Optimization of an air-based thermal management system for lithium-ion battery packs

• Published in: Journal of energy storage Vol. 44; p. 103314
• Main Authors: Wang, Yusong, Liu, Bin, Han, Peng, Hao, Changsheng, Li, Shaohua, You, Zhanping, Wang, Meiqi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2021
• Subjects: Computational fluid dynamics; Electric vehicle; Multi-objective optimization; Thermal management; Thermal management; Electric vehicle; Multi-objective optimization; Computational fluid dynamics
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2021.103314

•Multi-objective optimization method for air-based battery thermal management system is proposed.•Optimal parameters are predicted by neural-network-based model combined with particle swarm optimization  algorithm.•Results are validated against computational fluid dynamics predictions. A battery thermal management system controls the operation temperature of battery packs, which is of great importance to the safety and performance of electric vehicles. High operation temperature in a battery park will shorten the life of batteries, and even lead to thermal runaway. Conventional air-based battery thermal management systems are still widely applied in light-duty electric vehicles owing to unparalleled advantages such as low manufacturing cost, simple structure, and high reliability. However, an air-based battery thermal management system tends to suffer from a relatively low cooling effect and high energy consumption. Previous studies have focused more on one aspect either of the cooling performance or energy consumption of the system, but less on the optimization of the two aspects. Also, previous studies mainly focused on the optimization of the structure of the battery pack in one direction, but very few work considered the optimization in two directions. In this work, we construct an air system with uneven cell spacing distribution from both sides of the battery pack to reduce the energy consumption of the system and reduce the maximum temperature rise of the battery pack. First, based on an in-depth parametric study that used various design variables, a database related to the relationship between the in-space structure in the battery pack and the cooling performance parameters is established through a computational fluid dynamic simulation. Then, the multi-objective optimization, which combines maximum temperature and energy consumption, is conducted through a Neural-Network-based model incorporated with a Particle Swarm optimization algorithm. The optimal design parameters are obtained and cross validated against the computational fluid dynamics predictions. The energy consumption of the battery thermal management system under the optimal condition is reduced to 41.19% of the original battery thermal management system. Simulation analysis shows that the proposed multi-objective optimization method can effectively reduce the energy consumption of battery thermal management system.