A battery internal short circuit fault diagnosis method based on incremental capacity curves

Abstract

The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%.