# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
###### Abstract
In addition, an example of the thermal management of a battery pack in an EV model from the Simulink database is provided, along with a suitable approach for configuring a thermal system in a liquid-cooled battery pack that is currently in operation. Consequently, by incorporating PSO and GA algorithms into these hybrid thermal systems that blend model-based and non-model-based approaches, researchers can anticipate gaining a more intuitive understanding of the optimal design direction for the thermal system. Addressing this research gap, this study focuses on the application of meta-heuristic techniques for optimizing thermal systems, specifically targeting the design of components in electric vehicles. Electric vehicles rely on advanced Li-ion battery packs, and the effective cooling of these battery packs is essential for their performance and longevity. Therefore, optimizing the design of components, such as the chiller, pump, and cooling plate becomes paramount during vehicle operation operated in Simulink. Therefore, the main objective of this simulation study is to investigate the fundamental principles governing thermal devices, with a primary focus on the ultimate goal of becoming fully operational thermal systems. Throughout the engineering process of a thermal system, the quest for an optimal design remains a continuous pursuit, regardless of theoretical equations. This research explores the intricate relationship between thermal devices and thermal systems in Simulink operated by MATLAB, elucidating how the realization of a thermal system culminates in the attainment of the desired optimal design. The study draws upon various case studies and theoretical analyses to demonstrate the significance of this engineering objective. By comprehending the interplay between thermal devices and thermal systems, we can optimize designs to maximize efficiency and performance. This research began as an exploration of whether meta-heuristic algorithms could be applied in Simulink. To directly compare PSO and GA algorithms, we aimed to stay as close as possible to the \"fundamental\" and \"original\" algorithms in terms of their direction. Consequently, our study did not involve modifications to the algorithms, and only pre-designed algorithms were utilized. Additionally, the applicability of a genetic algorithm (GA) and particle swarm optimization (PSO) are explored for optimizing a thermal system simulated using Simulink. These two popular optimization algorithms have long been utilized by engineers to enhance system performance. To evaluate their suitability for thermal systems, a battery pack is chosen from an electric vehicle as a representative case, utilizing a pre-existing Simulink model provided by MATLAB. By applying GA and PSO to this model, we assess their effectiveness in determining the optimal thermal management strategies for the battery pack. The study aims to identify the most appropriate algorithm for enhancing the thermal performance of the battery pack and the role that each thermal management device plays in achieving this objective. Through this investigation, we can contribute valuable insights into the selection of optimization algorithms for thermal systems and their implications in forward-thinking designs.