Optimize Battery Control: A Multi-Objective Evolutionary Ensemble Reinforcement Learning Approach

Abstract

The Dynamically Reconfigurable Battery (DRB) systems, which use high-speed power electronic switches to dynamically adjust battery interconnections in real-time, are critical to the performance of the battery pack. Traditional battery management strategies often fail to address multi-objective optimization, leading to imbalanced performance and inadequate energy utilization. To enhance decision-making across multiple objectives, an Evolutionary Ensemble Reinforcement Learning (EERL) framework is proposed in this paper. This framework incorporates evolutionary algorithms to associate ensemble learning, thus improving reinforcement learning (RL) performance. It decomposes a complex objective into multiple sub-objectives, each optimized independently, while incorporating diverse performance metrics into the correlation stage to derive the Pareto optimal solution. The EERL can efficiently mitigate potential adverse effects such as short circuits, disconnections, and reverse charging, thereby effectively reducing capacity differences among various batteries. Simulations and real-world testing demonstrate that the proposed approach overcomes the issue of local optima entrapment in multi-objective optimization scenarios. In a real-world system, an 11.08 % increase in energy efficiency is observed compared to existing approaches.