Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use

Brian Bole, Chetan Kulkarni, and Matthew Daigle
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_14_052.pdf905.08 KBSeptember 16, 2014 - 11:42am

Tracking the variation in battery dynamics as a function of health is presently attracting attention in academia and industry due to the increased usage of expensive batteries in dynamic systems such as aircraft and electric cars. The online adaptation of battery models to account for age-dependent changes in dynamics is necessary to maintain accurate estimates of the remaining system operations that can be supported under battery power. A novel method for the adaptation of parameters in an electrochemical model of a lithium-ion battery is presented here. An unscented Kalman filtering algorithm is shown to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates using only battery current and voltage data collected over randomized discharge profiles. The use of only data collected over randomized discharge profiles distinguishes this work from other works that make use of reference discharge cycles to judge battery health. The experimental results presented here compare online model estimates produced by the proposed algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
052
Page Count: 
9
Submission Keywords: 
lithium ion battery
model adaptation
electrochemical modeling
unscented Kalman filtering
battery aging
Submission Topic Areas: 
Component-level PHM
Model-based methods for fault detection, diagnostics, and prognosis
PHM for electronics
Submitted by: 
  
 
 
 

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