Ricardo Capability for Correlation Study

Transcription

1 Ricardo Capability for Correlation Study Technical Working Group on the correlation of CO 2 emissions from NEDC to WLTP Simon Wrigley, James Baxter, Dr. John Kasab Brussels

2 Ricardo overview Ricardo Overview Ricardo delivers world class strategy, engineering and technology programmes to the global automotive, transportation, defence and energy industries Company Selected public domain clients Established in 1915 and independent Passenger Car Government million revenue (FY 11/12) Additional 39.1 million revenue from AEA Europe (FY 11/12) acquired on 8th November 2012 More than 2300 employees with over 2000 technical, scientific and engineering staff Global presence in 21 locations Values 2

3 Ricardo s R&D portfolio develops understanding of technologies that will be applied to future vehicles, backed by cross-sector innovation HyBoost e-boost, 12+X electrical system / super-capacitors 150 hp with <99g/km CO2 in a Ford Focus Syner-D Downsizing / advanced boosting and intelligent thermal systems delivering 500Nm in a Jaguar XF, with CO2 reduction from 179g/km to 125g/km 3

4 Its experience developing technology with industry clients enables Ricardo to offer advice on longer-term trends for strategy & policy Broad consensus exists on the required evolution for light duty powertrains a single revolutionary winning technology is unlikely Roadmap for future low CO 2 light duty powertrain EU Fleet Average CO 2 Targets (g/km) TBD Demonstrators Fuel Cell Vehicle H 2 Infrastructure Fuel Cell & H 2 Supply/Storage Breakthrough Niche EVs Mass Market EV Technology Charging Infrastructure Energy Storage Breakthrough Demonstrators Plug-In Hybrid Energy Storage Breakthrough Full Hybrid Micro/Mild Hybrid For the medium and longer term, powertrain electrification will play an increasing role alongside advanced combustion engines Represents UK OEM consensus, developed by Ricardo for UK Automotive Council / NAIGT Source: ProjectNumber Client Confidential - ClientName IC Engine and Transmission innovations (gasoline/diesel/gas/renewables/h 2 ) Vehicle Weight and Drag Reduction ## Month SHORT TERM: ~2015 MEDIUM TERM: ~2025 LONG TERM: ~2050 Boosting & downsizing Turbocharging Supercharging Ricardo Low plc 2013 speed 6 torque enhancements Friction reduction Advanced thermal systems Stop/Start & low cost Micro Hybrid technology Niche Hybrid, PHEV s & Electric Vehicles Weight reduction (5-10%) Source: Ricardo Technology Roadmaps, Ricardo Analysis ProjectNumber Client Confidential - ClientName Extreme downsizing with 2 & 3 cylinder engines Combined turbo/ supercharging systems Advanced 48 volt micro hybrid systems dominate PHEV s in premium & performance products EV s for city vehicles Significant weight reduction High Efficiency Lean Stratified Gasoline Advanced low carbon fuel formulations ## Month 2013 Plug-in/Hybrid electric systems dominate Very high specific power ICE s 50% lower weight Range of application specific low carbon fuels Exhaust & Coolant energy recovery Advanced thermodynamic Cycles Split Cycle? Heat Pumps? Increasing Importance of Electrification 8 4

5 NEDC-WLTP correlation Ricardo s approach The European Commission plans a simulation exercise to develop understanding of the correlation between CO 2 emissions test results on NEDC and WLTP A mixed approach is planned by the Commission Detailed vehicle simulation plus targeted testing for validation We only consider the exercise task here Ricardo is able to bring the following to bear on this topic: Direct knowledge of future powertrain technologies and vehicle specifications Extensive simulation capability continuously refined through vehicle and powertrain development projects with OEMs and suppliers and through internally-funded R&D Existing model platforms and system model databases Experience implementing and applying models appropriately to projects specific needs In addition, results of work by Ricardo for EPA and ICCT can be used as a basis for this study Supplemented as necessary to cover the range of vehicle specifications required Modelling methodology additional benefits User-friendly interface Flexibility Straightforward sensitivity analysis 5

6 Modeling Tools Ricardo has developed a Data Visualization Tool on programs for the US Environmental Protection Agency and ICCT The Data Visualization Tool (DVT) allows one to access the design space covered in the study User can query one point in the design space to assess a specific vehicle configuration User can generate "point clouds" by selecting ranges of continuous parameters for a given set of discrete parameters (LDV class, powertrain, engine, & transmission) Efficient Frontier within the design space subset can be marked Report and EPA DVT are publicly available and were peer reviewed Source: and 6

7 Modeling Tools The Data Visualization Tool calls Response Surface Models, which were fit from vehicle performance simulation results that sampled the design space Input Factors Vehicle class Powertrain configuration Engine Transmission Engine displacement Final drive ratio Rolling resistance Aerodynamic drag (C d A) Vehicle mass Engine efficiency Electric machine size Output Factors Drive cycle CO 2 emissions (g/km) NEDC, WLTP FTP, HWFET, and combined JC08, US06 Drive cycle fuel economy (mpg) NEDC, WLTP FTP, HWFET, and combined JC08, US06 Acceleration times 0-10, 0-30, 0-50, 0-60, 0-70 mph mph, mph Top speed on 5% or 10% grade Velocity or distance at 1.3 s Velocity or distance at 3.0 s 7

8 Future Design Space Technology packages in the Design Space Vehicle classes: [Exemplar vehicle in brackets] EU-relevant drive cycle B Class (Small Car) [Toyota Yaris] results available for C Class [VW Golf or Ford Focus] bold vehicle classes D Class (Standard Car) [Ford Mondeo] Full Size Car (E Class) [Chrysler 300]* Small crossover utility vehicle (Small multi-purpose vehicle) [Opel Antara] Small N1 [Ford Transit Connect] Large N1 (Large MPV) [Dodge Grand Caravan or Ford Transit] Light-Duty Truck [Ford F-150]* Light Heavy-Duty Truck [GMC 3500HD]* Powertrain architectures: Conventional, with stop-start Powersplit hybrid P2 hybrid * Exercised on US drive cycles only 8

9 Approach to study Ricardo s suggested process to correlate NEDC & WLTP emissions uses existing results plus targeted use of existing validated models Existing results supplemented with new results as required 1 2a Interrogate vehicle 3 4 design space from ICCT and EPA programmes, output results Identify vehicles & technologies 2b Add new technologies to previously correlated models, generate new results Integrate data Analyse & further validate data 5 Report JRC has indicated six vehicles Specifics to be agreed Define additional, new technology packages Engine Transmission Define input data Vehicle mass Coast down parameters Vehicle performance simulation on new vehicle configurations Output data NEDC values WLTP values Optional validation with new test data Stakeholder consultation to be carried out throughout process Draw together results from different sources Option to generate new RSMs Compare CO 2 results NEDC vs. WLTP Each vehicle. tech package Perform sensitivity analyses determine influences of key input parameters Optional validation with new test data Prepare tabulated or graphical summaries of results Option to use EPA/ICCT data visualisation tool Report out on findings Draw conclusions 9

10 Approach to Study 2 Previous results from EPA & ICCT studies used where possible New simulations performed to fill in gaps Steps 2a & 2b in the study are may be summarised in the following chart The balance between the top and bottom flows will depend upon the vehicle specification chosen at the start of the project 2a 2b Existing EPA & ICCT data set & results Carry Over: Some vehicle / technologies will be relevant to this study New Simulations: for vehicle spec not covered by EPA / ICCT work Use EPA & ICCT DVT to extract relevant data points Stretch outputs where spec of this study is sufficiently close to EPA / ICCT New Simulation results 3 Integrate data New inputs for this study Validated models Revised input data set Validation / test data 10

11 Approach to Study Step 3 Integrate Data This output data can then be presented in one of two ways Ve ehicles Technologies A B C D etc etc Relevant results from previous data set merged with new simulation results. Results in tabular form. Limited sensitivity analysis on key parameters performed to give confidence OR Response surface fitted to a large number (~500,000) of simulations to allow a Data Visualisation Tool to be built allows full design space exploration and interaction of parameters to be understood Results are integrated depending upon the vehicle and technologies selected: Existing results from EPA and ICCT work can be carried over directly Existing results will be scaled slightly to fit vehicle / technology combination New simulation results (vehicle / technology combination outside the valid range of previous work) 11

12 Modeling Tools For the EPA and ICCT programs, Ricardo used MSC.Easy5 for vehicle performance simulation MSC.Easy5 is a commercially available software package widely used in industry for vehicle system analysis, which models the physics in the vehicle powertrain during a drive cycle. Torque reactions are simulated from the engine through the transmission and driveline to the wheels. The model reacts to simulated driver inputs to the accelerator or brake pedals, thus enabling the actual vehicle acceleration to be determined based on a realistic control strategy. MSC.Easy5 was the vehicle performance simulation tool used in the 2008 and 2010 EPA studies, both of which were peer-reviewed. The models from the 2010 EPA study were applied to the NEDC, WLTP, and JC08 cycles in work for ICCT 12

13 Modelling Tools Overview of Ricardo Vehicle Systems Simulation Whole vehicle models are built from an existing library of vehicle components each sub-model has been validated in past projects Library includes all main vehicle systems, as depicted on the diagram below Aftertreatment DOC DPF SCR LNT Drivecycle Legislated or Real world driving cycle Performance Energy storage Battery Super-capacitor Flywheel Engine type Gasoline Light duty Diesel Heavy Duty Diesel Fuel cell Transmission Manual Planetary automatic AMT / DCT CVT Vehicle architecture Defence Trucks Passenger car Bus / coaches Thermal system Simple representation Refined representation Hybrid/electric drive systems Electric motor CVT 13

14 Model Validation NEDC validation experience can be carried over to WLTP Normalised Fuel Consumption (l/100km) Normalised CO 2 (g/km) Client Data Ricardo Simulation Client Data Ricardo Simulation Cold-Start Hot-Start Instantaneous Fuel Consumption n (kg/s) Fuel Consumption (kg/s) JLR Client Data Ricardo V-SIM Simulation Veh Vehicle SpdSpeed Vehicle Speed (m/s) Vehicle Speed (m/s) Time (s) 14

15 Model Validation Ricardo is already engaged in project work & clients involving WLTP Ricardo have some experience on internal R&D and client projects of simulating WLTP OEM s, Tier 1 suppliers Draws on same validation processes and knowledge as NEDC For example, recent projects have: Assessed coolant warm up, operating points and cycle fuel consumption for a conventional light duty diesel vehicle for NEDC v WLTP Modelled a mild hybrid for a major global OEM comparing fuel consumption benefits and component sizing for WLTP Ricardo also have a growing database of test results from the chassis dynamometer on the WLTP cycle to help correlate simulation Coolant Temperature ( C) & Vehicle Speed (km/hr) Pre-Turbine Exhaust Temperature (deg C) NEDC - Coolant Temperature WLTC - Coolant Temperature NEDC - Road Speed WLTC - Road Speed WLTC vs NEDC Coolant Warm-up Profile NEDC Ricardo NZED Vehicle - 1.9L, 1590kg, 6-speed auto V-SIM Simulation WLTP +150kg, +20% road load Time (seconds) Engine Speed (rpm)

16 Model Validation Detail of Approaches VETherm: Ricardo thermal modelling approach for low CO 2 VEHicle Thermal Modelling Complex modelling approach This flow diagram represents a thermal management powertrain simulation toolchain with the aim of assessing the fuel consumption and CO2 emissions reduction taking into account engine warm-up CAD engine, oil and coolant circuit Q comb,net PISTON OIL SPLASH OIL OIL DECK COOLANT FLAME DECK + COOLANT Lumped capacitance model for engine, oil (with oil pump) and coolant circuits Engine geometry data Heat exchanger maps (oil cooler, radiator, heater core) Heat rejection from combustion Wave simulations Maps of heat transfer coefficient and gas temperature in combustion chamber (h_gas (Woschni) and T_gas) Friction (FMEP) With FAST simulations or measurement by stripmethod fmep (bar) auxiliaries group valve train group reciprocating group crankshaft group engine speed (rpm) Speed / load profile Vehicle Model VSIM (Ricardo) Drive cycle NEDC Flowmaster or Amesim Predicted warm-up Fuel consumption (L/100 km) CO 2 emissions (g/km) Head Cab Heater Vehicle Model Gasket Friction Losses Expansion Tank Transmission Block Liners Radiator Oil circuit with pressure drops, oil pump, oil cooler Coolant circuit with pressure drops, water pump, thermostat Sump Thermostat Heat Pistons Rejection Calculation s Fuelling Calculations Thermo-hydraulic model for assessing engine thermal behaviour 16

17 Ricardo s track record for delivery of world class products and services draws on 2300 staff in 21 global locations Proven record of delivery on client projects for many global OEM s, Tier 1 supplies and other stakeholders, often with internationally-based project teams Ricardo have over 60 simulation engineers & analysts spread globally across the business Over half EU-based (UK, Germany, Czech Republic) NEDC / WLTP correlation project team would comprise: US-based simulation team (model operation) EU-based simulation team (EU market input data & validation) EU-based project leadership (links with EC LDV Framework) Ricardo UK Shoreham Technical Centre Ricardo UK Midlands Technical Centre Ricardo UK Cambridge Technical Centre Ricardo - AEA Glengarnock Ricardo AEA Harwell Ricardo - AEA London Ricardo AEA Cardiff Ricardo US Detroit Technical Centre Ricardo US Chicago Technical Centre Ricardo Germany Schwäbisch Gmünd Ricardo Czech Republic Prague Ricardo in Italy Turin Ricardo China Shanghai Ricardo India Delhi Ricardo Japan Yokohama Ricardo in Korea Seoul Ricardo in Malaysia Kuala Lumpur Ricardo in Russia Moscow 17

18 Appendix

19 Case Studies Technical Input to EPA for Light Duty Vehicle Greenhouse Gas Rule Approach Combined FTP-HWFET (mpg) Situation and Objectives 5 0 Baseline Stoich DIT+8AT Diesel+DCT Engine Displacement (L) EPA wanted rigorous technical input to support Rule Making Analysis estimates greenhouse gas emissions of vehicles based on future technology packages and combinations thereof Rely on Ricardo s global technical expertise to develop new methodology to quantify the effectiveness of several advanced technologies in a complex space defined by a large number of vehicle parameters Ricardo and EPA team identified future technology packages and estimated their effects on fuel consumption Created new vehicle classes, implemented hybrid powertrains and controls (P2 and Powersplit) and incorporated new technology packages to define a broad design space Ricardo's complex systems modeling approach used to examine the extensive design space Results and benefits Improved accuracy of modeling tools supporting rule making Broad design space allows examination of several combinations of technologies, and their synergistic effects Data visualization tool facilitates exploration of the design space Fully documented and peer-reviewed approach and results for use in rule by EPA 19

20 Case Studies Greenhouse Gas Reduction Potential for European Light Duty Vehicles in for ICCT Vehicle Rolling Aero. g CO 2 /km C Class Vehicle Configuration Mass Resist. Drag on NEDC Baseline with SI engine 100% 100% 100% 165 Baseline with Diesel engine 100% Baseline 100% 100% 124 SDIT 100% & DCT 100% 100% 107 P2 (Atk. CPS) Stoich DI Turbo + 8-spd DCT Powersplit 85% (Atk.DVA) 90% 90% 93 Slower than 70% 80% 80% 80 baseline 100% 100% 100% 104 Adv EU Diesel + 8-spd DCT 85% 90% 90% 93 70% 80% 80% % 100% 100% 96 Atkinson (CPS) Powersplit Hybrid 85% 90% 90% 86 70% 80% 80% % 100% 100% 81 Atkinson (CPS) P2 Hybrid 85% 90% 90% 71 70% 80% 80% 62 Approach Ricardo and ICCT team identified additional future technology packages and estimated their effects on fuel consumption Implemented NEDC and JC08 drive cycles, and adapted the Data Visualization Tool and results for EU Ricardo's complex systems modeling approach used to examine the extensive design space Situation and Objectives ICCT wanted to extend Ricardo's work for EPA to the European Union Analysis estimates greenhouse gas emissions of vehicles based on future technology packages and combinations thereof Rely on Ricardo s global technical expertise and complex systems methodology to quantify the effectiveness of several advanced technologies in a complex space defined by a large number of vehicle parameters Results and benefits Improved accuracy of modeling tools to evaluate future vehicle performance and GHG emissions Broad design space allows examination of several combinations of technologies, and their synergistic effects Data Visualization Tool facilitates exploration of the design space Final report and DVT are available from 20

21 Future Design Space Technology packages in the Design Space Engines: Stoichiometric direct-injection turbocharged (SDIT) SI engine Lean-stoichiometric direct-injection turbocharged (LDIT) SI engine EGR direct-injection turbocharged (EDIT) SI engine Atkinson cycle SI engine with cam-profile switching (CPS) Atkinson cycle SI engine with digital valve actuation (DVA) Advanced European Diesel Advanced U.S. Diesel 2010 Baseline SI engines 2010 Baseline Diesel engines Transmissions: 2010 baseline six-speed automatic Advanced automatic transmission, eight-speed Dual clutch transmission, eight-speed, dry or wet clutch Powersplit planetary gearbox 21

22 Future Design Space The Design Space was defined and sampled for simulation results The complete design space covered by the EPA and ICCT studies includes 9 light-duty vehicle classes total (7 for US, 6 for EU) 3 powertrain architectures (conventional, Powersplit Hybrid, and P2 Hybrid) 9 engine types (two baseline + seven advanced) 4 transmission types (baseline, advanced automatic, wet & dry DCT) 6 vehicle-level continuous parameters (mass, final drive ratio, aero, etc.) 7 drive cycles (WLTP, NEDC, FTP, HWFET, US06, JC08, performance) With DoE method, needed 500,000 simulations to sample the design space Hybrid Architecture P2 Hybrid with 2020 DCT Input Powersplit Stoich DI Turbo Baseline Engine with speed Automatic Trans. Advanced Engines Lean DI Turbo 2010 Diesel with speed Automatic Trans. EGR DI Turbo Stoich DI Turbo Atkinson with CPS Advanced Engines Lean DI Turbo Atkinson with DVA Vehicle Class B Class X X X X X X X C Class X X X X X X X D Class X X X X X X X Small CUV X X X X X X X N1 (Small) X X X X X X X N1 (Large) X X X X X X X EGR DI Turbo DoE Range (%) Parameter P2 Hybrid Powersplit Engine Displacement Final Drive Ratio Rolling Resistance Aerodynamic Drag Mass Electric Machine Size Advanced Transmission Vehicle Class B Class X X X X X X X X C Class X X X X X X X X X D Class X X X X X X X X X Small CUV X X X X X X X X X N1 (Small) X X X X X X X X X N1 (Large) X X X X X X X X X 2020 Diesel 6-speed Automatic 6-speed Dry DCT 8-speed Automatic 8-speed Dry DCT 8-speed Wet DCT Parameter DoE Range (%) Engine Displacement Final Drive Ratio Rolling Resistance Aerodynamic Drag Mass

23 Available results from DVT Results with "Pre-Baseline" Technologies are comparable to today's vehicles Pre-Baseline Nominal Cases use road load factors from the exemplar vehicles per the EPA Test List Continuous parameters only are varied; choice of diesel or gasoline engines 23

24 Available results from DVT From the European extension to the EPA program, results can be compared between WLTP and NEDC Results for selected C Class Vehicle configurations with identical road loads Vehicle Rolling Aero. g CO 2 /km on 0-60 mph C Class Vehicle Configuration Mass (kg) Resist. Drag WLTC NEDC Accel Baseline with SI engine % 100% Baseline with Diesel engine % 100% Baseline with SI engine (manual) % 100% Baseline with Diesel engine (manual) % 100% % 100% Stoich DI Turbo + 8-spd DCT % 90% % 80% % 100% Lean-Stoich DI Turbo + 8-spd DCT % 90% % 80% % 100% EGR DI Turbo + 8-spd DCT % 90% % 80% % 100% Adv EU Diesel + 8-spd DCT % 90% % 80% % 100% Atkinson (CPS) Powersplit Hybrid % 90% % 80% % 100% Atkinson (CPS) P2 Hybrid % 90% % 80%

25 Model Validation The vehicle performance models for the EPA program were validated against exemplar vehicles for each class Each of the light-duty vehicle classes from the EPA program has an exemplar vehicle model, which is at the technology level Earlier vehicles were used in the 2008 Ricardo study for EPA covering MY vehicles Small Car and Light Heavy-Duty Truck (LHDT) were new to the 2010 study C Class models were validated on the ICCT program Validation C Class Focus FUEL ECONOMY (mpg) CO 2 (g/km) Vehicle ETW (lb) Mass (kg) Displ. (L) Peak Torq. (Nm) Peak Power (kw) Trans Spd- Type Final Drive Ratio EPA FTP EPA HWY EPA US06 NEDC JC08 FTP HWY US06 NEDC JC08 Focus C Class MT Ford Focus ----> % difference (simulation model compared to EPA data base) ----> -2.7% Focus C Class MT Ford Focus ----> % difference (simulation model compared to EPA data base) ----> 1.4% 25

26 Model Validation A consistent methodology to account for cold start effects on fuel consumption has been used in these simulations A correlation factor for increased fueling requirements is applied to the calculated fuel flow during the warm-up period Factors were developed using Ricardo proprietary data to develop an engine warm-up profile that indicates the fuel consumption penalty during warm up The future vehicles have a lower correlation factor to reflect the benefits of an electrical water pump and of warm restart for the advanced vehicles. Drive Cycle Cold Start Period Baseline Factor WLTP First 390 s +22% +11% NEDC First 390 s +22% +11% FTP75 Bag 1 +20% +10% JC08 First 390 s* +22% +11% Advanced Factor * JC08 averages the cold start results with hot start results 26

27 Model Validation Ricardo developed model inputs for technology packages, e.g., Stoichiometric, Direct Injection Turbocharged Engine Efficiency map generated by Ricardo for EPA program (left) is based on benchmarking and research data, and compares favorably to research results from 2011 General Motors paper (right) from demonstration engine. Source: Ricardo Analysis Source: Schmuck-Soldan, S., A. Königstein, and F. Westin,

28 Model Validation Detail of Approaches Ricardo use a variety of approaches for cold start depending upon the aim, duration and type of project Spectrum of simulation approaches used within Ricardo depending upon the project: Detailed, standalone models for thermal & transient engine effects These are used for in depth, detailed projects such as SynerD where improved thermal management was a key project objective Complex modelling activities typically used to assess the impact of a technology change to a given engine This approach is of most use where detailed input data is available Use of multiplication factors & use of steady state maps for simulation Used most commonly for this kind of work by Ricardo Factors are based on years of experience with testing and using the detailed simulation models For this kind of project, pragmatic approach suggested to use cold start factors and steady state maps There are more detailed transient and thermal models available, but they don t bring a more accurate correlation bring greater level of prediction for e.g. calibration effects of new components but the extra complexity is not justified unless detailed input data is available Also, they are of greater use for comparing effect of base engine changes for a given duty, for comparing a powertrains response between cycle A & B it is not necessary to go into this level of detail in most cases Validation of models using maps and correction factors is achieved on client projects within ~2% of test data 28

29 Model Validation Detail of Approaches VETherm: Ricardo VTMS Amesim model engine thermal model Head Gasket Friction Losses Transmission Block Liners Sump Pistons 29

30 Model Validation Detail of Approaches Vehicle and Engine Performance Analysis Prediction of Vehicle Fuel Consumption From Cold Start Objectives Prediction of transient engine performance linked with transient thermal system and friction modelling for assessment of vehicle fuel consumption 1D engine modelling for prediction of engine heat rejection 1D thermal model of complete vehicle system for prediction of system warm-up Prediction of engine friction through links to thermal and performance models Link to vehicle model with manual or automatic transmission Vehicle Fuel Consumption Prediction Overview Case study showing gasoline engine fuel consumption prediction for a gasoline engine with automatic transmission 1D engine model running across engine speed/load range Automatic transmission model incorporating torque converter Vehicle and driver model linked to engine model Complete simulation of NEDC cycle shows excellent correlation with test data 30

31 Model Validation Detail of Approaches Energy balance calculation Engine fuel consumption during warm-up is closely related to oil temperature WAVE maps (gas temp, Heat Transfer Coefficient) Heat generation Engine oil temperature Total FMEP vs speed and oil temp (motored engine) Exhaust mass flow and temp vs speed and IMEP Contribution to IMEP (BMEP +FMEP) Oil viscosity Engine moving parts friction (FMEP maps) Engine Speed Engine load (Vsim) Fuel energy = Brake power + Heat to combustion chamber walls + Friction losses + Exhaust Enthalpy loss Fuel Consumption assessement Vehicle simulation prediction for BMEP and engine speed FM/Amesim prediction based on WAVE gas temperature, WAVE Heat Transfer Coefficient and predicted metal temperature Total FMEP from FAST predictions and trend from ricardo database of similar engines Cp*Exh.mass flow*(t exhaust gas - T amb ) 31

32 Model Validation Detail of Approaches Additional technologies can be integrated into existing models if necessary For example, if waste heat recovery technologies are being assessed, separate plant models of relevant systems would be integrated into base vehicle model Thermo-electric generator Cooling & A/C circuits Rankine cycle Cabin thermal model Absorption cycle Easy5 Model 32