Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink

Abstract

This study examines the use of meta-heuristic algorithms, specifically particle swarm optimization and genetic algorithms, for optimizing thermal systems, addressing a research gap on their efficacy in larger systems. Utilizing MATLAB’s Simulink and Simscape, the research initially targets an electric vehicle thermal system model, emphasizing the optimization of a Li-ion battery pack and associated cooling components, like chillers, pumps, and cooling plates, during operation. One consideration is the use of a glycerol–water mixture in the chiller pump, which demands the use of an optimal control algorithm that adjusts to outdoor temperatures and control strategies. This study focuses on computational efficiency reflecting the complexity of system simulations. Challenges related to applying particle swarm optimization and genetic algorithms to these systems are scrutinized, leading to the establishment of a new objective function to pinpoint target values for system optimization. This research aims to refine design methodologies for engineers by harmonizing optimal design with computational expediency, thereby enhancing the engineering design process in thermal management. This integrative approach promises to yield practical insights, benefiting engineers dedicated to the advancement of thermal system design and optimization. The results show that, compared to the base model, 1% of the overall state of charge could be saved, and the battery temperature could also be cooled by more than 4 °C compared to the initial temperature.

Keywords

• battery pack
• electric vehicle
• meta-heuristic optimization
• genetic algorithm
• thermal system optimization
• particle swarm optimization