A novel optimization method based on inverse calculation model for efficient design of battery thermal management system

Air-cooled parallel battery thermal management system (BTMS) is universally adopted in electric vehicles to ensure that temperature of battery packs locates within an optimum range. Thus, design of air-cooled BTMS to enhance its cooling performance is in need. In this study, an optimization method based on inverse flow resistance network model is proposed for efficient design of parallel BTMS. The inverse flow resistance network model is developed by taking structural parameters as the variables to be solved and supplementing constraint equation in the original flow resistance network model. Based on this inverse model, structural parameters of the system can be directly obtained once the flow rates among parallel channels are given. Furthermore, regulation of flow rates among parallel channels towards optimal distribution is carried out, which leads to optimal structural parameters of the system with the aids of the inverse calculation model. The proposed method is adopted to optimize the width distribution of parallel channels in BTMSs with different flow patterns. The results show that the temperature difference in battery pack after the optimization is reduced by at least 55%. Furthermore, the present optimized systems achieve better cooling performance than those in the previous study. Experiments are conducted to further demonstrate the effectiveness of the optimized systems for cooling performance improvement. The developed inverse model discloses the quantitative relationship between the flow rate distribution and structural parameters, and the proposed method based on this model optimizes the parallel BTMSs by searching the optimal flow rate distribution, which avoids the implement of repeated adjustments of structural parameters and the introduction of randomness operators in previous methods. The developed optimization is anticipated to guide the efficient design of parallel BTMS for enhancement of system performance.