Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper proposes a novel reconfigurable battery balancing topology and reinforcement learning-based intelligent balancing management system. The different degradations cause a significant loss of battery pack available capacity, as the pack power output relies on the weakest cell due to the relevant physical requirements. To handle this capacity drop issue, a reconfigurable battery topology is adopted to improve the usability of the heterogeneous battery. There are some existing battery reconfigurable topologies in the literature. However, these studies rely on the limited options of topology designs, and there is a lack of study on the reconfigurability of these designs and other possible new designs. Also, it is rare to find an optimal management system for the reconfigurable battery topology. To fill these research gaps, this paper explores existing battery reconfigurable topology designs and proposes a new reconfigurable topology for battery balancing. Besides, the battery reconfigurability problem is modeled as an optimization problem, and the balancing time and total power output are modeled as objective functions. Then, a reinforcement learning-based intelligent management system is proposed to identify the best battery topology for minimizing equalization time and battery degradation for heterogeneously degraded batteries. The simulation results show that the proposed method can effectively balance the inhomogeneous battery cell and alleviate battery degradation.</div></div>