Predictive Thermal-Electrochemical Battery Modeling for Optimization of EV Thermal Management

Brad Holcomb, Joe Wimmer, Shawn HarnishGamma Technologies

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Motivation• Vehicle range, recharge rate, and battery lifespan are key challenges in

market acceptance of battery electric vehicles

• Thermal modeling of BEVs poses unique challenges– System integration

– Transient events

– Battery aging

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Project Outline

• Develop integrated vehicle, battery, and cooling model for a BEV

• Use detailed thermal and electrical simulation of the battery to study fast charging and vehicle range

• Simulate aging of battery over repeated charge and discharge cycles using an electrochemical model

Detailed Drive Cycle

Charging Strategies

Battery Life Analysis

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Vehicle Specifications

• Vehicle Base Specifications

– Mass: 1600 kg

– Battery Pack: 67 kWh Li-Ion

– Motor: 200 Hp

– FWD, single motor

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Model Systems

• Overall Model

– Sub-systems and complete model built in GT-SUITE

– Domains include:• Electrical (Battery, 12V

System)

• Mechanical (Vehicle and Turbomachines)

• Thermal (Battery, Motor, Cabin masses)

• Fluid (Coolant, Refrigerant, Air)

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Model Systems

• Refrigerant Loop

– Working Fluid: R134a

– Main loop cools cabin, variable speed compressor

– Auxiliary loop to cool battery pack, control valves open and close on demand when battery coolant reaches control temperature

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Model Systems

• Coolant Loops

– Battery loop, cooled by heat exchanger in AC circuit

– Motor and cabin heater loop, cooled by radiator

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Model Systems

• Vehicle System

– Inverter Motor uses performance maps

– Dynamic vehicle to predict performance and energy usage

– Driver model provides accelerator and brake inputs to match desired drive cycle

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Model Systems

• Electrical System

– Low and high voltage systems

– High Voltage system Lithium-ion: NCM 622 cathode, Graphite anode

– 20 cells per module (series)

– 15 modules per battery (5S, 3P)

– Battery model can be electrical equivalent or electrochemical

– Electrochemical integration with AutoLion-GT

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Model Systems

• Heat Generation Model

– The heat generation in the battery is calculated from the electrical equivalent model

– 𝑞𝑞 = 𝐼𝐼 ∗ −𝑑𝑑𝑉𝑉𝑜𝑜𝑜𝑜𝑑𝑑𝑑𝑑

∗ 𝑇𝑇 + 𝑉𝑉𝑂𝑂𝑂𝑂 − 𝑉𝑉

– This heat generation is applied as a source heat rate for thermal module

– The average temperature from each module is imposed on the battery electrical model

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Model Systems

• Battery Thermal

– Battery thermal model built from CAD data

– 20 Cells in series per module, cooling fins in between the cells

– Cooling channels in the bottom plate

– Thermal masses and flow volumes represented in model

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Model Systems

• Battery Thermal

– Battery thermal model built from CAD data

– Imported data was converted using GEM3D to pipes, flowsplitsand thermal masses

– Convection and conduction connections created from 3D geometry

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Simulation Setup• Vehicle Use Case

– Range study to evaluate vehicle performance over a standard driving cycle

– Fast charging simulation to recharge the battery

– Repeated cycles to study battery aging affects

J1634 Cycle

Fast ChargeRest

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Simulation Setup

• J1634 Range Study

– Vehicle model followed J1634 driving cycle

– Start at 100% SOC, extend constant speed segment until battery depleted (10% SOC)

– Ambient conditions: 27 C

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Simulation Setup

• Fast Charge Simulation

– Vehicle Stopped, DC charge (current source) applied to battery pack

– Start at 10% SOC, charge until battery reaches 100% SOC

– Multiple charge and thermal management strategies simulated

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Simulation Setup• Cycle Aging Analysis

– Performed in AutoLion-GT using current and temperature data from drive cycle and charge simulations

– Electrochemical model in AutoLion-GT using Newman Pseudo-2D model for Lithium-ion operation with models of:

• SEI layer growth in anode• Graphite Cracking in anode• Lithium Plating in anode

Yang, Xiao-Guang et. all, “Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging.” Journal of Power

Sources, 360 (2017) 28-40

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Results

• Thermal Behavior during Range Cycle

– J1634 driving cycle was used to measure vehicle range

– Initial SOC is 100%, vehicle stopped when SOC is 10%

– Active cooling in battery using air conditioning loop when needed

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Results

• Thermal Behavior during Charge

– Three separate charge cases• 250 Amp without cooling

• 250 Amp with active cooling

• Current ramp and cooling

– Current increase from 5 Amp to 250 Amp

– Adds 3 minutes to charge

– Modified cooling strategy

• Charging from 10% to 100% SOC

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Results

• Battery Aging– Performed in AutoLion-GT– Repeated charge and

discharge cycles• J1634 Driving cycle• 250 Amp fast charge

– Multiple strategies compared

• Rest period between cycles

– Result: Active cooling of the battery reduces the capacity loss

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Results

• Battery Aging

– An additional test case was simulated to study the effect of the current ramp on the battery aging

– The temperature profile of the Active Cooling case was imposed along with the ramp current profile

– Result: Modifying the charge strategy did improve the battery lifespan

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Results

• Range Reduction with Aged Battery

– Updated battery performance with effects of aging after repeated charge and discharge cycles

– Reduced range due to reduced battery capacity

– Battery aged rapidly shows increased temperature at end of cycle due to increased resistance

All information in this document is confidential and cannot be reproduced or transmitted without the express written permission of Gamma Technologies, LLC ©

Summary• Project Results

– A detailed multi-domain model was built for a BEV in GT-SUITE

– The model was run through a variety of operational scenarios, including range analysis and fast charging

– An electrochemical model was used to study the aging effects on the battery over the vehicle life

– Improving the thermal management of the battery during high stress events can reduce aging the battery