Research on thermal characteristic optimization and prediction of power batteries based on composite cooling

Abstract

Thermal runaway of power batteries is a critical bottleneck restricting the development of electric vehicles. To improve the safety and operational reliability of electric vehicle power batteries, this study proposes a battery thermal management model with an air-liquid coupled heat dissipation structure and conducts optimization design and predictive analysis on its thermal performance characteristics. Firstly, a novel air-liquid coupled heat dissipation model is constructed with 21700 lithium-ion batteries as the research object. Then, numerical analysis of the temperature field characteristics of the battery heat dissipation model is performed based on fluid mechanics theory. The influences of structural and operating parameters, including the contact angle between the battery and aluminum tube, coolant flow direction, coolant flow rate, and cooling air velocity, on the heat dissipation performance are investigated via orthogonal experiments. Subsequently, the TOPSIS method is employed to comprehensively evaluate multiple heat dissipation performance indicators, and the overall performance of each design scheme is determined according to normalized scores. Finally, a BP neural network prediction model for the heat dissipation characteristics of the model is established to realize high-efficiency prediction of the scheme’s heat dissipation performance. The results show that Orthogonal Scheme 16 achieves a maximum battery temperature of 30.02°C and a maximum temperature difference of 4.54°C, both satisfying the temperature limit constraints for power battery modules. The normalized scores calculated by the TOPSIS method can accurately reflect the relative merits of each experimental scheme. In addition, the BP neural network presents obviously higher prediction accuracy than the multiple linear regression model, with reliable and precise prediction performance.