Enabling Safe Fast Charging of Lithium-Ion Batteries via a Simulation-Trained Digital Twin

Fast charging of lithium-ion batteries is essential for accelerating a widespread use of electric vehicles; however, its adoption significantly increases battery thermal stress and the risk of thermal runaway, particularly in aged cells. This study proposes a sim-ulation-trained digital twin (DT) framework for probabilistic assessment of thermal runaway and critical charging current estimation under fast charging conditions. A dataset is generated using an electrochemical–thermal Single Particle model, varying current rate, capacity, and internal resistance, then, an encoder–decoder neural net-work architecture is developed to map and convert static operating conditions into dynamic temperature evolution, enabling efficient surrogate modeling of thermal be-havior. The proposed methodology provides a computationally efficient tool for risk-aware fast-charging strategies which can be integrated into battery management systems for enhanced safety. While the current study is applied to specific single cell chemistry and simulation-based training, the framework can be easily extended to other battery systems and operating conditions.