Xinhua Liu, Mingyue Wang, Rui Cao, Meng Lyu, Cheng Zhang, Shen Li, Bin Guo, Lisheng Zhang, Zhengjie Zhang, Xinlei Gao, Hanchao Cheng, Bin Ma, Shichun Yang
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Review of Abnormality Detection and Fault Diagnosis Methods for Lithium-Ion Batteries
Electric vehicles are developing prosperously in recent years. Lithium-ion batteries have become the dominant energy storage device in electric vehicle application because of its advantages such as high power density and long cycle life. To ensure safe and efficient battery operations and to enable timely battery system maintenance, accurate and reliable detection and diagnosis of battery faults are necessitated. In this paper, the state-of-the-art battery fault diagnosis methods are comprehensively reviewed. First, the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized. Then, the fault diagnosis methods are categorized into the statistical analysis-, model-, signal processing-, and data-driven methods. Their distinctive characteristics and applications are summarized and compared. Finally, the challenges facing the existing fault diagnosis methods are discussed and the future research directions are pointed out.
期刊介绍:
Automotive Innovation is dedicated to the publication of innovative findings in the automotive field as well as other related disciplines, covering the principles, methodologies, theoretical studies, experimental studies, product engineering and engineering application. The main topics include but are not limited to: energy-saving, electrification, intelligent and connected, new energy vehicle, safety and lightweight technologies. The journal presents the latest trend and advances of automotive technology.