储能科学与技术 ›› 2023, Vol. 12 ›› Issue (7): 2155-2165.doi: 10.19799/j.cnki.2095-4239.2023.0152

• 储能锂离子电池系统关键技术专刊 • 上一篇    下一篇

锂离子电池组液冷式热管理系统的设计及优化

刘书琴1(), 王小燕2(), 张振东2, 段振霞2   

  1. 1.江苏省盐城技师学院,江苏 盐城 224000
    2.上海理工大学,上海 200093
  • 收稿日期:2023-03-09 修回日期:2023-04-07 出版日期:2023-07-05 发布日期:2023-07-25
  • 通讯作者: 王小燕 E-mail:434759407@qq.com;xiaoyan_wang@usst.edu.cn
  • 作者简介:刘书琴(1971—),女,硕士,高级讲师,从事汽车新能源技术开发,E-mail:434759407@qq.com

Experimental and simulation research on liquid-cooling system of lithium-ion battery packs

Shuqin LIU1(), Xiaoyan WANG2(), Zhendong ZHANG2, Zhenxia DUAN2   

  1. 1.Yancheng Technician College Jiangsu Province, Yancheng 224000, Jiangsu, China
    2.Univeresity of Shanghai for Science and Technology, Shanghai 200093, China
  • Received:2023-03-09 Revised:2023-04-07 Online:2023-07-05 Published:2023-07-25
  • Contact: Xiaoyan WANG E-mail:434759407@qq.com;xiaoyan_wang@usst.edu.cn

摘要:

为了设计一款新的锂离子电池组液冷式热管理系统,建立了锂离子电池组热管理系统试验台架以及该系统耦合电动汽车动力学的一维仿真模型。首先,以试验结果验证了仿真模型的准确性。其次,研究了系统配置参数对电池温度的影响机理;最后,以电池温度不超过32 ℃和最低的系统功耗作为优化目标,建立多目标优化模型对系统的配置参数进行了优化。结果表明:试验与仿真结果的误差在3.0%内。较高的流量、较低的入口温度、较低的冷却液浓度会降低电池温度,而延迟冷却干预可以降低20%左右的系统功耗,采用响应面法结合MOGA-Ⅱ算法进行多目标优化后,在1.0 C放电倍率时,最高电池温度为30.83 ℃,并且可进一步将系统功耗降低至2750 W。这说明优化得到的系统最优配置参数方案较好地平衡了电池温度与系统功耗,试验与仿真结合的设计方法为电动汽车锂离子电池组的热管理系统设计提供了参考。

关键词: 锂离子电池, 液冷式热管理系统, 耦合电动汽车动力学, 一维仿真, 多目标优化

Abstract:

This study aims to design a new liquid-cooling heat management system for lithium-ion battery packs. We have established a special experimental platform and a liquid-cooling system model coupled with an EV dynamic model to determine the optimal matching parameters for the components and the operational control strategies of the system. The results indicate that the deviation between experiment and simulation is within 3.0% under normal conditions. A higher flow rate and lower inlet temperature results in a lower battery temperature, while delaying the cooling intervention can reduce power consumption by around 20%. To further reduce the power consumption to 2750 W and maintain a battery temperature of 30.83 ℃ during normal 1.0 C discharge, we conducted a multiobjective optimization using the response surface method combined with genetic algorithm Ⅱ. Additionally, this optimization demonstrates a well-balanced solution between battery temperature and power consumption during the drive cycle. By combining the results of the experiment and simulation, this work provides valuable insights for designing an excellent liquid-cooling system for lithium-ion battery packs in electric vehicles.

Key words: lithium-ion battery, liquid-cooling system, coupled with EV dynamic model, one-dimensional numerical analysis, multi-objective optimization

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