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Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

Author

Listed:
  • Benedikt Rzepka

    (Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

  • Simon Bischof

    (Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

  • Thomas Blank

    (Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

Abstract

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3733-:d:579932
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    References listed on IDEAS

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    1. 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.
    2. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    3. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    4. 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.
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    Citations

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    Cited by:

    1. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    2. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(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. Alaa Omar & Alan Wood & Hamish Laird & Paul Gaynor, 2022. "Single-Phase Charging of EV Embedded Batteries in an MMC with Submodule Override Capability," Energies, MDPI, vol. 15(6), pages 1-20, March.
    7. Artun Sel & Bilgehan Sel & Umit Coskun & Cosku Kasnakoglu, 2021. "Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification," Energies, MDPI, vol. 14(19), pages 1-14, September.

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