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A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering

Author

Listed:
  • Wei He

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • David Flynn

    (Smart Systems Group, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK)

  • Fateme Dinmohammadi

    (Smart Systems Group, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK)

Abstract

State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter.

Suggested Citation

  • Wei He & Michael Pecht & David Flynn & Fateme Dinmohammadi, 2018. "A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering," Energies, MDPI, vol. 11(8), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2120-:d:163784
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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.
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    2. Tao Zhang & Ningyuan Guo & Xiaoxia Sun & Jie Fan & Naifeng Yang & Junjie Song & Yuan Zou, 2021. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    3. Wenshuo Tang & Darius Roman & Ross Dickie & Valentin Robu & David Flynn, 2020. "Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems," Energies, MDPI, vol. 13(18), pages 1-29, 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. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    6. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    7. Majid Astaneh & Jelena Andric & Lennart Löfdahl & Dario Maggiolo & Peter Stopp & Mazyar Moghaddam & Michel Chapuis & Henrik Ström, 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications," Energies, MDPI, vol. 13(14), pages 1-27, July.
    8. Amjad, Muhammad & Farooq-i-Azam, Muhammad & Ni, Qiang & Dong, Mianxiong & Ansari, Ejaz Ahmad, 2022. "Wireless charging systems for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Ali Wadi & Mamoun Abdel-Hafez & Ala A. Hussein, 2022. "Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter," Energies, MDPI, vol. 15(10), pages 1-15, May.
    10. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
    11. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    12. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 12(19), pages 1-22, September.

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