Review of Lithium-Ion Battery Fault Features, Diagnosis Methods, and Diagnosis Procedures
Jiahui Zhao, Mingyi Liu, Bin Zhang, Xiaolong Wang, Dawei Liu, Jianxin Wang, Panxing Bai, Chenghao Liu, Yue Sun, Yong Zhu
Abstract
The increasing adoption of lithium-ion batteries (LIBs) in low-carbon power systems is driven by their advantages, including long life, low self-discharge, and high energy density. However, LIB failures degrade performance and cause fire hazards. Effective fault diagnosis is thus critical yet challenging. This paper reviews LIB fault mechanisms, features, and methods with object of providing an overview of fault diagnosis techniques, emphasizing feature extraction’s critical role in detection via thresholds and isolation via multi-level strategies, and estimating detection quality. Several research gaps exist in current fault diagnosis techniques. Most techniques assume controlled conditions unlike complex real-world systems. Resource limitations often confine analytics to individual fault types, overlooking comprehensive approaches. Furthermore, many sensors lack the capability to detect precursor abnormalities, hampering early fault detection. To address these challenges, we advocate for advanced multiphysics and multiscale methods that incorporate sound, force, and thermal coupling to enhance fault diagnosis robustness in practical, complex systems. Multidimensional feature selection can prevent oversimplification in fault diagnosis. Overall, integrating analytics, sensing, and physics could enable comprehensive multidomain fault diagnosis under actual operating conditions.