Accurate prediction of the internal temperature field in batteries is of critical importance for ensuring safe operation, extending battery lifespan, and ultimately accelerating the global transition to electric vehicles (EVs). Unsafe thermal conditions not only degrade battery performance but also hinder EV adoption. However, conventional 3D thermal models involve complex computations that struggle to meet the fast computational speed requirements of Battery Management Systems (BMS), and traditional neural network models are not accurate enough for temperature field prediction, especially for low-gradient fields. To achieve precise and rapid prediction of the internal battery temperature fi... More >