Design optimization of staggered-arranged battery thermal management system using an integrated approach of physics-based simulations and evolutionary algorithms

The widely used Lithium-ion battery cells (LIBs) are very sensitive to the operating temperature, which impacts the cycle life and sometimes even leads to thermal overshoot. Hence, a battery thermal management system (BTMS) is required to maintain the temperature of the batteries within the optimum range and maintain the temperature uniformity among the battery pack. A BTMS can be configured in different architectures and either the air or a liquid can be used as a heat transfer fluid. Finding an optimum architecture configuration of BTMS is challenging which requires an economical solution based on comprehensive numerical parametric investigation and multidisciplinary design optimization. In addition, there are lot of uncertainties in design variables (inputs) which could affect the thermal performance of the battery. Therefore, this work proposes a comprehensive study on the design optimization of staggered-arranged BTMS air cooling system using an integrated approach of physics-based simulation (computational fluid dynamics simulations), parametric optimization using statistical design of experiments and evolutionary algorithms such as genetic algorithm (GA), and particle swarm optimization (PSO). The proposed approach incorporates the findings obtained from physics-based simulations in evolutionary algorithms to guide the solutions to obtain efficient and close-to-realistic solution. The numerical model has been mathematically validated using a variety of empirical heat-transfer correlations, and the outcomes are in good concurrence with one another. The comparative analysis investigation reveals that GA outperformed PSO and the optimization process yielded significant improvements including 0.627 % reduction in maximum temperature, 49.18 % decrease in maximum temperature difference, 102.379 % increase in pressure drop, and 6.804 % increment in volume. Conclusions are made and research recommendations are proposed for the future work.