A novel control strategy for active battery thermal management systems based on dynamic programming and a genetic algorithm

Abstract

An appropriate active battery thermal management system (ABTMS) control strategy should be able to extend battery life, improve battery capacity consistency and reduce system energy consumption; however, these three purposes are mutually conflicting. To address this conflict, this paper proposes representing the control of the ABTMS as a dynamic multiobjective control problem, with the battery state of health (SOH), battery SOH difference and system energy consumption as the optimization objectives and the airflow rate and refrigeration power as the control variables. Weight coefficients are proposed to couple the multiple objectives. Next, a combined control strategy comprising dynamic programming and a genetic algorithm is developed to obtain the optimal weight coefficient set and calculate the optimal path of the ABTMS that minimizes the total cost of the multiple objectives. Experimental results indicate that the combined strategy effectively balances the three optimization objectives to comprehensively optimize performance. Compared with the performance of a rule-based multiparameter control strategy, the proposed combined control strategy decreases the battery capacity attenuation rate by 3.7%, saves system energy consumption by 22.4%, and improves battery capacity consistency by 23.3%. These findings provide a basis for more efficient and intelligent control strategies for ABMTSs.