On-board soft short circuit fault diagnosis of lithium-ion battery packs for electric vehicles using extended Kalman filter

Abstract

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, it is necessary for early detection of soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC by coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in detecting a soft SC fault quickly and estimate soft SC resistance accurately.