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A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

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  • Caiping Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Jiuchun Jiang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Linjing Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Sijia Liu

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

  • Leyi Wang

    (Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA)

  • Poh Chiang Loh

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China)

Abstract

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

Suggested Citation

  • Caiping Zhang & Jiuchun Jiang & Linjing Zhang & Sijia Liu & Leyi Wang & Poh Chiang Loh, 2016. "A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery," Energies, MDPI, vol. 9(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:900-:d:81901
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    References listed on IDEAS

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    15. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    16. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    17. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    18. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    19. Torregrosa, Antonio José & Broatch, Alberto & Olmeda, Pablo & Agizza, Luca, 2023. "A generalized equivalent circuit model for lithium-iron phosphate batteries," Energy, Elsevier, vol. 284(C).
    20. S. N. Syed Nasir & J. J. Jamian & M. W. Mustafa, 2018. "Minimizing Harmonic Distortion Impact at Distribution System with Considering Large-Scale EV Load Behaviour Using Modified Lightning Search Algorithm and Pareto-Fuzzy Approach," Complexity, Hindawi, vol. 2018, pages 1-14, February.
    21. Prarthana Pillai & Sneha Sundaresan & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Open-Circuit Voltage Models for Battery Management Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-25, September.
    22. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    23. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    24. 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).

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