基于粒子群算法的锂离子电池组风冷热管理系统优化

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2636/1/012006
Wenbo Wu
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引用次数: 0

摘要

近年来,锂离子电池吸引了众多研究者,其过热、燃烧和爆炸等安全问题不断进一步限制电池的应用场景。这些问题主要是由未优化的电池结构参数或冷却方法引起的。本文提出了一种采用粒子群算法设计高效风冷系统的综合方法,以寻找空气流量与电池温度之间的最优关系。首先,该方法通过粒子群算法调整优化后的空气流速,以保证电池温度最小、能耗最低;此外,该算法还可以在锂离子电池组半径等结构参数变化的情况下获得最优的空气流速。通过仿真验证了该方法的可行性。与以往的工作相比,该方法可以利用粒子群算法的连续调制,实现电池散热系统的最佳散热能力,同时实现电池散热系统的最低散热能耗。同时,模拟显示了整个电池组的温度变化。与以前的方法相比,这种集成方法可以为设计新型电池热管理系统提供更多动态热管理灵感。
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Optimization of an Air-cooling Thermal Management System for Lithium-ion Battery Packs via Particle Swarm Algorithm
Abstract Recently, lithium-ion batteries have attracted many researchers and their safety issues such as overheating, combustion and explosion continue to further limit battery application scenarios. These issues are mainly caused by unoptimized battery structure parameters or cooling methods. In this paper, an integrated approach has been proposed to design an efficient air-cooling system using the particle swarm algorithm to find an optimal relationship between air flow rate and battery temperature. Firstly, this method can adjust an optimized air flow rate to ensure that the battery temperature is minimized with the lowest energy consumption via the particle swarm algorithm. Additionally, an optimized air flow rate can still be obtained with the change of structure parameters such as the radius in a lithium-ion battery pack via this novel algorithm. Then, we demonstrate the feasibility of this integrated method in simulations. Compared with the previous work, this method can employ the continuous modulation of the particle swarm algorithm, realizing both the best cooling capacity of the battery cooling system and simultaneously the lowest energy consumption for cooling in cell heat regulation systems. Meanwhile, temperature variations of the entire cell pack are also shown in simulations. In contrast to previous approaches, this integrated method may provide more dynamic thermal management inspirations for designing novel battery thermal management systems.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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