A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack. (15th September 2022)

• Record Type: Journal Article
• Authors: Zhang, Shuzhi; Jiang, Shiyong; Wang, Hongxia; Zhang, Xiongwen
• Is Part Of: Applied energy. Volume 322(2022)
• Publication Date: 2022-09-15
• Subjects: Voltage sensor fault diagnosis -- Dual time-scale -- State-of-charge estimation -- Measurement innovation generation -- Discreteness analysis; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119541
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel dual time-scale voltage sensor fault diagnosis for battery pack is proposed. All in-pack cells are artificially divided into two different classes. Two different time-scales are used to diagnose the fault of two classes of cells. The proposed method is robust enough against unpredictable operating conditions. The method is still effective when there exist certain faulty current. Abstract: 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 time-scale, 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.