A Fast Charging–Cooling Coupled Scheduling Method for a Liquid Cooling-Based Thermal Management System for Lithium-Ion Batteries. (August 2021)

• Record Type: Journal Article
• Authors: Chen, Siqi; Bao, Nengsheng; Garg, Akhil; Peng, Xiongbin; Gao, Liang
• Is Part Of: Engineering. Volume 7:Number 8(2021)
• Page Start: 1165
• Page End: 1176
• Publication Date: 2021-08
• Subjects: Lithium-ion battery module -- Fast-charging -- Neural network regression -- Scheduling -- State of charge -- Energy consumption; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2020.06.016
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg·s −1 ). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module's state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.