An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Cheng Zhang, Kang Li, Lei Pei, Chunbo Zhu

Research output: Contribution to journalArticle

55 Citations (Scopus)

Abstract

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.
Original languageEnglish
Pages (from-to)24-36
Number of pages13
JournalJournal of Power Sources
Volume283
Early online date19 Feb 2015
DOIs
Publication statusPublished - 1 Jun 2015
Externally publishedYes

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Power Sources. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Power Sources, 283, (2015)
DOI: 10.1016/j.jpowsour.2015.02.099

© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • LiFePo battery
  • Real-time SOC estimation
  • Hysteresis effect
  • Extended Kalman filter
  • Weighted recursive least square
  • Teaching learning based optimization (TLBO) method

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