A digital twin in battery management systems (BMS) serves as a virtual counterpart of a physical battery system. It leverages real-time data, sophisticated algorithms, and machine learning to emulate and forecast the battery's behavior and performance. This digital construct facilitates advanced monitoring, diagnostics, and prognostics, thereby enhancing battery lifespan, safety, and efficiency. By incorporating a digital twin, BMS can optimize the charging and discharging processes, anticipate maintenance requirements, and improve overall energy management. Our group focuses on optimizing the capabilities and performance of battery management systems (BMS) using advanced algorithms. Additionally, we aim to establish high-performance digital twins to achieve more sophisticated and efficient control of battery systems.
