Predictive Battery Thermal Management System Control Strategy and Simulation in Electric Vehicle

With the global issue of fossil fuel scarcity and the greenhouse effect, interest in electric vehicles (EVs) has surged recently. At that stage, because of the constraints of the energy density and battery performance degradation in low-temperature conditions, the mileage of EVs has been criticized. To guarantee battery performance, a battery thermal management system (BTMS) is applied to ensure battery operates in a suitable temperature range. Currently, in the industry, a settled temperature interval is set as criteria of positive thermal management activation, which is robust but leads to energy waste. BTMS has a kilowatt-level power usage under high- and low-temperature environments. Optimizing the BTMS control strategy becomes a potential solution to reduce energy consumption and overcome mileage issues. An appropriate system simulation model provides an effective tool to evaluate different BTMS control strategies. In this study, a predictive BTMS control strategy, which adjusts the heating or cooling thresholds dynamically utilizing the predicted battery states, is proposed. A simplified BTMS model implemented in the Geely brand EV is established using MATLAB/Simulink. The company provides the specifications associated with that model. The simulation results indicate the battery temperature variation and energy consumption under certain driving conditions. The simulation results show the predicted control strategy could reduce the thermal energy consumption in both high and low-temperature situations compared with the original control strategy. For the precise study, the control strategy can be upgraded when more comprehensive travel information is available in real EV applications and the BTMS model parameters can be optimized using more real experimental data.