Multi-objective optimization of lithium-ion battery pack thermal management systems with novel bionic lotus leaf channels using NSGA-II and RSM

This study employs a multi-objective optimization approach integrating the fast non-dominated sorting genetic algorithm (NSGA-II) and response surface methodology (RSM) to enhance the performance of battery thermal management systems (BTMS) through the design and optimization of a novel bionic lotus leaf (NBLL) channel. Heat generation rates, obtained from lithium-ion battery (LIB) testing experiments conducted under various discharge rates, along with design variables such as channel spacing, width, angle, and mass flow rate, are optimized. The objective functions, comprising maximum temperature difference, heat transfer coefficient, and pressure drop, are optimized while adhering to a maximum temperature constraint. Optimal Latin Hypercube Sampling (OLHS) is utilized for selecting design points, and RSM constructs objective function expressions. The optimal combination is determined through the Pareto optimal frontier generated by NSGA-II. Relative to the initial model, the optimized design demonstrates a reduction in the maximum temperature difference by 14.898 %, an increase in the heat transfer coefficient by 35.786 %, and a decrease in the pressure drop by 68.325 %. This optimized BTMS design significantly enhances heat dissipation performance, which is crucial for battery performance, longevity, and safety in the realm of battery thermal management.