Multi-objective optimization of cold plate with spoiler for battery thermal management system using whale optimization algorithm

IF 6.1 2区工程技术 Q2 ENERGY & FUELS

Applied Thermal Engineering Pub Date : 2024-11-19 DOI:10.1016/j.applthermaleng.2024.124974

Zeyuan Peng , Jianxun Huang , Junshuai Lv , Jiedong Ye

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引用次数: 0

Abstract

With the high population of electric vehicle adoption, precisely controlling the temperature of the battery modules is essential to provide long-term sustainability and reliability. In the amount of battery thermal management techniques, adding spoilers is a promising method for enhancing the heat transfer performance of cold plates, but the performance of the system is highly sensitive to different geometry parameters. In this study, heat transfer performance for the cold plates battery thermal management system with a tesla flow channel was numerically and experimentally investigated with a focus on the geometry parameters, including spoiler length (L), mounting position (X), and coolant flow rate (ν). A modified heuristic-based swarm intelligence multi-objective optimization algorithm is proposed to obtain the optimal spoiler parameters. The results show the maximum temperature of the battery module is reduced by 3.12 ℃ after adding the spoiler. The optimization spoilers maintain the maximum temperature of the battery module below 30.9 ℃, and the best spoiler parameters as L = 5.10 mm, X = 14.3 mm, and v = 1.186 × 10^-2 m/s. This study uses a heuristic-based swarm intelligence multi-objective optimization algorithm to investigate the optimization process of the spoiler structural parameters and provides guidance for the application of advanced multi-objective optimization algorithms in cold plate design.

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使用鲸鱼优化算法对电池热管理系统中带有扰流板的冷板进行多目标优化

随着电动汽车的普及，精确控制电池模块的温度对于提供长期可持续性和可靠性至关重要。在大量的电池热管理技术中，添加扰流板是提高冷板传热性能的一种很有前途的方法，但该系统的性能对不同的几何参数非常敏感。本研究对带有特斯拉流道的冷板电池热管理系统的传热性能进行了数值和实验研究，重点研究了扰流板长度（L）、安装位置（X）和冷却剂流速（ν）等几何参数。为获得最佳扰流板参数，提出了一种基于启发式的群智能多目标优化算法。结果表明，加装扰流板后，电池模块的最高温度降低了 3.12 ℃。优化后的扰流板使电池模块的最高温度保持在 30.9 ℃ 以下，最佳扰流板参数为 L = 5.10 mm，X = 14.3 mm，v = 1.186 × 10-2 m/s。本研究采用基于启发式的蜂群智能多目标优化算法研究了扰流板结构参数的优化过程，为先进的多目标优化算法在冷板设计中的应用提供了指导。

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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.