Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures

Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase rate and consistency. In this strategy, electrothermal films are placed between cells for preheating; battery module areas are differentiated according to the convective heat transfer rate; a controller regulates heating power to control the maximum temperature difference during the preheating process; and a co-simulation model is established to verify the proposed warm-up strategy. The numerical calculation results indicate that the battery module can be preheated to the target temperature under different ambient temperatures and control targets. The coupling relationship between the preheating time and the maximum temperature difference during the preheating process is studied and multi-objective optimization is carried out based on the temperature increase rate and thermal uniformity. The optimal preheating strategy is proven to ensure the temperature increase rate and effectively suppress temperature inconsistency of the module during the preheating process. Although preheating time is extended by 17%, the temperature difference remains within the safety threshold, and the maximum temperature difference is reduced by 49.6%.