Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

Zhongbao Wei, King Jet Tseng, Nyunt Wai, Tuti Mariana Lim*, Maria Skyllas-Kazacos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

176 Citations (Scopus)

Abstract

Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.

Original languageEnglish
Pages (from-to)389-398
Number of pages10
JournalJournal of Power Sources
Volume332
DOIs
Publication statusPublished - 15 Nov 2016
Externally publishedYes

Keywords

  • Battery model
  • Capacity
  • Joint estimation
  • Parameter identification
  • State of charge
  • Vanadium redox flow battery

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