Optimization of an Air-cooling Thermal Management System for Lithium-ion Battery Packs via Particle Swarm Algorithm

Abstract

Abstract Recently, lithium-ion batteries have attracted many researchers and their safety issues such as overheating, combustion and explosion continue to further limit battery application scenarios. These issues are mainly caused by unoptimized battery structure parameters or cooling methods. In this paper, an integrated approach has been proposed to design an efficient air-cooling system using the particle swarm algorithm to find an optimal relationship between air flow rate and battery temperature. Firstly, this method can adjust an optimized air flow rate to ensure that the battery temperature is minimized with the lowest energy consumption via the particle swarm algorithm. Additionally, an optimized air flow rate can still be obtained with the change of structure parameters such as the radius in a lithium-ion battery pack via this novel algorithm. Then, we demonstrate the feasibility of this integrated method in simulations. Compared with the previous work, this method can employ the continuous modulation of the particle swarm algorithm, realizing both the best cooling capacity of the battery cooling system and simultaneously the lowest energy consumption for cooling in cell heat regulation systems. Meanwhile, temperature variations of the entire cell pack are also shown in simulations. In contrast to previous approaches, this integrated method may provide more dynamic thermal management inspirations for designing novel battery thermal management systems.