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Thermal Management System for Battery Electric Vehicle

Integrated Thermal Management Systems for Battery Electric Vehicle

Research Objective

This thesis is an attempt to answer two questions regarding electric vehicles. First one is ‘What is the impact of external and environmental conditions like ambient temperature, humidity, cabin initial temperature, road gradient etc. on energy consumption or range of a battery electric vehicle for different driving cycles?’. The second question is ‘How to model internal temperature of a cylindrical lithium ion battery cell provided only the current profile of the cell?’

Introduction

Transportation sector contributed 23% of total carbon dioxide emissions globally in 2018 . Thermal management plays a major role in safety, performance and comfort of an electric vehicle. Components like battery, electric motor, power electronics should be kept in optimum temperature range for safety and optimum operations. Cabin air conditioning has more became a necessity than luxury when ambient temperature crosses 40°C in India.

Simulation Model

We developed electric drive train model using backward faced simulation approach to calculate traction force, motor power, battery power, regenerative braking, energy consumption and range of an electric vehicle. We validated our simulation results with experimental test data from Argonne National Lab(ANL) data for a car (Figure1). We also validated vehicle acceleration (time required for a vehicle to get to 100km/hr from the beginning) as shown in Table below.

Major Results and Discussion

Electric drive train model is integrated with CoolSim cabin model from NREL. We found that Energy consumption increases around 90%, mean auxiliary power consumption increases by 150%, COP of refrigeration cycle is nearly halved, 968W (27%) more heat to be removed if ambient temperature jumps to 50°C from 20°C for WLTP driving cycle. We found that % change in energy consumption increases 2.5 times from 25-35°C to 35-45°C

The integrated model was applied to major Indian cities to observe effect of ambient conditions. We found that energy consumption is 50% more when driving on a hottest day in New Delhi than driving on an average day in Bangalore in May for UDDS(city) driving cycle.

Cabin initial temperature is affected by parking location of the vehicle. 10°C increment in cabin initial temperature than ambient increased mean auxiliary power consumption approximately 3 times at 20°C ambient temperature while there is no change for 50°C. As the ambient temperature rises from 20°C towards 50°C, importance of initial cabin temp. deceases but is significant for 20-40°C.Range is increased approximately 20 times if road gradability is changed from +4% (upward slope) to -4% (downward slope) due to regenerative braking.

For the second research objective, a lithium ion battery cell model was developed and validated to estimate terminal voltage and internal battery temperature for the given current profile. Equivalent circuit method was used to model terminal voltage which in turn is used to model internal battery temperature using thermal lumped capacitance method. The terminal voltage was validated for 6C charging discharging cycle. The absolute average error was 42mV for terminal voltage. Battery surface temperature and internal temperature is also validated for 6C charging discharging cycle for which absolute average error was less than 1.25°C for both the temperature. Internal battery temperature was validated for different magnitude of current pulse cycle.

Conclusion

Ambient temperature, cabin initial temperature, road gradient has significant impact on energy consumption and so on the range of an electric vehicle. Ambient relative humidity, additional vehicle mass has not much considerable effect on auxiliary power, refrigeration COP and energy consumption/range. Among major Indian cities, New Delhi consumes 20% higher energy than Bangalore on an average day in the month of May. Battery model is validated with experiments from Forgez et.al.

Vehicle
Published Time
Simulation Time

car 1
11.5
11.4

car 2
5.4
5.6

car 3
10.5
10.65

car 4
7
7.5