Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter. (1st January 2017)

• Record Type: Journal Article
• Authors: Liu, Zhentong; He, Hongwen
• Is Part Of: Applied energy. Volume 185:Part 2(2017)
• Page Start: 2033
• Page End: 2044
• Publication Date: 2017-01-01
• Subjects: Lithium-ion battery pack -- Fault detection and isolation -- Statistical inference residual evaluation -- Adaptive extended Kalman filter -- Electric vehicles; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.10.168
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A sensor fault detection and isolation scheme for battery pack is presented. The proposed diagnostic scheme is with low computational effort. Adaptive extended Kalman filter is applied to help generate the residual. The residuals are evaluated by a statistical inference method. The effectiveness of the proposed scheme is experimentally validated. Abstract: This paper presents an effective model-based sensor fault detection and isolation (FDI) scheme for a series battery pack with low computational effort. The large number of current and voltage sensors in the battery pack, make it of high computational complexity. The major purpose of sensor FDI is to guarantee the healthy operations of the battery management system (BMS), and thus to prevent the battery from over-charge and over-discharge. In the voltage sensors fault scenarios, the most possibly being over-charged and over-discharged cells are these two cells with the maximum and minimum voltage respectively. Within the proposed scheme, these two cells are monitored in real time to diagnose the pack current sensor fault, or a voltage sensor fault of these two cells, while the rest cells are monitored offline with a long time interval, guaranteeing other voltage sensors working normally. For the scheme implementation, adaptive extended Kalman filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. Then the residuals are evaluated by a statistical inference method that determines the presence of the fault. Finally, the effectiveness of the proposed sensor FDI scheme is experimentally validated with a series battery pack under the UDDS driving cycles.