Cons

iste

ncy

Oco 1- External effect

Dem

and

Ode 1- Passenger demand

Table 39 – Common KPIs within the Evaluation Category Operations Theoretically there are a number of KPIs shared by many EUCs (for instance Ost1, Osu 2 and 3, etc.) but if the share is considered per each Thematic Pillar, it “shrinks” markedly. A



further reduction is observed if common KPIs are considered also per clusters22 of more, “similar” demonstrators/feasibility studies, i.e.

• Cluster 1 - A.2 to A.8, all aimed at assessing opportunity recharging • Cluster 2 – A.9 to A.11, all aimed at assessing the replacement of diesel buses • Cluster 3 – B.1 and B.2, both aimed at assessing the recuperation of braking

energy • Cluster 4 – C.2 to C.5, all aimed at assessing the use of tram/metro infrastructure to

provide energy to e-vehicles. Therefore, if the aim of the comparison is to have a general “Pillar” comparison, the most shared KPIs to be assumed as useful element for impacts comparison are: Pillar A - (four times and more)

• Ost1 – Driving Staff • Ost 2 - Drivers workload • Osu 1 - Passenger capacity • Osu 2 - Service coverage • Osu 3 - Daily supply • Osu 5 - Peak vehicles requirement • Oma 1 - Vehicles failures • Oma 9 - Durability of vehicles • Ose 3 - Dwell time • Ose 6 - Journey time • Ose 7 - Round trip time • Ose 10 - Charging time

Pillar B (two times and more) • Osu 1 -Passenger capacity • Osu 2 - Service coverage • Osu 3 - Daily supply • Osu 5 - Peak vehicles requirement • Ose 1 - Commercial speed • Ose 2 - Bus frequency • Ose 3 - Dwell time • Ose 6 - Journey time • Ode 1 - Passenger demand • Ose 7 - Round trip time • Ose 8 - Operation time

Pillar C (two times and more) • Ost 6 - Management workload • Ose 10 - Charging time

But if the focus is on clusters, the availability decrease as reported in Figure 23 for Cluster 1, where only Ose 10 and Ost 1 and 2 enable a full comparison, whereas the rest is a mix of trinomials and binomials. In the case of binomials, the availability even decrease in significance as the majority of them is composed by A4 + A7, being no other “couples” available. Same problem of poor representativeness can be detected for Cluster 2 where

22 Clustering proposed during the Eliptic User Forum held in Berlin on November 13th, 2015.



the most recurring binomial are A8 + A9, thus no cross case, occurring within the same EUC.

Figure 23 - Cluster 1, Common KPIs for Operations

Commonalities for Cluster 3 are all based on 5 binomials, reported in Figure 24 for B1 + B2.

Figure 24 - Cluster 3, Common KPIs for Operations

0

0,5

1

1,5

2

Ose 2- Busfrequency

Ose 3- Dwell time

Ose 6- Journey timeOse 7- Round triptime

Ose 8- Operationtime



For Cluster 4, cross case comparison seems to rely only on C3 + C6, on the KPIs previously mentioned. Table 40 describes the availability of common KPIs within the “Economy” Evaluation Category.

Economy

A.1

Brem

en

B.1

Brem

en

C.1

Brem

en

A.2.

1 Lo

ndon

A.2.

2 Lo

ndon

C.2

Lond

on

A.3

Brus

sels

B.2

Brus

sels

A.4

Barc

elon

a

C.3

Barc

elon

a

A.6

Leip

zig

C.4

Leip

zig

A.7

Obe

rhau

sen

C.5

Obe

rhau

sen

A.8

Gdy

nia

A.9

Gdy

nia

A.11

Sze

ged

C.6

Szeg

ed

B.3

Lanc

iano

Cost

s

Eco 1-Operating cost (general) Eco 2-Investment for the network Eco 3-Training operational costs Eco 4-Maintenance operational costs Eco 5-Drivers operational costs Eco 6-Vehicle capital costs Eco 7-Vehicle capital costs without battery

Eco 8-Battery capital cost Eco 15-Disposal costs Eco 16- Cash flow Eco 18-Residual value of vehicles (10-years)

Eco 19-Residual value of vehicles (15-years)

Eco 20-Residual value of battery Eco 21-Depot facilties Eco 22-Recharging infrastructure Eco 23-Electricty costs for vehicles Eco 24-Electricty costs for traction Eco 25-Electricty costs for non traction

Eco 26-Electricty costs for facilities Eco 27-Fuel costs Eco 28-Grid connection

Reve

nues

Ere 1-Economic surplus

Ere 2-Economic efficiency

Ere 3-Revenues per passenger

Ince

ntiv

es

Ein 1-Incentives for fuel/energy

Ein 2-Incentives for vehicle procurement

Table 40 – Common KPIs within the Evaluation Category Economy



The availability of common KPIs (four times and more) for Pillar A is based on the recurrence of:

• Eco 1 - Operating cost • Eco 6 - Vehicle capital costs • Eco 7 - Vehicle capital costs without battery • Eco 8 - Battery capital cost • Eco 18 - Residual value of vehicles (10-years) • Eco 19 - Residual value of vehicles (15-years) • Eco 22 - Recharging infrastructure • Eco 23 - Electricity costs for vehicles • Eco 24 - Electricity costs for traction • Eco 25 - Electricity costs for non traction • Eco 27 - Fuel costs

For Pillar B, common KPIs (two times and more) are • Eco 1 - Operating cost • Eco 2 - Investment for the network • Eco 4 - Maintenance operational costs • Eco 6 - Vehicle capital costs • Eco 24 - Electricity costs for traction

and for Pillar C • Ere 1 - Economic surplus • Ein 1 - Incentives for fuel/energy • Ere 3 - Revenues per passenger • Eco 22 - Recharging infrastructure

Such resulting availability of KPIs is very interesting as stresses the major interest for some kinds of cost rather than others, according to the type of demonstrators/feasibility study. For instance for Pillar A, costs of electricity and capital costs seem to be paramount. The cluster analysis reveals that within Cluster B there is no opportunity of cross case comparison. Cluster 4 coincides with the more general availability of Pillar C (for C3 + C5 and C3 + C6) whereas Cluster 2 relies on Eco1 and Eco 6 for the trinomial A8+A9+A11, whereas the other binomials are all within EUC Szeged. Figure 25 stresses how, within Cluster 1, highly recurring KPIs are the three on electricity costs and that on the cost of fuel, which clearly show the relevance of costs for power. The majority of binomials are A4 + A6.



Figure 25 - Cluster 3, Common KPIs for Costs

The breakdown of common KPIs for the “Energy” Evaluation category is reported in Table 41.

Energy

A.1

Brem

en

B.1

Brem

en

C.1

Brem

en

A.2.

1 Lo

ndon

A.2.

2 Lo

ndon

C.2

Lond

on

A.3

Brus

sels

B.2

Brus

sels

A.4

Barc

elon

a

C.3

Barc

elon

a

A.6

Leip

zig

C.4

Leip

zig

A.7

Obe

rhau

sen

C.5

Obe

rhau

sen

A.8

Gdy

nia

A.9

Gdy

nia

A.11

Sze

ged

C.6

Szeg

ed

B.3

Lanc

iano

Cons

umpt

ion

Ecn 1-Vehicle fuel efficiency Ecn 2-Fuel Mix Ecn 3-Usage of clean vehicles Ecn 4-Fuel consumption Ecn 5-Fossil fuel (liquid) consumption

Ecn 6-Fossil fuel (gas) consumption

Ecn 9-Electricty consumption Ecn 10-Electricty from renewable sources consumption

Supp

ly

Esu 3-Energy supplied by batteries at constant speed (50 km/h)

Esu 5-Recharging capacity Table 41 – Common KPIs within the Evaluation Category Economy



To a lower selection of KPIs corresponds a lower availability of common KPIs, virtually nonexistent for Pillar C which relies only on the comparability of Esu 5 - Recharging capacity between C2 and C6, and Pillar B which is based on the comparison of Ecn 9 -Electricity consumption among B1, B2 and B3. Both Esu5 and Ecn9 become also the only terms of comparison respectively for Clusters 4 and 3. The same applies to Cluster 2, where the only common KPI is Ecn 9 - Electricity consumption available for the couple A9 + A11. Ecn9 and Ecn2 - Fuel Mix are the only KPIs recurring four times or more within the Pillar A, and the analysis of Cluster 1(Figure 26) stresses again the relevance of Ecn9 for A3, A4, A6 and A7.

Figure 26 - Cluster 1, Common KPIs for Energy

Common KPIs within the “Environment” Evaluation Category (Table 42) can be found only within Pillar A, i.e.:

• Eco 1 - CO concentrations • Eco 2 - NOx concentrations • Eco 3 - PM10 concentrations • Eno 1 - Noise exposure • Eem 1 - CO2 emissions • Eem 2 - CO average emissions • Eem 3 - NOx average emissions • Eem 4 - PM10 average emissions

01234567

Ecn 1-Vehicle fuelefficiency

Ecn 2-Fuel Mix

Ecn 3-Usage of cleanvehicles

Ecn 4-Fuel consumption

Ecn 5-Fossil fuel (liquid)consumption

Ecn 9-Electrictyconsumption

Ecn 10-Electricty fromrenewable sources

consumption

Esu 3-Energy suppliedby batteries at constant

speed (50 km/h)

Esu 5-Rechargingcapacity



They clearly show the major interest in assessing impacts on perceived noise, air quality and climate change.

Environment

A.1

Brem

en

B.1

Brem

en

C.1

Brem

en

A.2.

1 Lo

ndon

A.2.

2 Lo

ndon

C.2

Lond

on

A.3

Brus

sels

B.2

Brus

sels

A.4

Barc

elon

a

C.3

Barc

elon

a

A.6

Leip

zig

C.4

Leip

zig

A.7

Obe

rhau

sen

C.5

Obe

rhau

sen

A.8

Gdy

nia

A.9

Gdy

nia

A.11

Sze

ged

C.6

Szeg

ed

B.3

Lanc

iano

Conc

entr

atio

ns

Eco 1-CO concentrations

Eco 2-NOx concentrations

Eco 3-PM10 concentrations

Oth

er

nuis

ance

s

Eot2- EM Radiation

Noi

se

Eno 1-Noise exposure

Emiss

ions

Eem 1-CO2 emissions

Eem 2-CO average emission

Eem 3-NOx average emission Eem 4-PM10 average emission

Table 42 – Common KPIs within the Evaluation Category Environment

Along with Cluster 2 for the binomial A9 + A11, such interest especially in assessing impacts on the CO2

emissions levels is also noticeable in Cluster 1 (Figure 27) where Eem1 is shared by A.2.2, A3, A4 and A6 and in Cluster 2 for the binomial A9 + A11. The same group of EUCs shares also the remaining KPIs on emissions, with exception of that related to CO (Eem2) which is shared only by A.2.2, A3, A4.



Figure 27 - Cluster 1, Common KPIs for Environment

To conclude, Table 44 describes the situation for the “People” Evaluation Category. There is no possibility so far to have common KPIs per specific clusters, but given the nature of the KPIs related to assess the passengers’ and drivers’ perception of the ELIPTIC innovations, it might be likely a cross-cluster assessment of impacts according to the results of all the KPIs reported in Table 43.

People

A.1

Brem

en

B.1

Brem

en

C.1

Brem

en

A.2.

1 Lo

ndon

A.2.

2 Lo

ndon

C.2

Lond

on

A.3

Brus

sels

B.2

Brus

sels

A.4

Barc

elon

a

C.3

Barc

elon

a

A.6

Leip

zig

C.4

Leip

zig

A.7

Obe

rhau

sen

C.5

Obe

rhau

sen

A.8

Gdy

nia

A.9

Gdy

nia

A.11

Sze

ged

C.6

Szeg

ed

B.3

Lanc

iano

Pass

enge

rs

Ppa 1-Awareness Ppa 2-Acceptance Ppa 3-Attractiveness Ppa 4-Travel comfort Ppa 5-Noise perception

Driv

ers Pdr 1-Driving comfort

Pdr 2-Acceptance Table 43 – Common KPIs within the Evaluation Category People

4.3 Further Developments The outcomes thus far reported steer the progress of the evaluation activities towards a series of actions to improve the definition of the test scenarios and quality and quantity of the selected KPIs also in sight of the provision of the baseline scenarios (Task 3.3) which is the reference term for the overall impact assessment at each EUC.

01234567

Eco 1-CO concentrations

Eco 2-NOx concentrations

Eco 3-PM10 concentrations

Eem 1-CO2 emissionsEem 2-CO average emission

Eem 3-NOx average emission

Eem 4-PM10 average emission



As said the selection of KPIs is an iterative process, therefore the next actions in this procedure can, then, progress as follows:

• Improving the KPIs selection Results reported in section 4.2 clearly show the need to enlarge the current selections of KPIs for two main reasons: a) to cover elements of assessment hitherto not much considered among those already proposed (especially noise, demand and staff, public perception), or new ones introduced as specific for one demonstrator/feasibility study and worth of wider interest among the EUC (for example the regulatory issue introduced by Leipzig C4); b) to improve the overall general cross case analysis and provide a sound basis for the prospective transferability exercise. To progress with the KPIs selection, the proposed action is based on the principle “learning from the others”: the current selections of all the EUC will be circulated among all the demo leaders for a local review about the possibility to include more KPIs according to the selection made to his/her peers in the same Pillar (or in the other Pillars as well), according to a sensibly “Pick ‘n’ mix” approach. This can help, on the one hand, cover some less considered issues and, on the other, foster some “stronger” impacts areas (for example those in the Evaluation Category related to costs).

• Fixing the scenarios For many EUCs data collection is still to be decided or uncertainties are present. Also in this case the proposed action is to proceed towards a fast revision of the feasibility in the data collection and to clear uncertainties. This activity goes hand in hand with that of enlarging the local KPIs palettes above mentioned and the expected result is to have a defined set of KPIs framed within fixed scenarios, especially for the baseline term for both the Full Evaluation (for which it is expected to have a definite set of NO ELIPTIC and ELIPTIC conditions) and the Technical Viability (for which it is expected to have a definite set of NO ELIPTIC conditions).

• Enabling the data collection A reporting data spreadsheet with the features reported in section 2.5 will be circulated among the EUCs as soon as the scenarios and the KPIs lists will be settled.

• Finalizing the terms for the cross case comparison From the results of section 4.2 it is also clear that to perform the cross case comparison the enlargement of the selection of KPIs is needed not only to enlarge the quantity of issues available but also to have more significant sets of EUCs to compare within the same Pillar and inter-pillars. Generally speaking, it would be advisable to have a set of KPIs large enough to perform a full comparison among all the items of the group included in the analysis. But if the items included in the group differ, then it is necessary to identify clusters of similar items and perform dedicated



comparison analysis for each of them. Needless to say, the higher the number of clusters, the larger the amount of common KPIs must be. The comparison of KPIs within Pillar A may serve as an example to the current difficulties in this field according to the KPIs status quo. The current amount of common KPIs among all the EUCs could be theoretically sufficient to perform a general comparative analysis for some Evaluation Categories. However, under the practical point of view, since the EUCs associated to Pillar A can be divided into 3 Clusters (Cluster 1, Cluster 2 and A1 which represents a “mono-cluster” itself, being the local demonstrator different from the others of Pillar A), the amount of KPIs available per single cluster is strongly reduced, thus affecting the quality of the comparison per cluster. In addition, being the amount of KPIs selected by A4 in average three times higher than that of the other EUCs, this means that most of the comparison “intra-pillar” between Cluster 1 and A1 or Cluster 1 and Cluster 2 (or both) is formed by the KPI binomial BKPI

𝐵𝐵𝐾𝐾𝐾𝐾𝐾𝐾 = 𝐶𝐶1 + �𝐴𝐴𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛

where virtually in all cases 𝐶𝐶1 = 𝐴𝐴4 + � 𝐴𝐴𝑚𝑚

𝑚𝑚𝑛𝑛𝑚𝑚

𝑛𝑛 = {2, 3, 6, 7, 8} 𝑚𝑚 = {1, 9, 11}

with Am being the other use cases associated to Cluster 1 and An the use cases associated to Cluster 2 and A1. This means a dominance of A4 in the overall comparison, which on the one hand could be interpreted as a strong element in the overall assessment, but on the other hand can be misleading if the terms of comparisons differ in scale (for instance A1 or A11). Moreover, the current availability of KPIs, within the cluster analysis “intra-pillar” and “inter-pillar” does not cover all the Evaluation Categories. This is obviously even worse where there are lesser KPIs, Clusters and EUCs, as in Pillars B and C. Again the solution is to enlarge the amount of KPIs to improve accuracy and soundness of the cross case analysis, both at the cluster and at pillar levels.

It is clear that the enlargement of the selection of KPIs can be now assumed as the main activity in the evaluation progress, which affects all the others above mentioned. It is expected a fast revision process (the so –called “sensibly pick ‘n’ mix”) so to have concluded all the activities by the first quarter of 2016, to meet the requirements of those EUC which plan to start an earlier data collection.



ANNEXES Annex 1 List of Key Performance Indicators Anne 2 List of Context Parameters



Annex 1 – List of Key Performance Indicators Evaluation Category: Operations



Unit of measurement

Reference period

Staf

f

Ost1 Driving staff Staff involved in driving activities counting man/vehicle day

Ost2 Drivers workload Workload required to drive a vehicle FTE man-month/vehicle month

Ost3 Maintenance staff Amount of personnel with maintenance duties divided by the amount of vehicles

counting man/vehicle day

Ost4 Staff for recharging/ refuelling operations Staff involved in refuelling/recharging activities counting man/vehicle day

Ost5 Maintenance workload Workload required to maintenance activity per vehicle FTE man-month/vehicle month

Ost6 Management workload Workload required to management and planning activities per month FTE man-month/vehicle month

Table 1 – KPIs for Evaluation Category: Operations



Table 1 – KPIs for Evaluation Category: Operations (cont.)

Evaluation Category: Operations



Unit of measurement

Reference period

Supp

ly

Osu1 Passenger capacity (line) Passengers volume that can be carried past a single point on a fixed route, in a given period of time

Product of the frequency and the maximum number of persons per vehicle pass/h peak time

Osu2 Service coverage Consistency of the service Travelled km divided by the number of operational vehicles per line km/veh day

Osu3 Daily supply Places (seat and standing) volume that can be carried on a fixed route per each vehicle, in a given period of time

Total amount of supplied places per day divided by the amount of daily operating vehicles places/veh day

Osu4 Regularity Missed trips per line Amount of missed departures divided by the monthly scheduled departures on a given line and multiplied by 100

% month

Osu5 Peak vehicles requirement The maximum number of vehicles required to operate a transport service at peak periods

Total amount of vehicle required to operate in the morning/afternoon peak hours vehicles/route km peak time

Mai

nten

ance

Oma1 Vehicles failures Monthly events recorded per vehicle and per travelled km

The events recorded for the vehicle divided by the km traveled by the vehicle in a month events/traveled km


Oma2 Days in workshop (or MTTR)

Average time required to repair a vehicle due to failed component or device in workshop (to be specified per component)

The corrective maintenance time at workshops divided by the total number of corrective maintenance actions in a month h/action


Oma3 Maintenance of the bus components (or MTBF)

Recorded time between two failures for a repairable component

Sum of the operational periods divided by the number of observed failures

days/failure per component








Unit of measurement

Reference period

Mai

nten

ance

Oma4 Technical maintenance of the bus

Recorded time between consecutive failures of a vehicle in operation Arithmetic mean time between recorded failures of a vehicle days


Oma5

Failures of non repairable components (or MTTF)

Recorded time between for a non-repairable component to fail Sum of the operational periods prior the failure days


Oma6 Durability of components Lifetime of a given mechanical component



Oma7 Durability of charging infrastructure Lifetime of charging infrastructure

Expected lifetime of charging infrastructure according to manufacturer specifications years


Oma8

Durability of traction battery Lifetime of the traction battery (E-Bus or Hybrid-Bus)



Oma9 Durability of vehicles Lifetime of a vehicle



Oma 10

Ratio of non working vehicles

Amount of unproductive vehicles due to technical failures, breakdown, etc.

Amount of days in workshop for repairments divided by the total amount of operational days per vehicle multiplied by 100 %








Unit of measurement

Reference period

Serv

ice

Ose1 Commercial speed Speed of operational vehicles For a given line, the total distance traveled divided by total time taken

(including schedule holding) km/h peak time in working day

Ose2 Bus frequency Arrivals recorded at a given stop Average amount of bus arrivals at a given stop on a selected route per hour events/h peak time in working day

Ose3 Dwell time Time spent for boarding/alighting passengers at a bus stop Average of all the time spent at a scheduled stop without moving minutes peak time in

working day

Ose4 Bus Punctuality

Timely operation of buses according to their operation schedules

For AVM-equipped fleets, and with reference to a specific bus route and stop, the daily amount of vehicles departing within a window of 1 minute early up to 5 minutes late is divided by the total daily amount of vehicles operating on the selected route and bus stop and multiplied by 100.

% peak time in working day

Ose5 Bus Reliability occurrences in which a vehicle arrives within a given interval around timetable times.

amount of arrival times per month that are within a given interval around the time shown in the timetable divided by the total arrival times recorded in the same month and multiplied by 100

% month

Ose6 Journey time Time spent for a single journey average of the time recorded for single journey, on a given route min peak time in working day

Ose7 Round trip time

Time between two subsequent passages of the same vehicle at a given point Time recorded for a round trip, on a given route min peak time in

working day

Ose8 Operation time Vehicles operational time as scheduled h/vehicle Month







Unit of measurement

Reference period

Serv

ice

Ose9 Not planned operations

Amount of additional/spare vehicles due to unplanned events (failures) as reported vehicles/month Eliptic demo

timeframe

Ose10 Charging time Amount of time due to fuel/recharging operations Sum of all the time spent for refuelling/recharging operations in a month divided by the monthly operational time of a vehicle and multiplied per 100

% per vehicle month

Safe

ty

Osa1 Staff accidents Maintenance personnel exposure to risks and damages related to Eliptic demo activities

Amount of personnel with maintenance duties involved in reported accidents (exposure to toxic-harmful products, irregular bus manoeuvres, etc.) divided by the annual workload

man/h month (possibly year to improve accuracy)

Osa2 Driver accidents Drivers involvement in accidents related to Eliptic demo activities Amount of driving staff involved in reported accidents divided by the

annual workload man/h month (possibly year to improve accuracy)

Con

sist

ency

Oco1 External effect Compatibility of the electric surcharge imposed by hybrid or electric bus on the already operational electric traction system Occurrence of significant voltage drops in electrical supply lines # Eliptic demo

timeframe

Dem

and

Ode1 Passenger demand Amount of passenger- kilometres travelled every month per line volume of passengers multiplied by the vehicles mileage per line passkm monthly







Unit of measurement

Reference period

Staf

f

Ost1 Driving staff Staff involved in driving activities counting man/vehicle day

Ost2 Drivers workload Workload required to drive a vehicle FTE man-month/vehicle month

Ost3 Maintenance staff

Amount of personnel with maintenance duties divided by the amount of vehicles composing the fleet counting man/vehicle day

Ost4

Staff for recharging/refuelling operations

Staff involved in refuelling/recharging activities counting man/vehicle day

Ost5 Maintenance workload Workload required to maintenance activity per vehicle FTE man-month/vehicle month

Ost6 Management workload

Workload required to management and planning activities per month FTE man-month/vehicle month



Table 2 – KPIs for Evaluation Category: Costs

Evaluation Category: Economy



Unit of measurement

Reference period

Cos

ts

Eco1 Operating cost (general)

Monthly expenditure due to staff, energy, maintenance management, to purchase external goods and services, to financial costs, depreciation, and taxes

Sum of all the expenditures for operations recorded in a month kEURO/vehicle month

Eco2 Investment for the network

Annual expenditure due to investments in infrastructures, vehicles and other items

Sum of expenditure for investments recorded in a year kEURO/vehicle year

Eco 3 Training operational costs Monthly expenditure due to staff training and updating Sum of expenditure for training recorded in a month kEURO/vehicle month

Eco4 Maintenance operational costs Monthly expenditure due to maintenance staff

Sum of expenditure for maintenance staff payment recorded in a month kEURO/vehicle month

Eco5 Drivers operational costs Monthly expenditure due to drivers

Sum of expenditure for drivers payment recorded in a month kEURO/vehicle month

Eco6

Vehicle capital costs (for all different vehicles: E-bus / diesel bus, 12m / 18m version etc.) Capital costs for vehicle owned Sum of expenditure for each vehicle owned kEURO/vehicle

Eco7 Vehicle capital costs without battery Capital costs for vehicle owned without battery Sum of expenditure for each E-Bus kEURO/vehicle

Eco8 Battery capital cost Capital cost for vehicle traction battery Sum of expenditure for all battery system components kEURO/kWh



Table 2 – KPIs for Evaluation Category: Costs (cont.)




Unit of measurement Reference period

Cos

ts

Eco9 Spare parts capital costs Capital costs for the assortment of stored spare parts Sum of expenditure for assortment kEURO/vehicle

Eco10

Additional components capital costs Capital costs for the assortment of stored additional components Sum of expenditure for assortment kEURO/vehicle

Eco11 Spare parts operational costs Operational costs for the replenishment of spare parts Sum of expenditure for the replenishment kEURO/vehicle month

Eco12 Batteries operational costs Operational costs for the replenishment of batteries Sum of expenditure for the replenishment kEURO/vehicle month

Eco13 Components saved Savings related to components saved due to the implementation of the Eliptic technologies

Sum of savings induced by the introduction of new technologies kEURO/vehicle month

Eco14 Maintenance facility Costs for fully equipped maintenance stall Sum of expenditure for operating maintenance stalls kEURO/vehicle Month

Eco15 Disposal costs Costs for disposal of used items (solid and/or fluid) Sum of expenditure to dispose of used items kEURO/vehicle month

Eco16 Cash flow net profit plus amounts charged off for depreciation, depletion, and amortization

Equals cash receipts minus cash payments over a given period of time kEURO/vehicle


Eco17 Debt service coverage ability to cover, or pay off, debt for the innovation installed

amount of cash or cash flow required to pay off a debt, and how much the total debt actually is. kEURO/vehicle




Table 2 – KPIs for Evaluation Category: Costs




Unit of measurement

Reference period

Cos

ts

Eco18 Residual value of vehicles (10-years)

sale value of the vehicles after 10 years of operational lifetime estimation kEURO/vehicle 10 years

Eco19 Residual value of vehicles (15-years)

sale value of the vehicles after 15 years of operational lifetime estimation kEURO/vehicle 15 years

Eco20 Residual value of battery sale value of the battery at end of life estimation kEURO/kWh battery lifetime

Eco21 Depot facilities Costs for the use of any kind of depot facilities (include rent, leasing, utilities, maintenance, etc. )

Sum of all the costs due to operate the depot for the Eliptic activities kEURO/vehicle


Eco22 Recharging infrastructure Costs for the use of the recharging infrastructure Sum of all the costs due to operate the recharging activities

kEURO/per charging operation


Eco23 Electricity costs for vehicles Total costs for electricity

Sum of itemized expenditures due to electricity (include all items) kEURO/vehicle month

Eco24 Electricity costs for traction Total costs for electricity due to traction operations Sum of expenditure due to traction operations kEURO/vehicle month

Eco25 Electricity costs for non traction

Total costs for electricity to operate non traction equipment (auxiliaries, etc). Sum of expenditure due operate on board equipment kEURO/vehicle month







Unit of measurement

Reference period

Cos

ts

Eco26 Electricity costs for facilities Total costs for electricity to operate maintenance facilities

Sum of expenditure due operate maintenance facilities kEURO/vehicle month

Eco27 Fuel costs Total costs for fuel purchase Sum of expenditure due to purchase fuels kEURO/MJ month

Eco28 Grid connection Cost of the use of the grid per recharging facility Sum of all the costs for connecting the recharging equipment to the grid

kEURO/per charging equipment


Eco29 Interest rate Average interest rate in investigated period estimation %

Investigated period for life cycle calculations

Eco30 Electricity costs development Average development of costs for electricity Contains price increase and inflation %

Investigated period for life cycle calculations

Eco31 Affordability

Comparison of costs due to fuel for conventionally-fuelled vehicles and costs for the provision of energy for the same amount and type of e-vehicles operating in same way (time and mileage)

Sum of expenditure due to fuel purchase divided by the sum of expenditure due to the supply of electric energy # 1000 km







Unit of measurement

Reference period

Rev

enue

s

Ere1 Economic surplus Monthly benefit due to operations Profit divided by the service production kEURO/vehkm month

Ere2 Economic efficiency Monthly capability of operations to generate revenues according to the passenger demand

Amount of ticket revenues divided by the passenger demand kEURO/passkm month

Ere3 Revenues per passenger

all income generated from fares and tickets for PT services on a monthly bases according to the demand.

Amount of ticket revenues divided by the passenger volume kEURO/passkm month

Ince

ntiv

es Ein 1

Incentives for fuel/energy Reduced price for fuel or electricity granted by external bodies regulatory reference Euro/MJ

Ein 2 Incentives for vehicle procurement Reduced price for vehicle procurement granted by external bodies regulatory reference Euro/vehicle



Table 3 – KPIs for Evaluation Category: Energy

Evaluation Category: Energy



Unit of measurement

Reference period

Con

sum

ptio

n

Ecn 1 Vehicle fuel efficiency Fuel used per vehicle km, per vehicle type.

Data should be derived starting from the city fleet characteristics (number and type of vehicles). A splitting of the vehicle types is recommended. For each class, the amount of the monthly total vkm multiplied by the average consumption provides the consumption per type of fuel and per vkm. The result thus obtained should be converted in MJ.

MJ/vkm month

Ecn 2 Fuel Mix Energy monthly used per type of fuel, per vehicle type

Data should be derived starting from the city fleet characteristics (number and type of vehicles). A splitting of the vehicle types is recommended. For each class, the total monthly vkm multiplied by the average consumption gives the total monthly consumption per vehicle type. The results should be combined per type of fuel (which is known for each type of vehicle). The amounts thus obtained should be converted in MJ.

MJ month

Ecn 3 Usage of clean vehicles

Level of exploitation of clean fleets per type of fuel (water-diesel emulsion, biodiesel, bioethanol, biogas, CNG, LPG, electricity)

Amount of vkm according to type fuel divided by the total amount of vkm operated by the fleet and multiplied per 100 % month

Ecn 4 Fuel consumption Total amount of fuel consumed as reported per vehicle MJ/vehicle Day

Ecn 5 Fossil fuel (liquid) consumption

Total amount of fossil fuel consumed (liquid) as reported per vehicle MJ/vehicle day



Table 3 – KPIs for Evaluation Category: Energy (cont.)

Evaluation Category: Energy



Unit of measurement

Reference period

Con

sum

ptio

n

Ecn 6

Fossil fuel (gas) consumption Total amount of fossil fuel consumed (gas) as reported per vehicle MJ/vehicle day

Ecn 7 Biofuel (liquid) consumption Total amount of biofuel consumed (liquid) as reported per vehicle MJ/vehicle day

Ecn 8 Biofuel (gas) consumption Total amount of biofuel consumed (gas) as reported per vehicle MJ/vehicle day

Ecn 9 Electricity consumption Total amount of electricity consumed as reported per vehicle MJ/vehicle day

Ecn 10

Electricity from renewable sources consumption Total amount of electricity from renewable sources consumed as reported per vehicle MJ/vehicle day

Ecn 8 Biofuel (gas) consumption Total amount of biofuel consumed (gas) as reported per vehicle MJ/vehicle day



Table 3 – KPIs for Evaluation Category: Energy (cont.) Evaluation Category: Energy


Collection method/sources Unit of measurement

Reference period

Supp

ly

Esu1

Energy supplied by batteries from 0 to 50 km/h

Average energy supplied by batteries in starting from 0 to 50 km/h (max speed)

Data from manufacturer or on the spot measurement kWh

Esu2

Energy required by diesel engine from 0 to 50 km/h

Average energy required by the engine in starting from 0 to 50 km/h (max speed)

Amount of fuel ( l or kg) required to operate from 0to 50 km/h multiplied per fuel calorific value and converted in kWh kWh

Esu3

Energy supplied by batteries at constant speed (50 km/h) Average energy supplied by batteries at a constant speed (50 km/h)

Data from manufacturer or on the spot measurement kWh/km

Esu4

Energy required by diesel engine at constant speed (50 km/h)

Average energy required by the engine to operate at a constant speed (50 km/h)

Amount of fuel ( l or kg) required to operate at a 50 km/h constant speed multiplied per fuel calorific value and converted in kWh kWh/km

Esu5 Recharging capacity Amount of e-vehicles recharged per charging facility counting vehicles/day recharging facility

Esu6 Rationalizing energy consumption

Comparison of energy consumption of a number of conventionally-fuelled vehicles and the same amount and type of e-vehicles operating in same way (time and mileage)

Measurements of fuel supplied to conventional vehicles and conversion in correspondent kWh divided by measurement of electric energy supplied to e-vehicles # 1000 km



Table 4 – KPIs for Evaluation Category: Environment Evaluation Category: Environment



Unit of measurement

Reference period

Con

cent

ratio

ns

Eco1 CO concentrations Average hourly (or peak/off-peak) of CO concentrations data from monitoring station mg/m3 peak/off peak hours (average working day)

Eco2 NOx concentrations Average hourly (or peak/off-peak) of NOx concentrations data from monitoring station mg/m3 peak/off peak hours (average working day)

Eco3 PM10 concentrations Average hourly (or peak/off-peak) of PM10 concentrations data from monitoring station µg/m3 peak/off peak hours (average working day)

Oth

er n

uisa

nce

Eno1 Noise exposure Amount of population exposed to traffic noise (day/night)

Population exposed, broken down into 5 different perception bands of Lday and Lnight: the perception is classified by five answer options, two negative, two positive and one neutral (absolutely dissatisfied, partly dissatisfied, absolutely satisfied, partly satisfied and neither satisfied nor dissatisfied. %


Eot1 Vibration Ground vibrations induced by bus traffic

Ratios of lateral and horizontal accelerations and main frequencies induced in the ground respectively by a conventional vehicle and a Eliptic demo vehicle; the measure are to be taken at 2 meter from the axis of the carriageway and on vehicles at full speed #


Eot2 EM Radiation Electromagnetic fields from fast-charging operations On the spot survey/measurement Tesla Eliptic demo timeframe



Table 4 – KPIs for Evaluation Category: Environment (cont.) Evaluation Category: Environment



Unit of measurement

Reference period

Emis

sion

s

Eem1 CO2 emissions Average emissions due to the Eliptic demos, distinguishing per vehicle category Modelling g/vkm


Eem2 CO average emission Average emissions due to the Eliptic demos, distinguishing per vehicle category Modelling g/vkm


Eem3 NOx average emission Average emissions due to the Eliptic demos, distinguishing per vehicle category Modelling g/vkm


Eem4 PM10 average emission Average emissions due to the Eliptic demos, distinguishing per vehicle category Modelling g/vkm


Was

te Ewa1 Hazardous waste Disposal of hazardous waste (fluid and/or solid) Reported kg/month


Ewa2 Non - hazardous waste Disposal of hazardous waste (fluid and/or solid) Reported kg/month Eliptic demo timeframe



Table 5 – KPIs for Evaluation Category: People Evaluation Category: People



Unit of measurement

Reference period

Pass

enge

rs

Ppa1 Awareness

Assessment of the passengers' awareness of the need to implement a given Eliptic measure

A specific questionnaire to be submitted to passengers provides a qualitative assessment on the awareness of passengers of the need to implement the tested measure according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %

Eliptic demo focus groups

Ppa2 Acceptance

Assessment of the passengers' acceptance of a given Eliptic measure

A specific questionnaire to be submitted to passengers provides a qualitative assessment on the acceptance of a given Eliptic measure according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %


Ppa3 Attractiveness

Passengers' perception of attractiveness of a given Eliptic measure

A specific questionnaire to be submitted to passengers provides a qualitative assessment for selected attractiveness issues (for instance , pleasure to travel by a greener mode ecc.) according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %


Ppa4 Travel comfort

Passengers' perception of travel comfort related to a given Eliptic measure

A specific questionnaire to be submitted to passengers provides a qualitative assessment for selected travel comfort issues (for instance , waiting time at bus stops, travel experience, travel time, ecc.) according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %


Ppa5 Noise perception

Passengers' perception of noise nuisance related to a given Eliptic measure

A specific questionnaire to be submitted to passengers provides a qualitative assessment of the perceived noise (for instance , on board and at bus stops) according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %




Table 5 – KPIs for Evaluation Category: People (cont.) Evaluation Category: People



Unit of measurement

Reference period

Driv

ers

Pdr1 Driving comfort

Drivers' perception of travel comfort related to a given Eliptic measure

A specific questionnaire to be submitted to drivers provides a qualitative assessment for selected travel comfort issues (for instance, braking operations, stopping at bus stops, ecc.) according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %


Pdr2 Acceptance

Assessment of the drivers' acceptance of a given Eliptic measure

A specific questionnaire to be submitted to drivers provides a qualitative assessment on the acceptance of a given Eliptic measure according to options measurable by a 10-point scale. The sum of each option is divided by the total amount of assessment and multiplied by 100 %


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 636012.


Annex 2 – List of Context Parameters Table 1 – Context parameters, Area of Investigation: Operations



Parameter reference

Operations

Vehicles

1 Fleet composition Unit 2 Operational vehicles Unit


sum of standing and seating places (standing place = 5 pax/sqm) vehicle

4 Total range km vehicle 5 Battery-only range km vehicle 6 Diesel-only range km vehicle 7 Vehicles operational time h day 8 Distance driven (route ) km day 9 Distance driven (total) km year

10 Distance driven (from depot to route) km day

11 Distance driven (from route to depot) km day

12 Distance driven in electric mode (to/from depot) km day

13 Distance driven in electric mode (route) km day

14 Commercial speed (route) km/h day 15 Empty mass kg vehicle

16 Vehicle mass (only seated pax) kg vehicle 17 Battery mass kg vehicle 18 Cells unit vehicle

19 Total mass (diesel + transmission + auxiliaries + electric motor) kg vehicle



Table 1 – Context parameters, Area of Investigation: Operations (cont.)



Parameter reference

Operations

20 Passenger mass kg (estimatated 70 kg per pax) Vehicle

Charge

21 Total daily time to recharge h vehicle

22 Daily time to recharge (route, fast chargers) h vehicle

23 Daily time to recharge (depot)

24 State of charge of the battery at the end of operations % vehicle

25 Charging operations events/operational time vehicle 26 Full charge kg vehicle


27 Allowed max speed km/h route

28 Maximum starting acceleration m/s2 vehicle

29 Mean acceleration 0 - 50 km/h m/s2 vehicle

30 Mean acceleration 0 - max speed m/s2 vehicle

31 Mean braking acceleration 50 km/h - 0 m/s2 vehicle

32 Mean braking acceleration max speed - 0 m/s2 vehicle

33 Acceleration space 0 - 50 km/h m vehicle

34 Acceleration space 0 - max speed m vehicle 35 Braking space 50 km/h - 0 m vehicle

36 Acceleration space max speed - 0 m vehicle



Table 2 – Context parameters, Area of Investigation: Sustainability


Parameter ID Parameter


Parameter reference

Sustainability

Power and energy

37 Diesel engine power kW vehicle 38 HVAC power kW vehicle 39 Other auxiliaries kW vehicle

40 Total energy stored in batteries kWh vehicle 41 Batteries nominal capacity Ah vehicle

42 Energy supplied from batteries 0 - max speed kWh vehicle

43 Energy supplied from batteries 0 - 50 km/h kWh vehicle

44 Recoverable energy from braking (batteries) kWh vehicle

45 Energy flash charging (10 seconds) kWh vehicle

46 Ratio between energy supplied and energy charged # vehicle

47 Ratio between energy supplied and energy charged # route

48

Total electric energy supplied from externals sources (catenary, public energy network, etc.) kWh 1 h

49 Total energy absorbed by diesel engine kWh/km route

50 Energy produced by diesel engine kWh/km route

51 Diagrams Traction - speed (electric)

vehicle



Table 2 – Context parameters, Area of Investigation: Sustainability


Parameter ID Parameter


Parameter reference

Sustainability

Power and energy

52 Diagrams Traction - speed (diesel engine)

vehicle

53 Diagrams total efficiency - speed (diesel engine)

vehicle

54 Diagrams total efficiency - speed (electric)

vehicle

55 Daily energy charged (fast chargers) kWh/day

56 Daily energy charged (depot) kWh/day charging facility

Table 3 – Context parameters, Area of Investigation: Context



Parameter reference

Context

Route

57 Route description narrative 58 Elevation diagram

route

59 Length km route 60 Bus stops # route

Environment 61 Ambient temperature °C daily average 62 Road conditions narrative

impacts evaluation plan - eliptic project d3.1 impacts... · figure 23 - cluster 1, common kpis for...

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