modelling methodologies for studying the effects of energy … · 2017-09-26 · • open standard...

Copyright © Claytex Services Limited 2017

Alessandro Picarelli Simon Robinson

[email protected] [email protected]

Modelling Methodologies for Studying the

Effects of Energy Efficient Glazing on Cabin

Thermal Energy and Vehicle Efficiency

Simulation and Modelling 2017


Introduction

• Based on the work:

– Gravelle et al. A multi-domain thermo-fluid approach to optimising HVAC systems,

IMA 2014, Bristol

– Gravelle et al. Implementation of energy efficient cabin technologies to improve

overall vehicle efficiency SVT 15, University of Warwick/LCV 2015

– Gravelle et al. Modelling the effects of energy efficient glazing on cabin thermal

energy & vehicle efficiency Modelica Conference 2015, Versailles, France

• This presentation discusses:

I. Acausal model requirements to facilitate systems integration and architecture

design

II. Methods for exporting models (incl. FMI) to HiL rigs and the specific benefits

derived from this

• This presentation references:

– Requirements expressed in previous projects undertaken with JLR Research

– Analysis activities & results which have permitted flexibility & effectiveness in

system-development


Part I

Requirements & Modelling Approaches


Modelling requirements and benefits

Requirements:

• Validated baseline HVAC cabin and powertrain model matching climatic

wind tunnel test results for both warm up and pull-down scenarios

• Configurable and replaceable cabin glazing models

• Climatic wind tunnel model rig and environment including solar loading

• Flexible model parameterisation to suit different data set types available:

– Maps/functions/coefficients

– Physical geometry and material properties

Benefits:

• No need to wait for available test facility slots post baseline validation

• Repeatability of tests under user specified conditions

• Ability to test a range of control strategies and glazing configurations in

parallel

Study the Effects of Energy Efficient Glazing on Cabin Thermal Energy and

Vehicle Efficiency


Modelling approaches (1)

A traditional approach might involve the use of decoupled independent

subsystem models which are run sequentially and fed with the results from

the previous simulation stage:

Stage 1. Powertrain

model running

steady state points

on drive cycle

Stage 2. HVAC using

crank speed from

powertrain model

results as an input

Stage 3. Heat

removed from

cabin model

• Benefits:

– Low complexity models that might run fast independently of each other

• Issues:

– No action-reaction modelled. • There is no immediate consequence of each simulated stage on the previous simulated stage

– Causality is predefined, limiting the versatility and reusability of the models

– Every equation is calculated at each time step

– Iterative process

– Problem specific with limited scalability

Stage 4. Re-run

powertrain model

with HVAC load



More integrated approaches might couple the subsystem model and use sub-

system outputs as feedback to update the boundary conditions of other

subsystems

1. Powertrain model

running steady state

points on drive cycle

compressor reaction torque feedback

crank speed2. HVAC model

3. Heat

removed from

cabin model.

• Not straight forward to redefine the model architecture or indeed to invert

models for backward facing analysis

• Each subsystem

– Has a predefined set of inputs and outputs

– Has causality predefined limiting the versatility and reusability of the subsystem

models

• Typically all equations are calculated at each time step



Acausal modelling approaches allow the user to focus on the system

architecture and let the software recast the model equations depending on

the boundary conditions applied. This allows models to be more flexible than

approaches 1 and 2.

• Interfaces can

– Inherently handle bi-directional flow of information

• Models can

– be inverted by simply changing the boundary conditions (speed vs. torque, heat

flow vs. temperature) to suit the analysis

• Symbolic manipulation reduces the equations to their simplest possible

form without removing any detail from the model

Powertrain model

speed + torque

balanceHVAC model

Cabin modelAcausal heat

transfer and fluid

flow balance

Heat transfer


The system models: HVAC

• Physical HVAC system validated

for baseline pull down tests:

– Geometry based Heat Exchangers

– Map based TXV

– Mechanical Compressor

– Prescribed boundaries for airflow

through the Heat Exchangers

Condenser

TXV

Evaporator

Compressor

Condenser air flow

Evaporator air flow

Example AC loop model schematic


The system models: Cabin

Cabin Glazing/Partitions

Cabin air flow

Multi-zone multi occupant cabin model validated for production glazing and

insulation running in climatic chamber


Integrated System Model

• HVAC

– HVAC system

– Multi zone cabin model

– Weather model incl. solar

radiation

• Powertrain

– Lossy driveline

– Lossy ICE

• Body

– Aerodynamic losses


Multi-threading the physical models

Powertrain model and HVAC system running in separate threads

• 37% CPU time reduction, 0.1% peak loss in accuracy. ~0% RMS error


Results examples from references

Cabin warm up and pull down scenarios. Traces represent different glazing technologies.

Glazing technology effect on vehicle energy consumption for ICE and EV powertrain


Part II

Model Export and FMI

(Functional Mock-up Interface)


Combining Engineering Models and Tools

Legacy code Computer algebra

calculation blocks

CFM simulationsFinite Element

Analysis

…some examples:

Control

Multibody

Fluids


FMI Standard – Overview

• Functional Mockup Interface standard - http://fmi-standard.org/

– Goal to improve the exchange of simulation models between partners

– Allows any modelling tool to generate C-code or binaries representing a model

which may be integrated in another simulation environment

• Open standard for model exchange

– Result of the Modelisar project, an ITEA 2 project led by Daimler

– 29 partners, started July 2008 and finished December 2011

– Already supported by over 100 tools

• Tool vendors provide open exchange, testing and verification

• Defines two mechanisms:

– Model exchange

– Co-simulation


FMI Standard – Supported By Many Tools


The Two FMI Mechanisms

• Model Exchange

– Designed to allow models to be connected together using a single solver provided by

the host environment

• Whole model runs at same rate

• Events handled robustly by iterating through all model equations

• Co-simulation

– Designed to allow models to be connected together with each model using its own

solver

– The host environment is responsible for controlling data exchange between each FMU,

usually at a fixed rate

• What is an FMU?

– Zip file with extension .fmu containing:

• XML file that describes the parameters, variables, capabilities (model exchange and/or co-

simulation) and author details

• Binaries for different operating systems (DLLs, etc.)

• C-source code can be included but not required by the standard

• Other resources such as data files


Preparing the model for FMI

• FMI supports real signal connections

– Physical quantities require to be converted to and from Real number signals

– Achieved through physical to causal input/output connectors:

Example of the required connections between two physical 1d rotational connectors

using real signal inputs and outputs


Preparing the model for FMI

• Modifications to the HVAC system to allow external coupling through FMI: …

HVAC and cabin

model


FMI Remarks

• A powerful interface which allows models from multiple tools to be coupled

together

– Allows understanding of

• whole system model dynamics by being able to look at sub-model interactions

– Allows integration of models from various departments and suppliers reducing

prescriptiveness about software tools used to deliver a model

– Can be used to export physical models to HiL, SiL and DiL

• Notes on usage:

– The integration and simulation of high numbers of coupled systems can lead to

difficulties in numerics, error handling and model debugging

– If using co-simulation, the communication step size will be crucial for achieving

good results especially when coupling plant dynamics together


System Level Modelling Conclusions

• OEMs struggle to ensure subsystem designs converge seamlessly

• Equation-based acausal tools such as Dymola Modelica that are input-

agnostic are available & have been proven on multi-physics applications

• Dymola supports multi-threading of models to reduce analysis time

• Modelica is an open standard for physical modelling

• FMI open standard provides a mechanism to reuse and exchange

models between departments/suppliers using different simulation tools


Thank you for your attention

modelling methodologies for studying the effects of energy … · 2017-09-26 · • open standard...

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