Structure Optimization of Battery Thermal Management Systems Using Sensitivity Analysis and Stud Genetic Algorithms

Battery thermal management systems (BTMS) are hugely important in enhancing the lifecycle of batteries and promoting development electric vehicles. The cooling effect BTMS can be improved by optimizing its structural parameters. In this paper, flow resistance heat dissipation models were used to optimize structure BTMS, which more efficient than computational fluid dynamics method. Subsequently, five parameters that affect temperature inside battery pack analyzed using single-factor sensitivity analysis under different inlet airflow rates, three selected as constraints a stud genetic algorithm. algorithm, maximal difference obtained model was within 5K constraint function, where objective function minimized overall area pack. optimized algorithm reduced 16% saved 6% package compared with original BTMS. It concluded combined network quickly efficiently air-cooled improve performance.