AI-assisted reconfiguration of battery packs for cell balancing to extend driving runtime

State of charge (SoC) cell balancing is one of the most important roles of battery management systems (BMS). The performance and lifespan of a battery pack can be significantly degraded and reduced by the presence of imbalance in cells SoC. Recently, we have shown that using a machine learning driven battery pack reconfiguration technique based on a network of controllable switches, one can periodically change the battery pack topology to effectively achieve better cell SoC equalization. As a result, the driving runtime achieved with a better balanced battery pack is increased. In this paper, we build on these promising results and investigate novel machine learning models used for prediction of the best battery pack topologies used during reconfiguration. In addition, to study the scalability of the proposed battery reconfiguration technique, we conduct our study on a battery pack with double the number of cells. For validation, we developed an in-house custom battery pack simulation tool that integrates state-of-the-art battery cell models and extended Kalman filtering (EKF) algorithms for SoC state estimation. Simulation results using several battery discharging workloads demonstrate that the machine learning algorithms can achieve better prediction accuracy compared to previous work, thereby resulting in better cell balancing, which in turn translates into up to 22.4% longer battery runtime.