Quanqing Yu a, Can Wang a, Jianming Li a, Rui Xiong b, and Michael Pecht c
a School of Automotive Engineering, Harbin Institute of Technology, Weihai, Shandong, 264209, China
b National Engineering Laboratory for Electric Vehicle, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China
c Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, United States
For more information about this article and related research, please contact Prof. Michael G. Pecht.
Lithium-ion batteries are the ideal energy storage device for numerous portable and energy storage applications. Efficient fault diagnosis methods become urgent to address safety risks. The fault modes, fault data, fault diagnosis methods in different scenarios, i.e., laboratory, electric vehicle, energy storage system, and simulation, are reviewed and compared comprehensively. The data characteristics, performance and limitations of fault diagnosis methods are discussed further. The results show that the fault diagnosis methods of laboratory scenario are more advanced than real-world applications because of the clean and perfect dataset, advanced equipment, and ideal operating conditions. At last, the outlook and challenges for applying fault diagnosis methods from laboratory to real-world applications are investigated from three aspects: unified framework of fault diagnosis methods, cloud big data fusion, and application of laboratory measurement technologies. To realize more accurate fault diagnosis in real-world applications within the advanced research of laboratory, more significant work is still needed.
This article is available online here and to CALCE Consortium Members for personal review.