Energy Storage Science and Technology ›› 2021, Vol. 10 ›› Issue (4): 1446-1453.doi: 10.19799/j.cnki.2095-4239.2021.0131

• Energy Storage Test: Methods and Evaluation • Previous Articles     Next Articles

Optimization of efficient thermal management channel for battery pack based on genetic algorithm

Yinquan HU1(), Heping LIU2   

  1. 1.Chongqing Vocational Institute of Engineering, School of Intelligent Manufacturing and Transportation, Chongqing 402260, China
    2.School of Electrical Engineering, Chongqing University, Chongqing 400044, China
  • Received:2021-03-29 Revised:2021-05-26 Online:2021-07-05 Published:2021-06-25
  • Contact: Yinquan HU E-mail:hujunping2018@163.com

Abstract:

The thermal management of the battery pack is essential to avoid problems such as overheating and thermal runaway. An active cooling system must be used to maintain a safe temperature of the battery and improve the performance and life of the battery. Liquid cooling is an effective cooling method, but parameterized research on the influence of structural parameters on cooling effects is still lacking. In this paper, a liquid cooling method based on a micro channel silicon-based cold plate is used, and a fluid-solid coupling heat dissipation model is established by using computational fluid dynamics. The Latin super vertical method is used to generate parameter combination samples and a cooling system with efficient heat dissipation performance and lower energy consumption is developed through a multi-objective genetic optimization method. Experimental results show that the optimized liquid cooling system is sufficient to control the temperature of the module below 45 ℃, and the temperature deviation between single cells can also be controlled within a small range of 2 ℃. The results of this study will provide effective research ideas for the design and optimization of the thermal management system of battery components, and will help promote the application of batteries in actual products.

Key words: battery pack, thermal management, genetic algorithm, CFD simulation

CLC Number: