# 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 \({}^{\circ}\)C compared to the initial temperature. b 2023 2  
aimed at advancing cooling technologies [6; 7; 8; 9]. [PERSON] et al. [10] investigated this area by developing a Li-ion battery thermal management system using air cooling under high discharge rates. By utilizing computational fluid dynamics (CFD), they tracked temperature patterns in the air and throughout the battery pack at varying airflow rates. [PERSON] and associates [11] extended this line of inquiry by creating a sub-model for a three-dimensional battery pack and applied simulation methods to assess the thermal behavior of battery cells under convective heat exchange scenarios. Their work involved analyzing the impact of different airflows over the battery pack and confirming the thermal consistency within the cells. Furthermore, a combination of computational and practical experiments were undertaken to evaluate the use of heat pipes with lithium-ion battery cells [12]. This line of research focuses on heat transference from the battery cells to a heat sink and tests a battery pack system that integrates passive cooling strategies for potential future applications. The system in question comprises passive cooling units that can remove heat from individual battery cells and heat pipe segments that are capable of managing heat over extended distances and dispelling intense heat loads. While current research trends heavily emphasize the examination of singular thermal devices, this focused approach does not often lead to a meaningful improvement in the thermal performance or efficiency of the systems. This issue is particularly evident in the case of electric vehicles, where aspects such as the operation of the liquid pump and properties of the ethylene glycol solution become more critical. The understanding of the entire thermal system, including the battery's operating conditions, emerges as an urgent concern. Such an approach demands an integration between diverse components and processes, acknowledging the complex interplay that governs the thermal behavior within these systems. In the ever-evolving era of electric vehicles, this comprehensive perspective between the optimization of thermal system is crucial, underscoring the need for a nuanced appreciation of both individual elements and their synergistic interactions. The meta-heuristic optimization method is widely used for the optimization of thermal devices.