Research on multi-objective control strategy of thermal management system of pure electric vehicle at low temperature based on Q-learning algorithm

In low-temperature conditions, a reasonable control strategy for thermal management systems can effectively alleviate range anxiety in pure electric vehicles and improve their adaptability to various working conditions. To further enhance the adaptability of thermal management system control strategies in different working conditions, this paper proposes a multi-objective control strategy based on Q-learning algorithm. Firstly, a pure electric vehicle model based on power-thermal coupling is established. The accuracy of the model is validated by comparing the simulation results from combined Amesim and Matlab/Simulink simulations with experimental data. Secondly, taking into consideration the factors such as vehicle economy, powertrain performance, and cabin comfort, a novel control strategy utilizing the Q-learning algorithm for the thermal management system of pure electric vehicle is developed. Finally, the efficacy of Q-learning control strategy is analyzed by simulations conducted under NEDC and WLTC conditions, with an initial temperature of −20°C. The results showed that, compared to the rule-based control strategy in WLTC and NEDC working conditions, the comprehensive improvement effect of Q-learning control strategy is 9.35% and 10.76% respectively. Moreover, the Q-learning control strategy achieves 94.25% and 90.19% of the global optimal control effect obtained through DP. The results indicate that the proposed control strategy has good adaptability to different working conditions.