Battery Capacity Anomaly Detection and Data Fusion



Published Oct 18, 2015
John Weddington Wuzhao Yan Wanchun Dou Bin Zhang


Anomaly detection is a critical enabling technique of PHM, especially in safety critical applications. In order for the PHM system to begin prediction of remaining useful life of a given system or component, the fault must be detected. This paper presents an integrated anomaly detection system for state-of- health of lithium-ion batteries. Two algorithms for state estimation and anomaly detection are used: the extended Kalman filter and the particle filter. A Dempster-Shafer Theory-based fusion approach is implemented to reduce the uncertainty of detection. The results on battery data show that the fusion improves the detection results significantly.

How to Cite

Weddington, J. ., Yan, W. ., Dou, W., & Zhang, B. . (2015). Battery Capacity Anomaly Detection and Data Fusion. Annual Conference of the PHM Society, 7(1).
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