1

01ATT-213

How to improve hybrid vehicles for environmentalsustainability. A case study of their impact.

Antonio MATTUCCI, Mario CONTE, Giovanni PEDEENEA – Advanced Energy Technologies Division

Copyright © 2001 Society of Automotive Engineers, Inc

ABSTRACT

ENEA is studying innovative vehicles such as pureelectric and hybrid-electric vehicles, aiming to increasetheir energy efficiency and to reduce pollutantemissions. As part of such activities, a quite largedemonstration project for verifying technical andeconomical viability of hybrid buses under real publictransport operation conditions, has been carried out.Furthermore, a controller for an electric hybrid vehiclehas been developed that allows to achieve a further30% fuel economy improvement. The results of suchactivities are the input information of the TREMOVEmodel to investigate the potential impact of electrichybrid vehicle deployment in an Italian city, as a casestudy. The positive effects of new hybrid vehicleintroduction for freight transport in terms of fuel savingand emission reduction, have been also investigated.

INTRODUCTION

In the last 15 years ENEA, the Italian National Agencyfor New Technology, Energy and the Environment hasbeen conducting advanced research and demonstrationprograms to reduce the adverse impact of thetransportation on the environment and the energyconsumption. New transport systems and trafficmanagement tools are being developed and tested toreduce polluting emissions, energy consumption andtraffic congestion in major Italian urban areas wherepoor air quality levels are a daily occurrence. Amongothers, focus has been directed on: electric vehicles ofvarious types (battery powered, hybrid and with fuel cellgenerator) characterized by the introduction ofinnovative components (batteries, fuel cells,supercapacitors and control systems); dedicated largefacilities [10, 14] able to test innovative vehicles andsubsystems used in European research projects(MATADOR, SCOPE) under different operatingconditions; models to verify the design and theeffectiveness of control system of innovative vehiclesand to evaluate their performances [15]; andquantitative analysis of the overall impact resulting froma large scale deployment of innovative vehicles.

The main framework for ENEA activities was a ProgramAgreement with the Italian Ministry of Industry. Underthis Program, specific testing facilities and technologieshave been developed to verify and improve hybridvehicle performances under controlled conditions (inlaboratory) and quite a large fleet of 24 hybrid buseshas been tested under real operating conditions.

In this paper is described the research work carried outat ENEA to develop a controller, which would improvethe energy consumption and the pollutant emissions ofhybrid vehicles. The impact of using such a controller onhybrid vehicles in a city such as Milan has beenevaluated by means of the TREMOVE model, a toolable to simulate the transport policy measures in theselected domain to have a quantitative forecast ofbenefits and drawbacks.

ENEA EXPERIENCE ON HYBRID VEHICLEFLEETS

In the last years, ENEA has sponsored the introductionof series hybrid vehicles and has provided three localpublic transport companies with 24 hybrid busesproduced by the Italian Company IVECO. The hybridbuses were developed by ALTRA (a research companyowned by IVECO and ANSALDO RICERCHE) andrealized in two versions, a 12 meter and a 6 meter city-bus, both named AltroBus. The 12m city-bus is amodified version of the 490-diesel bus, in which a serieshybrid drivetrain has been installed. A small dieselengine powers an electric generator (30 kW DC outputpower at 600 V) able to charge the lead-acid tractionbattery pack (with a nominal voltage of 600 V, a storagecapacity of 100 Ah, and an overall weight exceeding 2.2tons) and, jointly with it, to power the traction electricmotor (AC asynchronous 3-phase with an output powerof 164 kW @ 1500 rpm). The bus has a gross weight of19 tons and its overall range is about 250 km with about20-30 km in pure electric mode.

Three cities have been selected for the demonstration:a large city such as Rome, and two medium-size cities,Ferrara (flat land) and Terni (hilly city).

The fleet of hybrid buses has been divided according tothe peculiar needs and features of the local public

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transport company: 12 in Rome, 8 in Ferrara and 4 inTerni where the demonstration was also financed by theEU THERMIE Programme (FLEETS Project) [12]. In alldemonstrations, conventional and hybrid vehicles wereused in regular public transport service. All vehicleswere provided with dedicated real-time data acquisitionsystems, in order to fully monitor the on-the-road busbehaviour in any working and weather conditions. Theexperimental campaign lasted about one year.

Table 1 compares the average values of specificemissions of hybrid and diesel buses in Terni. Basically,the hybrid bus fleet demonstration showed significantlypositive effects in terms of polluting emission reductionbut negligible effects for fuel consumption. Therefore,only a limited interest towards the diffusion of such atechnology could be foreseen, especially considering thesignificant increase of vehicle costs. However, there is alikelihood of significant improvements in hybridtechnology achievable with the introduction of newcomponents and a more efficient control strategy, whichwould increase the appeal for such technology, asshown in the following paragraphs.

Table 1 - Hybrid and conventional bus on-the-roadaverage specific emissions

VehicleFuel

Consumption(L/km)

CO specificemissions

(g/km)

VOC specificemissions

(g/km)

NOx specificemissions

(g/km)

EURO IIBus

0.437 5.05 0.82 24.92

SeriesHybrid Bus

0.41 0.3 0.59 11.55

HYBRID VEHICLE AND FUEL ECONOMY

Technical works on hybrid vehicles [16] demonstratethat the energy consumption is strongly correlated to thepower management strategy. Therefore, in order tomaximize the vehicle efficiency, battery losses and DCSource specific consumption have to be considered.Furthermore, the knowledge of the state-of-charge(SOC) of the accumulator is of great importance in thesystem controller implementation. Therefore, in theframework of the collaboration between ENEA andUniversity of Pisa, a controller for an electric hybridvehicle, that takes in account such parameters toimprove fuel economy, has been developed. Thecontroller is installed on a purpose-designed Light DutyVehicle, whose hybrid driveline is the same as the 6-mAltroBus. The most important specifications of thehybrid bus driveline are provided in Table 2. The hybridvehicle was not originally provided of an automatic DCsource power control system and its generation systemwas directly controlled by the driver accordingly to thespecific mission of the bus, on the basis of a “on-off”duty cycle.

To acquire information on the vehicle behaviour as astarting point for the controller development, severalvehicle experimental tests were programmed at ENEAlaboratories. Such tests have been performedsimulating the driving cycle of the vehicle on the basis ofthe ECE 15 urban cycle, which corresponds to thepower profile reported in Fig. 1.

Table 2 – Characteristics of 6m AltroBus hybrid vehicledrive-line

Internal Combustion Engine Electric Propulsion SystemFuel Diesel oil Type Separately

Excited DC MotorDisplacement 1204 cc Rating Armature

Voltage192 V

Maximum Power(@3600 RPM)

24 kW

Rating speed 2200 RPM Storage SystemRegulation mechanical Type Lead Acid Battery

Synchronous PM Machine Cells 96Rating Power(@ 2200 RPM)

10 kW Capacity (C5) 100 Ah

Rating Voltage 220 V

The aim of the tests was to determine the specific dieselfuel consumption of such hybrid vehicle as a function ofthe battery SOC and the generator power level.

The test confirmed the results of the fleet demonstrationexperience, i.e. the energy consumption is stronglycorrelated to the power management strategy, as shownin Fig. 2. The diesel fuel specific consumption rangedfrom 16.6 L/100km to 21.7 L/100km with an averagevalue of 18.5 L/100 km. The SOC optimum value wasfound to be around 40%. Larger values did not allowhigher fuel economy and lower values were insufficientto drive the vehicle accordingly to the ECE 15 cycle.

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- 5

10

15

20

25

6 kW 7 kW 8 kW 9 kW Generator Power

liter

s/100

km

�������������� S.O.C. 85%

S.O.C. 65%

-30 -20 -10

0 10 20 30 40

0 50 100 150

seconds kW

195

Fig. 1 - ECE 15 power profile

Fig. 2 - Hybrid vehicle fuel consumption

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THE DESIGN OF THE CONTROL SYSTEM

The main objective of the control system of an electrichybrid vehicle is to provide the power required by thepropulsion system while keeping fuel consumption andvehicle emissions as low as possible. Thus, theobjective of the control should be the overallminimization of fuel consumption and pollutantemissions.

The hybrid vehicle (IVECO Daily truck) layout is shown inFig. 3.

The system qualitative power flows are shown in Fig. 4[1]. The DC source power operates on an on-off cycle atconstant power. However the power setpoint can bechanged “on line” in a narrow range, ± 20-25%, accordingto the variations of the control signal Ps*. The battery actsas a power filter, i.e., the DC source delivers only the“average” power requested by the electric drive, and thispower can be, for example, averaged every 5-10 min..

From Fig. 4 it is also seen that the requested power to bedelivered by the DC source is much more constant thanPd(t). To optimize the operation, the controller logic needsto know the future system load, i.e., the future behaviourof the power demand, at least approximately. Of coursethe controller does not need a precise forecast of the Pd(t)evolution, but only of its global behaviour; therefore anestimation such as an average value Pd* of Pd(t) in agiven forecast interval time, is sufficient.

For the load prediction, the forecast algorithm can useall the available information such as:

� the previous values of Pd(t);� other information on the trip route (i.e. road

slope, traffic conditions, etc.).

The two control variables are the two signals enteringthe DC source, i.e., the ON/OFF signal and the averagerequested DC source output power Ps*(t).

The DC source (see Fig. 3) includes the GenerationSystem and the Battery Charger to convert the alternatecurrent to direct current. Considering different ICErotational speeds, the DC source specific consumptionhas a qualitative behaviour of the type depicted in Fig. 5(upper, black curves).

If, for each Ps, the optimal speed is considered, thevariable-speed specific consumption curve becomesthe envelope of minimum points of all the consideredcurves, i.e., the lower (red) curve in the figure. It istherefore convenient to operate the ICE so that it canfollow the red curve, by varying the ICE rotating speedin an interval where the efficiency remains high,whenever the requested power Ps*(t) is to be provided.

BATTERY MODEL AND ENERGY LIMITS

To define the vehicle control strategy it is important tohave a reference model for the lead-acid battery thattakes into account properly the SOC variations. Tosimulate the battery, the simple electrical circuitrepresented in Fig. 6, can be considered.

It is to be noted that both the internal resistance Ri andthe electromotive force Eq are not constant during thecharge/discharge processes but depend on the state ofcharge SOC and the electrolyte temperature θ.However, for sake of simplicity, linear dependencies ofthe above-mentioned variables can be considered, asshown in the same figure where Ri and Eq are plottedversus SOC.

DC Source Electric Drive

Battery Storage

P (t) d

P* (t) s

ON OFF

P (t) s

P (t) b

ON/OFF

��

����Brake

Accelerator

ICE ACGenerator

BatteryPack

Charger ChopperDC/DC

Controller

DCMotor

Fuel Wheel

Wheel

Gear

P=K

Battery

Generation System

Electric Propulsion System

++Eq(SOC,θ)

Eg

Ri(SOC,θ)A

B

Eq

Ri

SOCmin SOCmax0

1

Fig. 3 - Drive-train System layout of Daily truck

Fig. 5 - DC Source behaviour

Fig. 4 - Qualitative power flow in the system

Fig. 6 - The battery equivalent circuit

Ps

rpm

= variable-speed, minimum-consumption curve

Spec

ific

Con

sum

ptio

n

4

Such model is utilized for SOC on-line estimate andprovides an indication of the energy stored in thebattery. During normal operation, the battery is operatedso that the SOC remains constrained within two limits,later called as SOCmin and SOCmax. In particular:

� SOCmin is determined on the basis of the powerdeliverable by the batteries, which decreaseswhenever the SOC decreases; therefore SOCmin

is to be chosen with the requirement that thebattery is able to deliver the peak power requiredby the driveline under the worst conditions;

� SOCmax is determined on the basis that, at themaximum SOC, the VAB voltage does not passthe gas evolution threshold in case of peakpower to be absorbed.

CONTROLLER DESCRIPTION

A low cost controller prototype (10-20 $) has beendeveloped at ENEA and installed in the cockpit of thehybrid vehicle IVECO Daily truck (see Figure 7). Thedevice contains a power supply, sensors, relays, etc..At the present a limited attention has been paid to thedimension of the device but a future industrializedversion would certainly be much smaller and cheaper.To be able to follow the transients of an electric vehicle,the controller is required to perform all the computationsneeded in a short time. A sampling time of 200 ms hasbeen estimated sufficient.

The present project plan is focussed on developing thecontrol algorithm. The plan foresees that the algorithmcan be developed and tested in a step by step way. Atthe moment, the vehicle uses the simplest version ofthe SOC controller, so there is no load forecasting andthe setting of the power generator is of the “off-line”kind, selected by the driver. The driver also chooses thesetting for the initial SOC and its range. The algorithmoperates so that the SOC is maintained within thedesired range.

TEST CAMPAIGN

The vehicle (see Fig. 7) was tested on the dynamic twin-roll electric dynamometer. Besides the benchequipment, the experimental facility consists of a set ofinstruments to measure electric parameters such asvoltage, current and amount of electric charge, and of aprecision equipment for fuel consumption measurement.

The test campaign was performed according toEuropean, American and Japanese urban drivingcycles. In this way, the fuel consumption results couldbe easily compared to the available testing results fromall the above driving cycles. The main test results aresummarized in Table 3.

Table 3 - Main results of the test campaign

Driving cycleMain parameters

Unit EuropeanCycle

(ECE/NEDC)

JapaneseCycle(1015)

AmericanCycle

(UDDS)

Driven distance Km 28.89 19.80 26.96

Total time Hours 1h 34’ 1h 15’ 1h 11’

Average traction power W 7251 7370 9140

Total traction energy Wh 11384 9212 10785

Batterycharge/depletion

W 7251 7370 5.80

Ah -1.05 -0.84 4.06

Wh -201.60 -161.28 779.52∆SOC(depletion is positive)

Wh/km -6.98 -8.15 28.91

kg/h 2.54 2.46 2.68

Total kg 3.99 3.08 3.16

Wh 46765 36060 37085Fuel oil consumption

Wh/km 1618.74 1821.26 1375.59

Equivalent fuelconsumption

L/100km

15.7 17.5 12.5

The Urban Dynamometer Driving Schedule (UDDS) isfound to be enough severe in terms of average energyusage to force the hybrid vehicle to a "charge depletingmode" instead of the conventional "charge sustainingmode".

FUEL ECONOMY CONSIDERATIONS

The influence of driving cycles on the energyconsumption is clearly shown by the SOC variations.

The SOC is maintained in a range of about 10% ofnominal capacity by the ENEA controller in any test withthe exception of the UDDS cycle where the final SOC iswell below its initial value. The reason is that, in the firstpart of the test, there is a net SOC depletion, becausethe generator is kept in the "off" condition till the SOCgets the lower limit of its range, and the vehicle behaveslike a "pure electric" vehicle. The consequence is thatthere is a battery discharge and the SOC cannot regainits initial level at the end of the mission.

However, among all cycles tested, the best fuelefficiency performance was obtained with the samecycle, as the American UDDS average power is thenearest (-2%) to the one provided by the IVECO Dailytruck generator. The UDDS cycle is therefore the onethat is better interpreted by the dimensioning of the

Fig. 7 - The ENEA hybrid vehicle

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generation system (on a single, specific cycle) that wasbased on the following condition:

Generator power = Average traction power (1)

From the SOC diagram in Fig. 8, which results from theAmerican cycle tests and oscillates around 74 %, it canbe seen that the battery-motor power flows are onlycorrelated with the variation of the traction power duringthe mission. In simpler words, there is no net batterycharging or discharging after the initial phase. Thepositive consequence is that the SOC variation range isself-reduced, without any diesel engine starting andstopping.

As a conclusion, a reduction of the fuel consumptioncan be foreseen by implementing the control systeminstalled on the vehicle with a periodic load followingadditional function. The next version of the controlleralgorithm will include a periodical check of the condition(1) to adjust the generator power set to the cruisecondition.

The load following could create some difficulties interms of allowing the generator engine to work within thedomain of efficient operation, especially if such adomain is not sufficiently extended and/or the controlalgorithm is not enough sophisticated to utilizecompletely the optimal domain. It must be consideredthat, to obtain on the European cycle the samebehaviour of the American cycle, the thermal enginewould have to be set up to work at reduced power of 7kW, but with a specific consumption equal to the oneachieved at the highest power (9 kW) of the Americancycle. Hence the necessity of a more sophisticatedgeneration group management, which has to control notonly the load, but also the rotational speed of thegenerator thermal engine.

In conclusion, the improvements that can be achievedunder such assumptions for the European cycle can bederived through a simulation [1,2], that shows aconsumption of 13 L/100 km, with the same hardwareand batteries. Another way to achieve similar resultscan be followed, by using an energetic analysis. To thisend it is useful the definition of efficiency ηηηη:

η = (η = (η = (η = (total traction energy) /(equivalent fuel consumption) (2)

As a matter of fact (see Table 3), the equivalent fuelconsumption during the European cycle was 11384 Wh,the total generated energy 46765 Wh, the efficiency :

ηηηηECE = 24 %

During the UDDS, the total traction was 10785 Wh, theequivalent fuel consumption 37864 Wh (alsoconsidering the SOC depletion), and the efficiency :

ηηηηUDDS = = 29 %

With the same efficiency, the equivalent fuelconsumption in the European cycle would be :

11384/0.29 = 39255 Wh

instead of 46765 Wh, corresponding to 3.27 kg (or 3.89L). Considering the total driven distance of 28.9 km, theequivalent fuel consumption would be reduced to 13.5L/100 km, vs. 15.7 L/100 km of the conventional typeone (-14 %). This result corresponds to the onecalculated for the Mitsubishi HEV, whose efficiency, onthe Japanese urban cycle, is expected to improve fromthe 9.2% for current vehicle, to a 14.3% for the HEV [3].

Fuel economy could increase even more and aconsumption less than 30%-40%, respect to theconventional vehicle, could be achieved if a dieselengine, with a specific fuel consumption comparable tothe 2.5 litres DI diesel used on the conventional version(210-220 g/kWh vs. 270 g/kWh of the IDI used on thehybrid), and better batteries (ηch/dsch = 0.8 vs. 0.7) wereintroduced on the hybrid.

HYBRID VEHICLE DEPLOYMENT CONTEXT

After having demonstrated that the hybrid vehicletechnology is useful, the focus is to be addressed on theimpact of such technology on a large-scale applicationin order to substitute the old internal combustion enginevehicles thus avoiding or reducing their related noxiouseffects. Of course this implies that a significant marketshare, although at a niche level, could be associated tothe hybrid technology deployment in a way that itseffects can fully detected.

With this in mind, the first issue is to identify a correctfield of application for hybrid vehicles with theconstraints that the experience effects should bedetectable at a global level. To this end, a rapid answercan be provided by restricting the application to theurban domains. This has relevant advantages: allows tobetter monitor the results of the experiences; reducesthe investments to be made; is of general applicabilityas the experience can be easily transferred to otherurban domains; and does not require the deployment ofmany infrastructures.

S.O.C.

69

74

79

84

89

36300 36800 37300 37800 38300 38800 39300 39800 40300

seconds

%

Fig. 8 - SOC trend during UDDS cycle

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THE FRAMEWORK OF THE STUDY

The city of Milan was selected as framework, since ithas been considered the reference Italian city underAUTOIL II Programme carried out by the EuropeanCommission, together with the car makers, the oilindustries and the Member States. The scope of AOP II,which was completed in the year 2000, was to providean assessment of the most effective policies for thetransport sector (mainly for the road transportation), toimprove the air quality level by reducing theconcentration of noxious pollutants. Air quality targetshave been defined by the Air Framework Directive(96/62/EC) and its related Daughter Directives enforcedover the entire European Union territory.

Under AOP II, sophisticated simulation tools have beendeveloped. They allow to make a quantitativeassessment of the provisions and to check that theforecasted pollutant concentrations are not providingdamages to the human health and to the ecosystem ingeneral. Besides, they allow the selection of the mosteffective provisions, i.e. the ones at the minimum cost.Among such tools is the TREMOVE model [4,5,6] thatgives the forecast of the transport policy effects in termsof vehicle emissions and costs of the transport, thatdepend on the mode under consideration, the cost ofthe travel time, and other elements such as taxes andincentives on vehicles, fuels, etc..

Among the polluting substances taken intoconsideration by the model are carbon monoxide (CO),nitrogen oxides (NOx), volatile organic compounds(VOC), benzene (C6H6), particulate (PM10), sulphurousdioxide (SO2), methane (CH4), carbon dioxide (CO2),other greenhouse gases, etc. The list of substancesincludes all the pollutants considered by COPERTmethodology [7, 8], which is embedded into TREMOVE.The model needs as input information the provisions tobe studied and predicts the transport behaviour for aselected time interval. The tool begins its processingfrom an aggregated level of the transport demand forpeople and goods and determines how the policieswould act on such a demand. The transport demand issplit among the available transport modes (road,railway, ship) and the related public and privatecategories (e.g. for the private road transport modemany categories exist, such as different typologies ofcars -further subdivided on the basis of fuel and enginesize-, of motorcycles, etc.). The modal split is carriedout by calculating the cost of each transport mode andselecting each modal share on the basis of its marginalcost.

The following step is the evaluation of the resultingtraffic conditions, i.e. the speed and the load factors forthe vehicles considered, through a simplified congestionfunction. The transport demand is an important item forthe forecast of the vehicle fleet, which also depends onsome specific provisions (i.e. scrappage policies,selective taxation, etc.). The forecasts of vehicle fleetcomposition and usage, together with the vehicle speed,are the parameters that allow the calculation ofemissions and fuel consumption. The fuel consumption,together with the vehicle fleet composition, is then usedfor a new iteration until the convergence criteria aremet.

THE TECHNICAL AND TERRITORIAL CONTEXT

The next issue is related to which type of hybrid vehiclesare suitable for a large-scale experience and how toproceed. Also for this item an answer can be providedby looking at the field of the urban freight transport.Presently, goods are delivered in the urban contextmainly by Light Duty Vehicles, fuelled both by gasolineand by diesel. This is a consequence of the structure ofItalian towns that are characterized by narrow roads andthe presence of historical buildings. Therefore, the useof Heavy Duty Vehicles is very difficult for accessibilityand environmental reasons. However, some otherinsights are required in order to consider if the proposalto use series hybrid LDVs for freight transport istechnically sound and is applicable to large-scale urbandeployment. To this end, it is useful to overview theItalian situation. In Italy [9], the main share of the freighttransport (about 70% in the year 1998) is covered byroad transportation. This includes both urban and extra-urban transport. Another important item is the nationalroad vehicle fleet, whose composition in thousands ofvehicles at the end of the year 1999 [11] is shown in Fig.9. It can be seen that LDVs correspond to about twomillions of vehicles, of which 1.8 millions use diesel fueland the remaining ones gasoline. This corresponds to ashare of some more than 5% of the Italian fleet. Databelonging to Milan province show that, at the samedate, the LDVs registered in the province were about142 thousands.

Of course, this does not allow to consider that the onlyLDVs travelling through the city of Milan are the onesabove indicated, but gives an idea on what is thenumber of vehicles to be taken into account for thedeployment analysis. The above mentioned data onfleet composition confirm the idea that looking at LVDsfor their introduction on a niche market such as the oneof urban freight delivery, can be viable as the amount ofvehicles to be replaced is not very high.

On the other hand it is important to evaluate if theapplication of such vehicles in an urban area may havedetectable effects by providing sensible reduction of theurban environment pollution. An answer to this questionis given in Fig. 10, where the impact of LDVs for thecity of Milan is shown. The data are derived by the AOPII Italian reference scenario and are related to the year2000.

Fig. 9- Composition of Italian vehicle fleet (end of year 1999)

26386

4132

1511

2186

654

86 3400

Gasoline Cars

Diesel Cars

LPG Cars

Ligh Duty Vehicles

Heavy Duty Vehicles

Bus

Motorcycles & Mopeds

7

From the diagram it is clear that LDVs contributesignificantly to all main road transport pollutants. Inparticular, the shares of PM10 and SO2 are very high,respectively of 29 and 14%.

Thus the questions are: is it feasible to build hybridLDVs and do they allow reductions of the road transportimpact? Some of the answers have been alreadyprovided, but some other considerations can be drawn.

From the technical point of hybrid LDVs can bedesigned more easily than passenger cars, as they canbe more flexible in order to allocate the volume requiredby the additional equipment (electric motor, batteries,etc.).

However, to evaluate their real effectiveness, it isnecessary to compute their specific emissionsaccording to COPERT. It has to be underlined thatCOPERT correlations are based on the informationcoming from the tests of existing vehicles and therefore,strictly speaking, they are unable to predict thebehaviour of future vehicles. To overcome this problemin EURO III and EURO IV standards (applicable to theICE vehicles on the market as of 2001 and of the 2005respectively), the related emissions are calculatedadopting some reduction coefficients, applied to theemission values of the equivalent EURO I vehicles. It isalso necessary to observe that the reduction coefficientsshould have been determined by adopting conservativecriteria; therefore, whenever such vehicles were widelyavailable on the market, emission values equal orsmaller than those obtainable from the calculation couldbe observed for them.

Similar considerations can be applied to compute thehybrid vehicle specific emissions. Additional reductioncoefficients can be considered in order to reduce theforecasted vehicle emissions, whenever they aredeployed in a significant share. It is easy to justify thisas the hybrid vehicles would have the same engine ofICE vehicles (Euro III and IV), but working in a morecontrolled domain where a better efficiency can beachieved. In particular this assumption holds, wheneverthe driving conditions change rapidly and stop and gosituations are frequent. Even in the off-peak hours, thepresence on the road of pedestrians, traffic lights,junctions, vehicles searching for parking, etc. requirecontinuous adjustments of the vehicle speed. Therefore,in urban areas several accelerations and slowdowns arerequired, causing the engines of the presently availablevehicles not to operate at their most efficient workingpoint. This could be avoided or at least reduced in ahybrid vehicle with the help of a control system thatconstrains the thermal engine to work in a very efficientinterval, so providing significant improvements ofspecific consumption and emissions.

The problem is now to determine the reduction factorsto consider. To this end, the indications coming fromfleet tests made in Italy (see Table 1) are very valuable[12,13]. The large improvement on the field specificemissions of the hybrid bus justifies to considerconsistent emission reduction factors. In practice,values such as the ratio of the measures shown inTable 1 can be assumed. Furthermore, the hybridbuses tested in Terni were not provided of any controlsystem while such option is considered for this analysis,

thus increasing the engine efficiency and reducing theemissions. Therefore the same reduction factors usedfor HDVs can be considered for the LDVs emissions.For gasoline hybrid LDVs no field data are available;therefore it is safer to use higher factors as they act inthe conservative direction. Looking at fuel consumption,the previous analyses show that there are consistentmargins to increase the efficiency. However, to be in theconservative side also in this case, a fuel consumptionreduction of only 10% has been considered for anyLDVs. The consumption and emission reduction factorsas reported in Table 4.

Table 4- Hybrid LDVs reduction factors

Indicator Reduction factor

Gasoline LDV Diesel LDV

CO emission 0.7 0.1

NOx emission 0.7 0.5

VOC emission 0.7 0.7

PM emission 1.0 0.6

Fuel consumption 0.9 0.9

The use of the above factors allows the computation ofhybrid vehicle specific emissions as a function of theaverage speed according to COPERT approach. Tohave an idea of the speed behaviour the histograms inFig. 11 and 12 are provided, respectively for fuelconsumption and CO specific emission.

It is interesting to observe that in the fuel consumptionhistogram there is an increase of fuel values for EUROIV vehicles, as the new targets on pollutant emissionhave required the introduction of additional devices thatlower the vehicle fuel efficiency.

THE HYBRID LDVS SCENARIOS

It is now possible to evaluate the effect of theintroduction of LDVs in the city of Milan. The mainhypothesis is that from year 2002 a growing portion ofthe new LDVs, purchased in the city, is based on theseries hybrid technology. In the scenario it is assumedthat the hybrid LDV share would be 20% for year 2002,55% for year 2003 and 100% for year 2004 and the

Fig. 10Impact of LDV in urban areas (year 2000)

8.6%

10.4%

0.7%

28.8%

6.5%

6.4%

6.5%

3.5%

13.8%

4.3%

10.4%

CO

FC

NOx

PM

C6H6

VOC

NMVOC

CH4

SO2

N2O

CO2

8

following years. Although the introduction of the hybrid

vehicles could appear too fast, such time interval allowsboth the industry to be able to provide the vehicles andthe users to become acquainted with the newtechnology. In particular for the year 2002, only 2000hybrid LDVs are required and this could be handled bythe industry. On the other hand, it is to be stressed that,to provide consistent benefits, the new technologyneeds to replace a considerable share of the entire fleetand this can be achieved only if there is a strong boosttoward the substitution of the scrapped vehicles with theinnovative ones.

The emission reduction in the city of Milan, evaluatedthrough the use of TREMOVE is summarized in Fig. 13.The main benefit of introducing hybrid LDVs would beon the particulate reduction, whose emission is cut ofabout 5%. In fact LDVs are responsible for a high shareof this pollutant and therefore the substitution of old anddirty vehicles with the hybrid ones plays an importantrole. In any case for all the indicators there is a growingbenefits that stabilizes at year 2008, whenever almostall of the oldest vehicles have been replaced.

The effect of the substitution of conventional LDVs withhybrid ones can be also investigated in the direction ofincreasing the delivery efficiency by creating anassistance framework that can co-ordinate the freighttransport. This could be achieved by providing the citywith a logistic system that is able to assign the loads todifferent vehicles to optimize the goods delivery. Theavailability of such a logistic system could provideconsistent savings in terms of total mileage, travel time,fuel consumption, etc.. Presently telematics technologyhas the full capability to help consistently the freighttransport in this direction. The assistance can beperformed both at the trip planning phase by creating

the optimal load filling for the LDVs fleet, and at thedelivery phase where, for instance, indications on thebest routes could be provided to each vehicle in realtime.

Of course it is not easy to simulate such a scenario, thathas been analyzed only by increasing the average loadof the vehicles. The results are provided in fig. 14,where it can be seen that the benefits in terms ofemission and consumption reduction are very relevant.Such feature can be also applied to conventionalvehicles, but it could be implemented in an easier wayon the hybrid vehicles, as they could be designed takinginto account such requirement, in order to make themfully compatible with the logistic system.

The increase of vehicle load factor implies as naturalconsequence the reduction of the total LDVs annualmileage. Therefore in the urban area twoenvironmentally positive effects will be seen, i.e. thedirect effect provided by LDVs and an indirect effect bythe other vehicles. By reducing the number of LDVsvehicles on the road, there will be better trafficconditions with an increase of the average speed. Thiseffect will be beneficial as shown by COPERTcorrelations that generally provide lower emissions athigher speed.

This scenario clearly shows that the vehicle technologyimprovement could be much more effective if othersynergic provisions are added. To this end, there is awide variety of provisions that could be taken intoconsideration. Among them there are road pricing,scrappage incentives, etc. Another interesting item to beconsidered is that through the selective introduction ofhybrid vehicles, the citizens will become familiar with thetechnology and other important markets could be

Fig. 13Impact of series hybrid LDV in Milan

-6.00%

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

2002 2003 2004 2005 2006 2007 2008 2009 2010Years

Em

issi

on R

educ

tion

(%

)

CO

FC

NOx

PM

VOC

CO2

Fig. 14Impact of LDVs load increase

-18.00%

-16.00%

-14.00%

-12.00%

-10.00%

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2002 2003 2004 2005 2006 2007 2008 2009 2010

years

Red

uct

ion

(%

)

CO Milano

FC Milano

Nox Milano

PM Milano

C6H6 Milano

VOC Milano

CO2 Milano

Fig. 11Fuel consumption specific emissions for Light Duty vehicles

0

20

40

60

80

100

120

140

160

180

10 15 20 25 30 35 40 45 50 55 60 65 70

Vehicle average speed (km/h)

g/km

ConventionalGasoline LDV

Euro IV GasolineLDV

Hybrid Euro IVGasoline LDV

ConventionalDiesel LDV

Euro IV DieselLDV

Hybrid Euro IVGasoline LDV

Fig. 12CO specific emissions for Light Duty vehicles

0

5

10

15

20

25

30

35

40

45

50

10 15 20 25 30 35 40 45 50 55 60 65 70

Vehicle average speed (km/h)

g/km

ConventionalGasoline LDV

Hybrid Euro IVGasoline LDV

Euro IV GasolineLDV

ConventionalDiesel LDV

Euro IV DieselLDV

Hybrid Euro IVGasoline LDV

9

opened to it, i.e. buses, passenger cars, heavy dutyvehicles, etc.

To have an idea of the additional costs connected to thehybrid LDVs deployment it is easy to understand that themajor impact will be in the first years, where scaleeconomies on vehicle production are very difficult to bemet. After this initial period the LDVs cost will decreaseas a consequence of the significant industrialproduction of such means. Considering the time intervalfrom year 2002 to 2005 in Milan under the scenariohypotheses, about 29 thousand hybrid LDVs would bedeployed in Milan. Therefore, considering an extra-costof about 30% for each LDV and an average cost ofabout 20 kEuro the additional cost will be of about 174millions of Euro. It is to be underlined that the totalinvestment is distributed in a period of time of four yearsand, for the first year, the extra-cost is 12 millions ofEuros, as only 2000 hybrid LDVs are deployed.

The total extra-cost is quite high but can be afforded,especially if the target city for a practical hybrid LDVsdeployment would be a smaller city. In such a case theinvestments would be dramatically reduced.

CONCLUSIONS

In this paper the viability of the series hybrid vehicleshas been considered in order to understand what arethe benefits that can derive by their use. Laboratoryexperiences carried out at ENEA Casaccia and fleetexperiences demonstrated that high emission and fuelconsumption reductions can be obtained with respect toconventional vehicles. This can be achieved if someimprovements are added to the present vehicles. Inparticular the development of an automatic controldevice has been carried out for a prototype hybridvehicle.

To this end, a campaign of tests on the vehicle withoutthe controller has given the reference information inorder to evaluate the possible benefits of the controlsystem. In particular, an average consumption of 18.5L/100 km has been measured. This means that anequivalent conventional vehicle, having a consumptionof 16 L/100 km, is better of the uncontrolled hybrid.

The second campaign of test, with the controllerinstalled on the vehicle, has improved the situation withthe consumption of the hybrid being similar to the bestone of the values found in the first set of tests. In thiscampaign better specific consumption has been foundespecially on the American cycle, that represented theheaviest cycle considered.

A computer simulation and an energetic analysis haveshown that the load following implementation on thecontrol system allows to reach a specific consumptionof 13.5 L/100 km versus 15.7 L/100 km of theconventional version (-14%). If a diesel engine, withspecific fuel consumption comparable to the 2.5 litres DIdiesel used on the conventional version and betterbatteries, should be introduced on the hybrid, fueleconomy could increase even more.

Starting from these results, the possible impact of aquite large-scale hybrid LDVs deployment has beenanalysed for the city of Milan. The driving criterion

adopted for the choice of Milan was the availability ofalready configured forecast tools and high quality data.This has contributed positively in speeding up theanalysis and has also made possible to compare theresults with a reference scenario, i.e. the “business asusual” reference scenario created under AOP II.Besides Milan indications can quite easily be transferredto any other city, where the deployment of hybrid LDVsis considered useful. The results have shown that thehybrid technology can give a positive contributionespecially for the reduction of harmful pollutants, as theeffect on the fuel was minimized by the use of veryconservative factors. In particular the positive effects arestrengthened by adding some non-technical measure tothe hybrid vehicle deployment. To this end the onlyhybrid LDVs introduction shows that the mainimprovement is a reduction of 5% of PM, while, if somelogistic measures are also added, almost all theindicators show positive effects.

However, the paper has not addressed some importantaspects related to the introduction of the newtechnologies into the market. Up to now the main effortsin this direction have been concentrated on promotingexperiences, where fleet tests of a few innovativevehicles have been carried out. Of course this is animportant step, but other very important issues such aslegislation, lack of infrastructures, user information, etc.have been neglected.

Presently this is one of the most important reasons forthe poor presence of the innovative vehicles in theoverall fleet, although relevant investments have beenmade in the last years.

In conclusion, the analysis has shown that thetechnology can push for the improvement of theenvironment and for fuel consumption reduction, but byitself it is unable to reach all the targets and can benefitof other provisions.

In fact, the transport problems are to be solved byconsidering that three main items, each otherinteracting, are to be considered: the vehicles, theinfrastructures and the users. Therefore the transportsolutions must be always found considering all thesethree items and this rule is to be considered also for thehybrid technology deployment. In any case the hybridtechnology can contribute to provide viable, durable andsustainable solutions to the transport problems.

ACKNOWLEDGMENTS

The authors are very thankful to Stefano Passerini forthe useful comments provided.

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ACRONYMS

ICE: Internal Combustion Engine

DC: Direct Current

AC: Alternate current

SOC State Of Charge

HEV: Hybrid Electric Vehicle

DI: Direct Injection

IDI: In-Direct Injection

HDV: Heavy Duty Vehicle

LDV: Light Duty Vehicle

AOP: Auto-Oil Programme