Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Binyu Xiong
  • Jiyun Zhao
  • Zhongbao Wei
  • Maria Skyllas-Kazacos

Detail(s)

Original languageEnglish
Pages (from-to)50-61
Journal / PublicationJournal of Power Sources
Volume262
Publication statusPublished - 15 Sep 2014
Externally publishedYes

Abstract

State of charge (SOC) estimation is a key issue for battery management since an accurate estimation method can ensure safe operation and prevent the over-charge/discharge of a battery. Traditionally, open circuit voltage (OCV) method is utilized to estimate the stack SOC and one open flow cell is needed in each battery stack [1,2]. In this paper, an alternative method, extended Kalman filter (EKF) method, is proposed for SOC estimation for VRBs. By measuring the stack terminal voltages and applied currents, SOC can be predicted with a state estimator instead of an additional open circuit flow cell. To implement EKF estimator, an electrical model is required for battery analysis. A thermal-dependent electrical circuit model is proposed to describe the charge/discharge characteristics of the VRB. Two scenarios are tested for the robustness of the EKF. For the lab testing scenarios, the filtered stack voltage tracks the experimental data despite the model errors. For the online operation, the simulated temperature rise is observed and the maximum SOC error is within 5.5%. It is concluded that EKF method is capable of accurately predicting SOC using stack terminal voltages and applied currents in the absence of an open flow cell for OCV measurement. © 2014 Elsevier B.V. All rights reserved.

Research Area(s)

  • Extended Kalman filter, Flow rate, State of charge, Temperature, Thermal dependent electrical model, Vanadium redox flow battery

Citation Format(s)