A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

Rong Jing, Zhimin Xi, Xiao Guang Yang, and Ed Decker
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_14_009.pdf628.52 KBAugust 28, 2014 - 8:51am

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery diagnostics and prognostics. As a consequence, accuracy of the battery performance estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework to quantify battery model and parameter uncertainties for more effective battery performance estimation. Such a framework is generally applicable for estimating various battery performances of interest (e.g. state of charge (SOC), capacity, and power capability). Case studies for battery SOC estimation are conducted to demonstrate the effectiveness of the proposed framework.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
009
Page Count: 
10
Submission Keywords: 
Battery SoC
model uncertainty
parameter uncertainty
extended Kalman filter
battery diagnostics
Submission Topic Areas: 
PHM for electronics
Submitted by: 
  
 
 
 

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