Battery Capacity Anomaly Detection and Data Fusion

John Weddington, Wuzhao Yan, Wanchun Dou, and Bin Zhang
Submission Type: 
Full Paper
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phmc_15_068.pdf315.88 KBAugust 26, 2015 - 9:03am

Diagnosis is a critical enabling technique of Prognostics and Health Management (PHM), especially in safety critical applications. In order for the PHM system to begin prediction of remaining useful life (RUL) of a given system or component, the fault must be detected, isolated, and identified. This paper presents an integrated diagnosis of state-of-health (SOH) of lithium-ion (Li-ion) batteries. Two algorithms for fault diagnosis are used: the extended Kalman filter (EKF) and the particle filter (PF). To improve the performance of diagnosis and reduce the uncertainties, a Dempster-Shafer Theory (DST)-based fusion approach is used to fuse the SOH state estimations from the EKF and the PF approaches. The results on battery data demonstrate the effectiveness of the proposed approach in improving the diagnosis significantly.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
068
Page Count: 
8
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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