Computer-aided thermal energy storage research of electric vehicle battery thermal management system

This paper constructs an optimization framework that integrates thermodynamics and machine learning, and proposes an improved deep reinforcement learning thermal management algorithm. By establishing a heat generation correction formula with SOC influence, a 3-D thermal resistance network model, etc., combined with COMSOL simulation (temperature error <3?) and 18650 battery pack experiments, the algorithm advantages are verified: the response lag is reduced to within 0.5 second, the control deviation is ?1?, the temperature difference is reduced by 56.9% compared with the traditional PID, and the energy consumption is reduced by 18%, providing a new path for safe and efficient operation of batteries.