Li‐ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal runaway or even explosion. This paper reviews recent research and development of ageing mechanisms of Li‐ion batteries to understand the origins and symptoms of Li‐ion battery faults. Common ageing factors are covered with their effects and consequences. Through ageing tests, relationship between performance and ageing factors, as well as cross‐dependence among factors can be quantified. Summary of recent research about fault diagnosis technology for Li‐ion batteries is concluded with their cons and pros. The suggestions on novel fault diagnosis approach and remaining challenges are provided at the end of this paper.

A fault diagnostic approach for lithium-ion battery is proposed in this paper, which is suitable for high-power applications such as hybrid electrical vehicle and electromagnetic launch. A serial of abusive experiments including overcharge, over-discharge, and low-temperature operation, which commonly occur during high-power applications, are arranged in order to extract corresponding features. Fault feature vector in the form of electrical parameter variation and incremental capacity analysis curve variation is obtained through reference test. The results from the tests are synthesized to establish the diagnosis system based on fuzzy logic, which provides a detailed description for the fault symptoms and connects them with internal fault mechanisms.

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO₄ batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (ε_n, D_s,n) and electrolyte (D_e, C_e) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.

A detailed research on fault mechanism of lithium (Li)‐ion battery at over‐discharge condition is reported in this study. Cells were cycled with different depths of discharge and reference performance tests were performed to extract parameters in dynamic and equilibrium conditions. The over‐discharge process indicates that the abrupt change of temperature and impedance can be used for fault predication, while the parameter variations from federal urban driving schedule test can clearly identify the fault mode. Curves from incremental capacity analysis suggest loss of active material at negative electrode (LAMNE) as well as Li inventory loss may dominate the over‐discharge process. Finally, post‐mortem examination was carried out to validate authors’ mechanism deduction and diagnosis approach. The proposed approach is suitable for over‐discharge diagnosis and predication in electric vehicle applications.