A Soft Short-Circuit Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft short-circuit (SC) faults in lithium-ion battery packs is critical to enhance electric vehicle safety and prevent catastrophic hazards. This article proposes a battery fault diagnosis method that achieves joint soft SC fault detection and estimation. Specifically, based on an augmented state-space battery model, an H-infinity nonlinear observer is constructed to estimate state of charge (SOC) and soft SC current in the presence of model parameter variations. Then, the asymptotic stability of the estimation error system under the desired H-infinity, performance is formally proved and a tractable observer design criterion is derived. Furthermore, a diagnosis algorithm is developed to detect soft SC faults via checking the difference between the estimated SOC from the observer and the calculated SOC from Coulomb counting. Once a soft SC fault is detected, the algorithm also allows the soft SC resistance to be calculated from the estimated soft SC current. Finally, soft SC experiments of a series-connected battery pack under different working conditions and various SC resistance values are conducted to verify the effectiveness of the proposed method.

Automated summary

This article proposes a battery fault diagnosis method that achieves joint soft short-circuit fault detection and estimation. It constructs a nonlinear observer to estimate the battery's state of charge and soft short-circuit current, and develops a diagnosis algorithm to detect soft short-circuit faults.