计算机冷却
冷却液
电池(电)
材料科学
热的
相变材料
核工程
水冷
相变
热失控
机械工程
汽车工程
流量(数学)
体积流量
遗传算法
能量(信号处理)
电子设备和系统的热管理
优化设计
工作温度
主动冷却
热能
人工神经网络
混合动力系统
两相流
相变存储器
相(物质)
液相
计算机模拟
能源管理
热力学
计算机科学
模拟
作者
Danxing Zheng,Xu Chen,Xinyue Han,Ningning Chu
标识
DOI:10.1016/j.applthermaleng.2026.130103
摘要
To address critical thermal management challenges in high-capacity cylindrical lithium-ion batteries under high discharge rates, this study proposes a novel hybrid battery thermal management system (BTMS) that synergistically integrates liquid cooling (LC), vapor chambers (VCs), and phase change material (PCM). The innovation lies in a strategic dual-VC layout specifically designed for cylindrical batteries to significantly enhance temperature uniformity. Numerical simulations evaluate various configurations, and a multi-objective optimization framework based on artificial neural network (ANN) and genetic algorithm (GA) is developed. Results indicate that the LC-2VCs-PCM system significantly reduces battery temperature and improves uniformity. Under a 4C discharge rate with 0.02 m/s coolant flow velocity, it maintains a maximum temperature ( T max ) of 309.53 K and a maximum temperature difference (∆ T max ) of 4.56 K. These values represent improvements of 27.79 K and 25.16 K, respectively, over conventional liquid cooling. The optimized design achieves a system energy density ( ED ) of 132.12 Wh/kg, a 7.1% improvement, while reducing ∆ T max by 21% at reduced flow rate. This dual enhancement underscores the effectiveness of the proposed optimization approach in delivering superior thermal regulation and energy efficiency. Furthermore, by maintaining temperatures within a safe and uniform range, this design significantly advances battery safety by mitigating thermal runaway risks and preventing premature degradation. This study provides valuable insights for designing advanced hybrid cooling solutions. • VC-PCM liquid cooling system improves thermal management for cylindrical batteries. • LC-2VCs-PCM obtains T max (309.53 K) & Δ T max (4.56 K) under 4C discharge at 0.02 m/s. • ANN-GA framework optimizes system design for both thermal and energy performance. • 27.79 K reduction in T max and 25.16 K in Δ T max vs. conventional liquid cooling. • Optimal design boosts energy density by 7.1% & cuts Δ T max by 21% at low flow rate.
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