Optimization for Liquid Cooling Cylindrical Battery Thermal Management System Based on Gaussian Process Model
Wei Li, Akhil Garg, Mi Xiao, Liang Gao
Abstract
Abstract The power of electric vehicles (EVs) comes from lithium-ion batteries (LIBs). LIBs are sensitive to temperature. Too high and too low temperatures will affect the performance and safety of EVs. Therefore, a stable and efficient battery thermal management system (BTMS) is essential for an EV. This article has conducted a comprehensive study on liquid-cooled BTMS. Two cooling schemes are designed: the serpentine channel and the U-shaped channel. The results show that the cooling effect of two schemes is roughly the same, but the U-shaped channel can significantly decrease the pressure drop (PD) loss. The U-shaped channel is parameterized and modeled. A machine learning method called the Gaussian process (GP) model has been used to express the outputs such as temperature difference, temperature standard deviation, and pressure drop. A multi-objective optimization model is established using GP models, and the NSGA-II method is employed to drive the optimization process. The optimized scheme is compared with the initial design. The main findings are summarized as follows: the velocity of cooling water v decreases from 0.3 m/s to 0.22 m/s by 26.67%. Pressure drop decreases from 431.40 Pa to 327.11 Pa by 24.18%. The optimized solution has a significant reduction in pressure drop and helps to reduce parasitic power. The proposed method can provide a useful guideline for the liquid cooling design of large-scale battery packs.