# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
###### Abstract
Meta-heuristic optimization techniques have emerged as a prominent alternative to conventional optimization methods, particularly in complex multi-objective problems where the search space is large and the landscape is non-continuous [13; 14; 15]. These techniques are renowned for their adaptability and capability to escape local optima, making them suitable for the non-linear and intricate nature of BTMS. In the context of BTMS, meta-heuristic optimization techniques can provide dynamic solutions, adapting to various operational constraints and environmental conditions. These techniques, such as genetic algorithms (GAs) and particle swarm optimization (PSO) are capable of exploring a vast search space to find near-optimal solutions that balance various objectives like energy consumption, thermal comfort, environmental considerations, and cost-effectiveness [16; 17; 18; 19; 15]. Particle swarm optimization (PSO) and genetic algorithms (GAs) are popular optimization algorithms inspired by social-psychological behaviors, such as those seen in bird flocks and individuals. They are viewed as simpler and more intuitive compared to other heuristic optimization methods. For a similar example, a three-fluid cross-flow plate-fin heat exchanger with offset strip fins is optimized, focusing on maximizing hot fluid effectiveness and minimizing entropy generation [19]. Finite element method (FEM) is used to solve the governing equations and determine the rate of entropy generation and effectiveness. The optimization uses genetic algorithm (GA) and particle swarm optimization (PSO) to find optimal geometric parameters, achieving nearly identical design values for both objectives, with PSO being effective [19]. However, a limitation of these algorithms is their susceptibility to low optimization precision. This can result in rapid convergence to local minima, potentially leading to suboptimal solutions [19; 20; 21; 22; 23; 24]. To this end, the application of meta-heuristic techniques has garnered significant attention, aiming to maximize system performance, while considering complex design constraints. Previous studies have explored the utilization of particle swarm optimization and genetic algorithms for optimizing thermal devices. However, limited attention has been given to their suitability for large-scale devices or thermal systems. In recent studies focusing on thermal systems and battery pack thermal management, a notable drawback is the limited presentation of open platforms. To address this issue, leveraging recently developed tools such as Simulink and Simscape by MATLAB could prove to be an excellent solution [4; 25; 26]. These platforms offer numerous advantages and hold the potential to fill the research gap in thermal system simulations and battery pack thermal management. As an illustration of similar studies, several investigations, specifically concerning battery packs, have been conducted using Simulink. For instance, the simulation procedure involved applying pulse current-voltage conditions to lithium battery cells under various scenarios [5; 25; 26; 27]. The battery thermal model employed is based on electrochemical knowledge, where the voltage changes exhibits non-linear characteristics in response to the current input. As a result, certain simplifications are necessary, especially when considering factors like battery-related resistance correlation. The commonly used equivalent circuit model (ECM) model is used to establish the relationship between battery currents, voltages observed at the battery terminals, and coulomb accumulation. This includes accounting for open-circuit voltage based on current history, encompassing self-discharge, and current charge inefficiency [28; 29]. The ECM model effectively captures the phenomenological behavior of the battery and facilitates the estimation of the state of charge (SOC) through coulomb counting methods [5; 13; 29; 30].