Intelligent temperature control framework of lithium-ion battery for electric vehicles

Abstract

The heat generated during battery charging and discharging induces rapid temperature rise, potentially affecting battery performance and safety. Coolant flow rate control has been used to regulate battery temperature to address this. However, traditional battery temperature control strategies have difficulty balancing temperature control accuracy and system response speed. Thus, an intelligent temperature control framework employing two control strategies: Fuzzy Logic Control (FLC) and Reinforcement Learning Control (RLC), is proposed in this paper. Meanwhile, a single-valve temperature control loop based on FLC and a double-valve temperature control loop based on RLC is designed in the framework. Moreover, an intelligent decision method is proposed to select the appropriate control strategy for each operation stage to achieve intelligent control. The results indicate that, compared with the traditional PID control strategy, the response time decreased from 361 s to 225 s by FLC, and the temperature difference decreased from 5.33 K to 2.36 K by RLC. The performance of the temperature control strategy for liquid cooling has been significantly improved.