Performance Analysis of an Integrated BEV Thermal Management

Fast introducing battery electric vehicles to the market leads to the accumulation of errors and recalls of the vehicle. It can be avoided by simulating battery electric vehicles (BEV) in an integrated manner. Electric vehicle thermal management is key in optimal performance of a vehicle. A drivetrain model is developed integrated with cabin, battery, and power electronics models. Figure 1 shows architecture of the integrated electric vehicle model.

Figure 1 : Integrated battery electric vehicle model

Key system-level outputs like vehicle energy consumption, system COP, and total auxiliary power are obtained. The battery is modelled using 2RC equivalent circuit modelling approach. The internal battery cell temperature is also estimated and validated along with the surface temperature and terminal voltage. Power electronics diode and IGBT temperature is estimated and validated with the literature. The battery and power electronics is cooled with cold plate liquid cooling. CoolSim model from NREL is integrated with the vehicle model to estimate cabin air temperature. The integrated model is also validated at system level with American Automotie Association (AAA) data. Figure 2 illustrates electrical and thermal network diagram for the integrated model.

Figure 2 : Model network diagram

Effect of external parameters (ambient temperature and road load coefficients)

Total auxiliary power is the sum of compressor power, evaporator blower power, condenser fan power, pump power for the battery and power electronics cooling loop, radiator fan power for the battery, and power electronics cooling loop. System mean COP is the ratio of evaporator load to the total auxiliary power. Compressor power contribution to the total auxiliary power increases from 63 % to 80 % when ambient t temperature is increased from 25 to 50 degree Celsius.

Figure 3: Effect of ambient temperature

Fig. 3 shows the effect of ambient temperature on energy consumption, total auxiliary power, and system COP. Energy consumption rise is higher at 35–45 ◦C (3.67 Wh/km/◦C) than at 25–35 ◦C (2.48 Wh/ km/◦C). From 45 to 50 ◦C, the energy consumption rate reduces to 1.12 Wh/km/◦C. When the ambient temperature is doubled from 25 to 50 ◦C, the mean auxiliary power increases around six times from 1058 W to 6197 W, energy consumption increases by 35 % per kilometer (from 189 to 256 Wh/km), and system COP drops by 39 % from 1.52 to 0.93 for US06 driving cycle covering 12.9 km. Fig. 4 shows the comparison of energy consumption and auxiliary power consumption for different Indian cities for the US06 driving cycle (aggressive driving). Energy consumption and auxiliary power consumption are highest in New Delhi and lowest in Mumbai. If a car travels 100 km with a US06 driving cycle in May in Mumbai, then the same car can travel only 91.72 km in New Delhi, 95 km in Pune and Chennai, 99.7 km in Bangalore.

Figure 4: Model application to Indian cities

Vehicle manufacturers conduct significant testing for various regulatory agencies. In addition to fuel economy and emissions data, the EPA in the United States requires the manufacturers to supply data on vehicle road-load coefficients based on the “coast-down” test. In this test, the vehicle is allowed to coast down from 120 km/h in neutral, and three coefficients are generated to simulate the rolling, spinning, and aerodynamic resistances.

where v is the velocity of the vehicle in m/s and coefficients A, B, and C are determined from the coast-down test. Road load coefficients are tested for specific road conditions, but real-life road conditions can vary from test conditions. These coefficients are obtained for the new vehicle. But with the aging of the vehicles, these coefficients may change. Hence it is essential to perform a sensitivity study for these road load coefficients. This study is performed for two different driving cycles to understand the effect of vehicle speed on the sensitivity of road load coefficients. Energy consumption increase and decrease by 6.3 Wh/km with city and 6.7 Wh/km with highway driving cycle when road load coefficient A is increased/decreased by 20 %. Energy consumption increase and decrease by 4.5 Wh/km with city and 9.6 Wh/km with highway driving cycle when road load coefficient B is increased/decreased by 20 %. Energy consumption increase and decrease by 3.5 Wh/km with city and 8.7 Wh/km with highway driving cycle when road load coefficient C is increased/decreased by 20 %.

Effect of drivetrain model parameters

Figure 5: Effect of motor and transmission efficiency on energy consumption

Fig. 5 shows the effect of motor and transmission efficiency on energy consumption for the city and highway driving cycle when the cabin A/C is turned off. For example, suppose a vehicle could travel 100 km with a transmission efficiency of 0.98. The same vehicle could only travel 94.6 km with a transmission efficiency of 0.95, keeping other parameters constant with the UDDS cycle. Energy consumption increases by 2 Wh/km with UDDS driving cycle and 1.5 Wh/km with highway driving cycle for every 1 % drop in transmission efficiency in the 0.95–0.98 range. Energy consumption increases by 20 % for UDDS and 12 % for highway drive cycles when motor efficiency decreases from 0.94 to 0.85. Every 100 kg increase in vehicle mass increases vehicle energy consumption by 1.5 Wh/km with UDDS and 0.4 Wh/km with HWFET driving cycle when the cabin A/C is turned off. The gradability is the maximum slope that a vehicle can climb at a certain speed. It is the tangent of the road incline angle and is often expressed in percentage. Energy consumption increases by 7.82 times with UDDS and around five times with the HWFET drive cycle when road gradability is changed from 􀀀 2 % (downward slope) to +2 % (upward slope). If a card could travel 100 km on a flat road, the same car could only travel 54.45 km with UDDS and 59 km with HWFET drive cycle on the road with an upward slope of 2 %, keeping other parameters constant.

Effect of cabin parameters variation

Auxiliary power increases by 11 % (from 2771 to 3071 W) when the solar heat rate to the cabin shell is increased from 0 to 1500 W for the US06 driving cycle. Auxiliary power is more than doubled and energy consumption increases by 9 % when the initial cabin temperature is raised from 25 to 35 degree C for 25 degree C ambient conditions with a US06 drive cycle. For 35 degree C ambient conditions, an increase in initial cabin temperature by 10 degree C to 45 degree C increases auxiliary power by 1231 W and energy consumption by 16.3 Wh/km, which means the car could only travel 93 km, which was earlier traveling 100 km with 35 degree C as initial cabin temperature. These results signify that parking location before the drive significantly affects the vehicle’s energy consumption.

Figure 7: Effect of cabin target temperature on auxiliary power and energy consumption at different ambient temperature.

Fig. 7 shows the effect of cabin target temperature on energy consumption and auxiliary power for different ambient temperatures with the US06 drive cycle. For example, there is a 32 % reduction in auxiliary power and a 6 % reduction in energy consumption if the cabin target temperature increases from 22 degree C to 28 degree C at 35 degree C ambient temperature. If a car could travel 100 km with a cabin target temperature of 22 degree C, the same car could travel 109 km when the cabin target temperature is raised to 28 degree C at 35 degree C ambient temperature.

Effect of battery parameters variation

Two different battery cooling schemes are considered for assessing its impact on internal cell temperature and terminal voltage with the US06 drive cycle at 35 degree C ambient temperature. In one cooling loop, exit air from the cabin is blown on the battery radiator to cool the coolant, as shown in Fig. 2. Ambient air is directly blown on the battery radiator in another cooling loop. Fig. 8 compares internal cell temperature and terminal voltage with these different cooling schemes. Cell terminal voltage is slightly lower when cabin air is blown on the battery radiator. At the end of the US06 drive cycle, around a 5 degree C temperature difference is observed in internal cell temperature between two cooling loops. This signifies that careful designing of the battery cooling loop could result in a considerable reduction in internal cell temperature.

Figure 8: Cell internal temperature and terminal voltage comparison with two different cooling loops for battery thermal management

Effect of power electronics model parameter variation

We observed maximum transistor temperature increase from 71 degree C to 74 degree C and maximum diode temperature from 66 degree C to 68 degree C when road gradability increased from 0 % (flat road) to 2 %, with a coolant flow rate of 15 l/min. Maximum transistor junction temperature is reduced by 10 degree C from 81 degree C to 71 degree C when the coolant flow rate increases from 5 to 15 l/min. Further increasing the coolant flow rate from 15 to 25 l/min reduced maximum transistor junction temperature by only 3 degree C to 68 degree C. A similar temperature pattern is observed in diode temperature for coolant flow rate.

Energy saving opportunities

Most automakers have 8–10 years or 1,00,000 miles (1,60,934 km) warranty period on their electric car batteries. So, it is assumed that an electric car travels 1,60,934 km during its lifetime. Table below shows energy savings with respective energy-conscious decisions for the US06 driving cycle and 35 degree C ambient temperature.

Note that cabin A/C is turned off for motor and transmission values. Parking the electric vehicle under the shed and using coating on the vehicle to reflect out solar radiation can save 2623 kWh of energy over the vehicle’s lifetime. Increasing cabin target temperature from 22 ◦C to 28 ◦C resulted in saving 2060 kWh of energy throughout the vehicle’s lifetime. Using an efficient electric motor can save 4297 kWh of electricity, while an efficient transmission system saves 1336 kWh of energy over the lifetime of the battery-electric vehicle.

Please refer to the paper for more details: Vinayak Kulkarni, Gautam Ghaisas, Shankar Krishnan, Performance analysis of an integrated battery electric vehicle thermal management, Journal of Energy Storage, Volume 55, Part A, 2022, 105334, ISSN 2352-152X

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