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Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter

Author

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  • Yasser Diab

    (Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France
    Department of Electrical Power Engineering, Damascus University, Damascus B.P. 86, Syria)

  • François Auger

    (Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France)

  • Emmanuel Schaeffer

    (Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France)

  • Moutassem Wahbeh

    (Department of Electrical Power Engineering, Damascus University, Damascus B.P. 86, Syria)

Abstract

A real-time determination of battery parameters is challenging because batteries are non-linear, time-varying systems. The transient behaviour of lithium-ion batteries is modelled by a Thevenin-equivalent circuit with two time constants characterising activation and concentration polarization. An experimental approach is proposed for directly determining battery parameters as a function of physical quantities. The model’s parameters are a function of the state of charge and of the discharge rate. These can be expressed by regression equations in the model to derive a continuous-discrete extended Kalman estimator of the state of charge and of other parameters. This technique is based on numerical integration of the ordinary differential equations to predict the state of the stochastic dynamic system and the corresponding error covariance matrix. Then a standard correction step of the extended Kalman filter (EKF) is applied to increase the accuracy of estimated parameters. Simulations resulting from this proposed estimator model were compared with experimental results under a variety of operating scenarios—analysis of the results demonstrate the accuracy of the estimator for correctly identifying battery parameters.

Suggested Citation

  • Yasser Diab & François Auger & Emmanuel Schaeffer & Moutassem Wahbeh, 2017. "Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter," Energies, MDPI, vol. 10(8), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1075-:d:105839
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    References listed on IDEAS

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    1. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    2. Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
    3. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    4. Fei Feng & Rengui Lu & Chunbo Zhu, 2014. "A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide Ambient Temperature Range," Energies, MDPI, vol. 7(5), pages 1-29, May.
    5. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
    6. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.
    7. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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    Cited by:

    1. Yasser Diab & Francois Auger & Emmanuel Schaeffer & Stéphane Chevalier & Adib Allahham, 2022. "Real-Time Estimation of PEMFC Parameters Using a Continuous-Discrete Extended Kalman Filter Derived from a Pseudo Two-Dimensional Model," Energies, MDPI, vol. 15(7), pages 1-23, March.
    2. Prarthana Pillai & Sneha Sundaresan & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound," Energies, MDPI, vol. 15(22), pages 1-21, November.
    3. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    4. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    5. Nicolae Tudoroiu & Mohammed Zaheeruddin & Roxana-Elena Tudoroiu, 2020. "Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-45, May.
    6. Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
    7. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.

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