计算机冷却
相变
相变材料
粒子群优化
电池组
电池(电)
电子设备和系统的热管理
优化设计
汽车工程
工艺工程
计算机科学
机械工程
工程类
物理
算法
功率(物理)
机器学习
量子力学
工程物理
作者
Guiqi Hou,Lisheng Ye,Changhong Wang,Xianqing Liu,Wenxuan He,Xiaoxing Zeng
标识
DOI:10.1016/j.est.2023.108936
摘要
Battery optimization models can extend battery life, improve safety, and maximize battery performance. Previous optimization methods focus more on (Computational Fluid Dynamics) CFD and data mining. However, the two optimization methods apply only to specific battery module structures. In addition, previous studies have focused more on improving optimization algorithms to improve performance, but the computational design of large-scale battery thermal management systems still requires large loads. This paper presents a new optimization method based on the thermal equivalent circuit model (TECM) of battery thermal management system (BTMS) for the efficient design of liquid-cold coupled phase change material BTMS (PCM). The method can easily change and optimize the battery module structure according to different application scenarios. Compared with the reference structure, the TECM Parto solution optimized combined with multi-objective particle swarm optimization (MOPSO) saved 39.51 % of the liquid cooling energy consumption, reduced the maximum temperature rise of BTMS by 0.38 °C, and reduced the maximum temperature difference by 19.81 %. The method adopted can improve the efficiency and quality of BTMS design, reduce the cost of trial and error, and speed up the design process.
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