Pure electric vehicles have a variety of benefits such as energy efficiency, zero environmental emissions, elimination in air pollution, and decreased carbon dioxide emissions. While it offers major benefits, it suffers from numerous battery-related issues, and, among them, heat dissipation is considered to be a major challenge, leading to significant performance degradation if not handled properly. In this present work, a battery thermal management system design is presented using ANSYS Fluent and adaptive elephant herding optimization algorithm for optimizing the battery spacing, reducing the heat dissipation, and ensuring a proper battery temperature in the lithium-ion battery pack. The adaptive elephant herding optimization algorithm provides an optimal battery spacing of (17, 23, 21, 0.23, 0.23, 0.174, 0.174), and the maximum temperature, minimum temperature, and temperature difference values observed are 298.3112 K, 292.9874 K, and 5.47 K, respectively. The research findings show that the adaptive elephant herding optimization algorithm works as an appropriate cost-effective strategy for depicting the influence of the battery spacing towards the battery temperature and results in a uniform cooling of the entire battery pack.

纯电动汽车具有多种优势，例如能效，零环境排放，消除空气污染和减少二氧化碳排放。尽管它提供了很多好处，但是它却遇到了许多与电池有关的问题，其中，散热被认为是一项重大挑战，如果处理不当，则会导致性能严重下降。在本工作中，提出了一种使用ANSYS Fluent和自适应象群优化算法的电池热管理系统设计，以优化电池间距，减少散热并确保锂离子电池组中的电池温度合适。自适应大象放牧优化算法可提供（17、23、21、0.23、0.23、0.174、0.174）的最佳电池间距和最高温度，最低温度和观察到的温差值分别为298.3112 K，292.9874 K和5.47K。研究发现表明，自适应大象群优化算法可作为一种合适的成本有效策略来描述电池间距对电池温度的影响，并导致整个电池组的均匀冷却。