设计一个离子电池冷却系统,结合PCM,热管和液体电路,使用海洋捕食者算法增强的ANN和多向优化
Naim Ben Ali1, Borhen Louhichi2, Waqed H Hassan3
1Department of Industrial Engineering, College of Engineering, University of Ha'il, 81451, Ha'il City, Saudi Arabia.
Scientific reports
|March 2, 2026
概括
本研究提出了一种使用机器学习和优化来改进离子电池热管理系统 (BTMS) 的混合方法. 该研究为增强电池安全性和性能提供了实际的设计建议.
科学领域:
- 电池技术 电池技术
- 热管理系统 热管理系统
- 机器学习应用 机器学习应用
背景情况:
- 离子电池的热管理对于安全性,效率和寿命至关重要.
- 过度的温度和不均的热量分布带来了重大挑战.
- 现有的方法需要先进的解决方案,以有效控制热量.
研究的目的:
- 为推进电池热管理系统 (BTMS) 开发结构化混合方法.
- 整合机器学习,优化和多标准评估用于热设计.
- 为各种电池应用提供实用设计建议.
主要方法:
- 一个四阶段的框架:数据预处理,混合预测建模 (GA-MLPNN,MPA-MLPNN),设计变量优化 (MOMVO) 和决策分析 (CoCoSo).
- 开发和对两种混合机器学习方案进行比较评估,用于热和性能指数预测.
- 多目标优化,分析热性能和能量密度之间的权衡.
- 多标准评估,将最佳解决方案转化为可操作的设计选择.
主要成果:
- MPA-MLPNN显示了温度差异的高精度 (ΔT > 0.9985).
- 在高峰温度 (Tmax > 0.9986) 和能量密度 (ED > 0.999) 上,GA-MLPNN 显示出卓越的可靠性.
- 优化发现了权衡:较厚/较高的设计减少了热点 (ΔT ≈ 1.8-2.5°C,Tmax ≈ 38-39°C),但降低了ED (≈ 139-144 Wh/kg),而较薄的设计增加了ED (≈ 157 Wh/kg),以牺牲热稳定性.
结论:
- 混合方法有效地解决了电池热管理方面的挑战.
- 设计几何学显著影响热调节,特定尺寸提供平衡的性能.
- CoCoSo 方法成功地将复杂的权衡转化为各种运营优先事项的实用,可适应的设计建议.
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