期刊
JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE
卷 18, 期 2, 页码 -出版社
ASME
DOI: 10.1115/1.4048010
关键词
electric vehicle; lithium-ion battery pack; thermal management system; optimal design; multi-objective optimization; batteries
资金
- Natural Science Foundation of Hunan Province of China [2017JJ3057]
- Research Foundation of Education Bureau of Hunan Province of China [17C0473]
- Natural Science Foundation of Guangdong Province of China [2018A030310150]
- Education Department of Guangdong Province of China [2019KTSCX039]
Battery thermal management system is crucial for preventing safety issues such as overheating and thermal runaway. This study proposed an optimal design framework using surrogate model and multi-objective optimization, which significantly improved both temperature rise and energy consumption through five steps including structural design and fluid-solid coupled modeling.
Battery thermal management system is critical to prevent the battery pack from such safety issues as overheating, thermal runaway, and spontaneous combustion. Many research works have been done to improve the thermal performance of the thermal management system by reducing the maximum temperature of the battery pack. However, the temperature difference and energy consumption were not discussed in most of the researches. This paper proposed a framework of optimal design of the battery thermal management system using surrogate model and multi-objective optimization methodology. The accuracy of this method was then validated through two cases. The proposed framework aims to find a way to design a battery pack with at least two types of the following objectives: the smallest maximum temperature, smallest temperature deviation, and the lowest energy consumption. The framework can be divided into five steps: the structural design of the battery thermal management system; the fluid-solid coupled heat transfer modeling using computational fluid dynamics (CFD) method; the design of experiments and selection of surrogate models; the multi-objective optimization algorithm based on Pareto optimal solution; and the experimental verification. The optimized designs showed significant improvement by decreasing both the temperature rise and the energy consumption.
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