Multi-objective optimization of parallel flow immersion cooling battery thermal management system with flow guide plates based on artificial neural network

IF 6.1 2区工程技术 Q2 ENERGY & FUELS

Applied Thermal Engineering Pub Date : 2025-05-14 DOI:10.1016/j.applthermaleng.2025.126833

Zhiguo Tang, Xinghao Li, Yan Li, Jianping Cheng

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引用次数: 0

Abstract

In order to ensure the safe and stable operation of a lithium-ion battery energy-storage system within an appropriate temperature range, it is essential to design a battery thermal management system. A novel parallel-flow immersion-cooling battery thermal management system with flow guide plates is proposed, and the physical and computational models of the battery thermal management system are established. It is found that compared with a battery thermal management system without flow guide plates, a battery thermal management system with flow guide plates can significantly reduce the maximum temperature and maximum temperature difference of the battery. Moreover, as the ring width between the flow guide plate and the battery decreases, the number of the flow guide plates or the inlet velocity of the coolant increases and both the maximum temperature and maximum temperature difference of the battery decrease. However, as the height of the single flow guide plate or spacing of the flow guide plates increases, both the maximum temperature and maximum temperature difference of the battery show a trend of first decreasing and then increasing. Also, an artificial neural network model is adopted to perform multi-objective optimization on these structural and flow parameters, and the optimal structure is determined. Compared with conventional series-flow immersion-cooling battery thermal management system, under laminar-flow conditions, the flow pressure drop of the optimized series-flow immersion-cooling battery thermal management system increases slightly, while the maximum temperature and maximum temperature difference of the battery decrease, respectively, which is superior to the thermal management indices of immersion-cooling battery thermal management systems reported in existing literature. This provides a feasible solution for thermal management of energy-storage batteries.

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基于人工神经网络的导流板并联浸没式电池热管理系统多目标优化

为了保证锂离子电池储能系统在合适的温度范围内安全稳定运行，必须设计电池热管理系统。提出了一种新型的带导流板的平行流浸没冷却电池热管理系统，并建立了该系统的物理模型和计算模型。研究发现，与不带导流板的电池热管理系统相比，带导流板的电池热管理系统可以显著降低电池的最高温度和最大温差。随着导流板与电池之间环宽的减小，导流板数量或冷却剂进口速度增加，电池的最高温度和最大温差减小。但随着单个导流板高度或导流板间距的增加，电池的最高温度和最大温差均呈现先降低后升高的趋势。采用人工神经网络模型对结构参数和流量参数进行多目标优化，确定最优结构。与传统的串流式浸没冷却电池热管理系统相比，在层流条件下，优化后的串流式浸没冷却电池热管理系统的流动压降略有增大，电池的最高温度和最大温差分别减小，优于现有文献报道的浸没冷却电池热管理系统的热管理指标。这为储能电池的热管理提供了一个可行的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Thermal Engineering 工程技术-工程：机械

CiteScore

11.30

自引率

15.60%

发文量

1474

审稿时长

57 days

期刊介绍： Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application. The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.