A Fast Charging–Cooling Coupled Scheduling Method for a Liquid Cooling-Based Thermal Management System for Lithium-Ion Batteries

作     者：Siqi Chen Nengsheng Bao Akhil Garg Xiongbin Peng Liang Gao Siqi Chen;Nengsheng Bao;Akhil Garg;Xiongbin Peng;Liang Gao

作者机构：State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Intelligent Manufacturing Key Laboratory of Ministry of EducationShantou UniversityShantou 515063China 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2021年第7卷第8期

页      面：1165-1176页

核心收录：

学科分类：0810[工学-信息与通信工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0808[工学-电气工程] 0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Program for Huazhong University of Science and Technology(HUST)Academic Frontier Youth Team(2017QYTD04) the Program for HUST Graduate Innovation and Entrepreneurship Fund(2019YGSCXCY037) Authors acknowledge Grant DMETKF2018019 by State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology This study was also financially supported by the Guangdong Science and Technology Project(2016B020240001) the Guangdong Natural Science Foundation(2018A030310150) 

主　　题：Lithium-ion battery module Fast-charging Neural network regression Scheduling State of charge Energy consumption 

摘      要：Efficient fast-charging technology is necessary for the extension of the driving range of electric ***,lithium-ion cells generate immense heat at high-current charging *** order to address this problem,an efficient fast charging–cooling scheduling method is urgently *** this study,a liquid cooling-based thermal management system equipped with mini-channels was designed for the fastcharging 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 *** sub-model had a desirable testing accuracy(99.353%,97.332%,and 98.381%)after *** 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 *** 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 *** maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8
C,*** approach described herein can be used by the electric vehicles industry in real fast-charging ***,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.