Hardware in the Loop Demonstration of Battery Surface Temperature Prediction
2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022
Hardware-in-the-Loop, Li-ion battery, long-term prediction, model-based approach, Thermal modeling
Long-term continuous operation or high-rate charging and discharging processes generate a lot of heat in lithium-ion batteries. This can cause a rise in battery temperature, resulting in battery performance deterioration. The battery thermal management system (BTMS) increases the battery performance by keeping the temperature within an optimum range. In this study, a hardware-in-the-loop (HIL) system is developed for the verification of the designed temperature prediction algorithm. Since it is important to evaluate the performance of developed algorithms in real-world applications. In this work, a platform is developed to collect data from all sensors, store it on a computer, and then feed it into a temperature prediction algorithm. The MATLAB environment is used to compute and predict the surface temperature of the battery. To test the performance of real-time temperature prediction, normalized mean absolute error (NMAE) was chosen as the error metric. The demonstration of real-time temperature prediction is shown in a discharge test with 2C-rate and NMAE is computed as 1.7352 %.
Kumar, Pradeep; Fuerth, Cole; Rankin, Gary; Pattipati, Krishna R.; and Balasingam, Balakumar. (2022). Hardware in the Loop Demonstration of Battery Surface Temperature Prediction. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022.