Optimal design and control strategy for enhanced battery thermal management

Abstract

The Battery Thermal Management System (BTMS) plays a crucial role in the safety and performance of new energy vehicles. This study proposes an innovative cooling structure design that ingeniously combines the advantages of air cooling, water cooling, and Phase Change Materials (PCMs) to enhance the cooling efficiency of the battery system. Through numerical simulations, the effectiveness of this cooling system was validated and further supported by experimental evidence confirming the accuracy of the simulation results. Subsequently, this research utilized the Response Surface Methodology (RSM) to optimize key parameters of the cooling structure, including the fin height, gap length, and PCM thickness, with their optimal values determined to be 5.20 mm, 17.69 mm, and 3.38 mm, respectively. This parameter optimization work provides precise guidance for the design of battery thermal management systems, contributing to enhanced heat dissipation performance. To further enhance the intelligence of battery thermal management, this study introduced a novel neural network model based on Temporal Convolutional Networks (TCN) and Bidirectional Long Short-Term Memory (BiLSTM) with Multi-Head Attention (MHA). This model is capable of predicting temperature changes during the battery discharge process in real-time with high accuracy. Based on the prediction results, this research designed an efficient control strategy that, compared to a scenario without a control strategy, demonstrated a reduction in energy consumption by 20.87 %, a decrease in maximum temperature by 2.52 K, and a reduction in the maximum temperature difference by 0.05 K. These results not only prove the effectiveness of the control strategy but also provide a new direction for the optimization of battery thermal management systems.