Analysis and prediction of battery temperature in thermal management system coupled SiC foam-composite phase change material and air

Abstract

Lithium-ion batteries crucially rely on an effective battery thermal management system (BTMS) to sustain their temperatures within an optimal range, thereby maximizing operational efficiency. Incorporating bio-based composite phase change material (CPCM) into BTMS enhances efficiency and sustainability. This study commences by blending lauric acid and myristic acid in a 7: 3 mass ratio to synthesize a bio-based CPCM, following which the thermophysical properties of this CPCM are tested. Subsequently, the CPCM is integrated into a SiC foam and coupled with air to develop a novel BTMS. Then the effects of air velocity and initial temperature on the temperature of the battery pack are analyzed. Finally, the RIME-Convolutional Neural Network (CNN)- Self-Attention (SA)- Gated Recurrent Unit (GRU) model is established to predict the temperature of the battery in this BTMS. The results indicate that the latent heat of the CPCM is 97.98 J/g, melting at 35 °C upon heating. The incorporation of SiC foam-CPCM effectively reduces battery temperatures. When the air velocity is set at 3 m/s, the battery temperature remains below 40 °C during a discharge rate of 2C. Notably, the CPCM melts at higher initial temperatures, effectively mitigating the temperature rise in the battery pack. The RIME-CNN-SA-GRU model exhibits remarkable accuracy, with a maximum prediction error of 0.56 °C and a RMSE of 0.23, precisely capturing the trend of a sharp temperature rise at the end of discharge. This study presents an efficient solution for lithium-ion battery thermal management and temperature prediction.