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On-line battery state-of-charge estimation based on an integrated estimator

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  • Wang, Yujie
  • Zhang, Chenbin
  • Chen, Zonghai

Abstract

Energy crises and environmental issues have promoted research into development of various types of electric vehicles (EVs). Since the control strategy of EVs is essentially dependent on the state-of-charge (SoC) estimation of the batteries, one of the most critical issues of battery management system (BMS) is to accurately estimate the SOC in real-time. This paper proposed an integrated SoC estimator based on the adaptive extended Kalman filter (EKF) and particle filter (PF). The adaptive EKF which can provides more accurate approximate distribution in PF, is used for on-line parameters estimation of polarization voltage and noise covariance. The PF is used for on-line SoC estimation based on the priori knowledge given by the adaptive EKF. In order to get accurate SoC estimation results, the cell model is established based on the integration of the equivalent circuit model and electrochemical model. The combined electrochemical model is employed to simulate the cell electrochemical characteristics and estimate the terminal voltage. What is more, the recursive least-squares (RLS) method is used for parameters identification to improve the model precision. Experiments are performed on the LiFePO4 cell at different temperatures and under dynamic current to verify the reliability and robustness of the proposed method. The results indicated that accurate and robust SoC estimation results can be obtained by the proposed method.

Suggested Citation

  • Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:2026-2032
    DOI: 10.1016/j.apenergy.2015.09.015
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    References listed on IDEAS

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    2. Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Pierluigi Siano & Ramesh Krishnamoorthy & Raghu Selvaraj, 2017. "Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    3. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    4. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    5. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    6. Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
    7. Choi, Wonjae & Song, Han Ho, 2018. "Well-to-wheel greenhouse gas emissions of battery electric vehicles in countries dependent on the import of fuels through maritime transportation: A South Korean case study," Applied Energy, Elsevier, vol. 230(C), pages 135-147.
    8. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    9. Ye, Jinhua & Xie, Quan & Lin, Mingqiang & Wu, Ji, 2024. "A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network," Energy, Elsevier, vol. 294(C).
    10. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    11. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

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