A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack

Considering the limitations in existing correlation coefficient-based, entropy-based and big data analysis-based voltage sensor fault diagnosis methods, we develop a novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack in this paper. Firstly, a ohmic resistance-based selection method is periodically performed to artificially divide all in-pack cells into representative cell and non-representative cells. Secondly, during the representative cell-based battery pack state-of-charge (SOC) and cell SOC inconsistence estimation process, the measurement innovation (MI) between measured and estimated voltage of the representative cell and non-representative cells is generated in micro time-scale and macro timescale, respectively. Regarding the representative cell, the faulty voltage sensor is immediately detected at the moment of the voltage sensor fault occurrence by catching the abnormal MI. As for the non-representative cells, through analyzing the discreteness degree of generated MI under faultless and faulty voltage sensors, an abnormal variance-based voltage sensor fault diagnosis method and an abnormal variance contribution-based fault location method are developed. The validation results through three sophisticated cases demonstrate that this method can rapidly catch the abnormal features for further voltage sensor fault diagnosis with low complexity and satisfactory robustness even though there exist certain faulty current.

Automated summary

This paper proposes a novel voltage sensor fault detection and isolation method for lithium-ion battery packs. It divides the cells into representative and non-representative cells and generates measurement innovation at different time scales to detect and diagnose the faulty voltage sensors. The method shows low complexity and satisfactory robustness in the presence of faulty current.