Real Time Prediction of Li-Ion Battery Pack Temperatures in EV Vehicles

A semi-analytical digital twin model of a 90 kW.h li-ion battery pack was developed to capture thermal behavior of the pack in a real-time environment. The solution uses reduced-order models that minimize compute cost/time yet are accurate in predicting real-world operation. The real-time heat generation rate in the battery pack is calculated using 2RC equivalent circuit model. A series of HPPC tests were conducted to calibrate the equivalent circuit model in order to accurately calculate heat generation rate as a function of SOC, temperature, current, charge/discharge mode and pulse duration. In the paper, live-sensor data was integrated into the digital twin system level model of the battery pack to create a real-time environment. The generated tool was utilized to monitor the real-time temperature of the battery pack remotely and have a predictive maintenance solution. The model results for heat generation rate, terminal voltage, and temperature were found to be consistent with the test data across a wide range of conditions. The generated model was used to accelerate battery pack design and development by enabling the evaluation of design feasibility and to conduct in-depth root causes analyses for various inputs and operating conditions, including initial SOC, temperature, coolant flow rate, different charge and discharge profiles. The resulting digital twin model provides additional data that cannot be measured offering the EV industry an opportunity to improve its safety record.