System Level Vehicle Model Development of Light Heavy Duty ... · Santhosh Pasupathi, Model Based Development Engineer, ITCA. Smruti Rathod, Model Based Development Engineering Intern,

System Level Vehicle Model Development of Light Heavy Duty Battery Electric Vehicle

in GT-SUITE

North American GT-SUITE Conference – 2018

Isuzu Technical Center of America, Inc.

Santhosh Pasupathi, Model Based Development Engineer, ITCASmruti Rathod, Model Based Development Engineering Intern, ITCA

11/15/2018 2

Table of Contents

Introduction

Deliverables

Project Scope

BEV Model Setup in GT-Suite – Architecture, Modifications & Powertrain Specifications

Simulation Results

Conclusion & Next Steps

References & Acknowledgements

1

2

3

4

5

6

7

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Introduction

1D – MAP Based Model

Can be built in very short time

Requires less data for validation

Faster than real time

Low fidelity model

This approach can be used for basicperformance and range estimationstudy with less input data fordifferent powertrain architectures

Important applications includebattery and motor componentsizing

1D map based EV model [1]

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Build a basic map-based Light Duty BEV model withMaster controller

Strategize control logic to implement:

Regenerative and friction braking combination

Regenerative pedal mapping for different regenmodes

Perform component sizing for motor power

Perform component sizing for battery capacity basedon vehicle architecture

Validate model against field data [GPS data] for rangeestimation and performance evaluation

Deliverables

Predicting Range & Performance

Component Sizing & Optimization[3]

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Project Scope

Modeling approach is map based – onlyestimated numbers can be obtained

Battery has been modelled on pack level,not cell level

Battery thermal characteristics areassumed to be constant

Traction/Inverter motor model is nottemperature dependent

Control system and strategy developmentfor individual components is not theprimary focus


[Start] [End]

[Energized, not active]

700 V

: Charging : Discharging

[Start]

[End] 700 V


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Mode 1: Key Start

Mode 2: Driving

Mode 3: Regen

EV power flow diagram[1]

Model is setup with three main operating modes of a BEV:

Key start – Power flow starts from 12 V battery whichenergizes motor and auxiliary system

Driving mode – 700 V main battery pack powers theTraction/Inverter motor, that propels the vehicle

Regen mode – Braking energy from vehicle is recoveredas useful energy and stored in main battery pack

EV Model Setup in GT-Suite: Architecture


[Start]

[End] [End] [End]

700 V

Charging: Battery in charging conditionDischarging: Battery in discharging condition

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Model Setup:

Traction/Inverter Motor: Brake torque &Electromechanical Efficiency maps*

Main Battery pack: Open Circuit Voltage &Resistance maps for charging & discharging*

Regen mode: Implemented using 3 variablelookup map

- Regen Motor Torque[Nm]

- Vehicle speed [mph]

- Battery SOC [%]

* Data source: Supplier, Nordresa



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Model Setup:

Traction/Inverter Motor: Brake torque &Electromechanical Efficiency maps*




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Model Setup:

Main Battery pack: Open Circuit Voltage &Resistance maps for charging & discharging*




Vehicle Speed [mph] Battery SOC [Fraction] Motor torque [Nm]x x xx x xx x xx x x

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Model Setup:

Regen mode: Implemented using 3 variablelookup map*

- Regen Motor Torque [Nm]

- Vehicle speed [mph]

- Battery SOC [Fraction]

Sample regen map:Mapped during field test for different regen modes,for different vehicle speed and battery SOC ranges:

* Data source: Field test


Regen mode Regen natureD1 Heaviest regenD2 Medium-II regenD4 Medium-I regenD6 Lowest regen

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EV Model Setup in GT-Suite: Modifications

Modifications to the motor controller model include:

Creep torque for initial 10 kph vehicle speedrange

Regenerative braking mode implementationusing selective regen mode lookup maps[D1/D2/D4/D6]

Different regenerative braking modes used in themodel:

Motor controls architecture setup in GT-Suite[1]

Parameters BEV - Map based model

GVW [lbs.] 14500

Battery Model 700V, 80 kWh, 122 Ah Capacity

Battery Data

# Open Circuit Voltage # Internal Resistance

[as function of Temperature [K] and battery SOC]

Motor Model 150 kW Power Capacity

Motor Data

# Electromechanical efficiency # Maximum & minimum Brake torque

[as function of Motor speed [RPM] & Torque [Nm]

Final Drive Ratio [FDR] 4.3

Tire Specs 225/70 R19.5

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EV Model Setup in GT-Suite: Powertrain Specifications

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Results - D1 (Heaviest) Regen mode selected

Results - D4 (Medium-I) Regen mode selected

Results

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Simulation Results

Results - D1 (Heaviest) Regen mode selected

Result plots

Energy Efficiency, Range & MPGe results

*[4]

*[5]

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Results plots

*Regen mode used: D1 - Heaviest Regen*Target vehicle speed: GPS data from field test

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Results plots

Motor torque for regenerative braking part

*Regen mode used: D1 - Heaviest Regen*Target vehicle speed: GPS data from field test

Combined (Driving + Regen) Combined (Driving + Regen) Combined (Driving + Regen)

Energy efficiency [Wh/mile] x x x

Range [miles] x x x

MPGe x x x

Field Result Simulation Result % Difference from field result

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Results & Validation : Energy Efficiency, Range & MPGe

Energy Efficiency Whmile

= ∑ Power consumed over entire drive cycle∑ Vehicle speed over entire drive cycle

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 (𝑚𝑚𝑚𝑚𝑚𝑚𝑅𝑅𝑚𝑚) = Battery efficiency ∗BatteryWh rating

Energy efficiency ( Whmile)

𝑀𝑀𝑀𝑀𝑀𝑀𝑅𝑅 = Total miles driven ∗Energy equivalent for 1 Gasoline gallonTotal energy from all fuels consumed

1 Gasoline gallon = 33.7 kWh electrical energy [6]

Regen mode used: D1 - Heaviest regen


Energy efficiency 100.0 99.7 -0.3

Range 100.0 100.3 0.3

MPGe 100.0 100.2 0.2

Field Result - Normalized *Simulation Result -

Normalized * % Difference from field result

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*Field Result: Field test data*Simulation Result: Field test GPS data used as target to the model







Regen mode used: D1 - Heaviest regen

* Numbers have been normalized

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Simulation Results

Results - D4 (Medium-I) Regen mode selected

Result plots

Energy Efficiency, Range & MPGe results

*[4]

*[5]

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Results plots

*Regen mode used: D4 – Medium I Regen*Target vehicle speed: Vehicle speed data from field test used,

due to unavailability of GPS field data

*Regen mode used: D4 – Medium I Regen*Target vehicle speed: Vehicle speed data from field test used,

due to unavailability of GPS field data

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Results plots


*Regen mode D1 – Heaviest Regen*Regen mode D4 – Medium I Regen

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D1 & D4 Regen Mode Comparison plots


Regen mode Regen natureD1 Heaviest regenD2 Medium-II regenD4 Medium-I regenD6 Lowest regen


Energy efficiency [Wh/mile] x x x

Range [miles] x x x

MPGe x x x

Field Result Simulation Result % Difference from field result

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Regen mode used: D4 – Medium-I regen


Energy efficiency 100.0 108.6 8.6

Range 100.0 92.1 -7.9

MPGe 100.0 92.1 -7.9



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*Field Result: Field test data*Simulation Result: Due to unavailability of GPS field test data,

vehicle speed data used as target to the model










Energy efficiency 100.0 108.6 8.6

Range 100.0 92.1 -7.9

MPGe 100.0 92.1 -7.9



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Reasons for this difference are: Unavailability of GPS field test data to be used for validation of

the model Regen map for D4 mode can be improved to be more accurate,

currently we are limited by data availability Once field test data is obtained, we can achieve % difference

within 5%







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Conclusion

1-D map based Light Duty BEV model with basic mastercontroller has been built and validated against field data

Powertrain architecture development with differentconfigurations is possible with map-based modeling

Model can be used for component sizing (motor andbattery)

Component optimization can be achieved by studyingmodel performance with different powertrainconfigurations

Based on performance study, final prototypearchitecture can be selected

Performance target setting for final prototype can beachieved through the model

Cost savings is a major outcome, without vehicleprototype development

Overall time and effort involved in product development& field test can be reduced by this approach

[1]


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Next Steps


Demonstrate accurate energy consumption of auxiliary system using system level model

Integrate detailed and predictive battery model with existing vehicle model

Implement HVAC system in the model for cabin heating/cooling

References

GT-Suite Vehicle Modeling Software

https://www.gtisoft.com/blog-post/lithium-ion-battery-modeling-for-the-automotive-engineer/

https://www.am-today.com/article/nissan-la-2eme-vie-des-batteries-pour-ve

http://prozza.com/english/pecolo.html

https://wattev2buy.com/efficient-ev-ranking-efficiency-electric-vehicles-usa/

http://large.stanford.edu/courses/2016/ph240/kountz2/

[1]

[2]

[3]

[4]

[5]

[6]

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Gerald BergseikerSenior Manager, PVRDE, ITCA

Role: Mentor and Technical Advisor

Amruth HalemathaPVRDE Intern, ITCA

Role: Test Data Analysis & Support

Acknowledgement

Bruce VernhamTechnical Director, PVRDE, ITCA

Role: Mentor and Technical Advisor

Yasuo FukaiChief Engineer, PVRDE, ITCA

Role: Engineering Leadership & Guidance

Marc DaigneaultChief Technology Officer, Nordresa

Role: Supplier Data Support

Francois DubeMechanical Junior Engineer, Nordresa

Role: Supplier Data Support

Jonathan ZemanVehicle Applications Team Leader, Gamma Technologies

Role: Software Support

Dhaval LodayaProject Engineer - Electrified Vehicle Applications, Gamma Technologies


Joe Wimmer Senior Engineer, Gamma Technologies


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Thank You!

Documents

System Level Vehicle Model Development of Light Heavy Duty ... · Santhosh Pasupathi, Model Based Development Engineer, ITCA. Smruti Rathod, Model Based Development Engineering Intern,