Deep Reinforcement Learning for Cell Balancing in Electric Vehicles With Dynamic Reconfigurable Batteries
Silvio Baccari, Massimo Tipaldi, Valerio Mariani
Abstract
Cell balancing is used in battery systems to guarantee uniform charge and discharge of their cells during operations, and aims at improving the performance of the whole battery pack. Onboard battery performance and lifespan are particularly important in Electric Vehicles (EVs), since they have a direct impact on their autonomy. This paper proposes a Deep Reinforcement Learning (DRL)-based framework for Dynamic Reconfigurable Batteries (DRBs), where the capability of dynamically reconfiguring their cell topology can be exploited to attain cell balancing in EV applications. Thanks to the model-free nature and the robustness/adaptability properties of DRL-based solutions, the resulting trained agent is able to reach DRB cell balancing, while also taking into account both operational and modelling aspects of the combined EV-DRB system (e.g., all the events included in a typical driving cycle, the EV regenerative braking, and the heterogeneity of the DRB cells). The training process is carried out by using standard driving cycles, while the resulting trained agent is validated with a dataset based on real driving profiles. Finally, the performance achieved by the DRL policies is compared with some heuristic/rule-based approaches inspired from the literature.