Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

作     者：Yunhong Che Zhongwei Deng Xiaolin Tang Xianke Lin Xianghong Nie Xiaosong Hu Yunhong Che;Zhongwei Deng;Xiaolin Tang;Xianke Lin;Xianghong Nie;Xiaosong Hu

作者机构：College of Mechanical and Vehicle EngineeringChongqing UniversityChongqing 400044China State Key Laboratory of Mechanical TransmissionsChongqing UniversityChongqing 400044China Department of AutomotiveMechanical and Manufacturing EngineeringOntario Tech UniversityOshawaON L1G 0C5Canada Powertrain Engineering R&D InstituteChongqing Changan Automotive Co.LtdChongqing 401133China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2022年第35卷第1期

页      面：192-207页

核心收录：

学科分类：0710[理学-生物学] 0711[理学-系统科学] 1002[医学-临床医学] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China(Grant Nos.51875054,U1864212) Graduate Research and Innovation Foundation of Chongqing China(Grant No.CYS20018) Chongqing Municipal Natural Science Foundation for Distinguished Young Scholars of China(Grant No.cstc2019jcyjjq X0016) Chongqing Science and Technology Bureau of China 

主　　题：Lithium-ion battery packs Lifetime prediction Degradation prognostic Model migration Machine learning 

摘      要：Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe *** paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process *** health indicators are extracted from the partial discharge *** sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation *** future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime ***,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model *** results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging ***,it largely reduces the time and labor for battery pack *** predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.