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Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers

Author

Listed:
  • Vahid Behnamgol

    (Energy Research Centre, Islamic Azad University of Damavand, Damavand 1477893780, Iran)

  • Mohammad Asadi

    (Department of Electrical Engineering, Iran University of Science and Technology, Tehran 168463114, Iran)

  • Mohamed A. A. Mohamed

    (Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)

  • Sumeet S. Aphale

    (Artificial Intelligence, Robotics and Mechatronic Systems (ARMS) Group, School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK)

  • Mona Faraji Niri

    (Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK)

Abstract

The state of charge (SoC) is a critical parameter in lithium-ion batteries and their alternatives. It determines the battery’s remaining energy capacity and influences its performance longevity. Accurate SoC estimation is essential for making informed charging and discharging decisions, mitigating the risks of overcharging or deep discharge, and ensuring safety. Battery management systems rely on SoC estimation, utilising both hardware and software components to maintain safe and efficient battery operation. Existing SoC estimation methods are broadly classified into direct and indirect approaches. Direct methods (e.g., Coulumb counting) rely on current measurements. In contrast, indirect methods (often based on a filter or observer) utilise a model of a battery to incorporate voltage measurements besides the current. While the latter is more accurate, it faces challenges related to sensor drift, computational complexity, and model inaccuracies. The need for more precise and robust SoC estimation without increasing complexity is critical, particularly for real-time applications. Recently, sliding mode observers (SMOs) have gained prominence in this field for their robustness against model uncertainties and external disturbances, offering fast convergence and superior accuracy. Due to increased interest, this review focuses on various SMO approaches for SoC estimation, including first-order, adaptive, high-order, terminal, fractional-order, and advanced SMOs, along with hybrid methods integrating intelligent techniques. By evaluating these methodologies, their strengths, weaknesses, and modelling frameworks in the literature, this paper highlights the ongoing challenges and future directions in SoC estimation research. Unlike common review papers, this work also compares the performance of various existing methods via a comprehensive simulation study in MATLAB 2024b to quantify the difference and guide the users in selecting a suitable version for the applications.

Suggested Citation

  • Vahid Behnamgol & Mohammad Asadi & Mohamed A. A. Mohamed & Sumeet S. Aphale & Mona Faraji Niri, 2024. "Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers," Energies, MDPI, vol. 17(22), pages 1-39, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5754-:d:1523272
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    References listed on IDEAS

    as
    1. Qian, Wei & Li, Wan & Guo, Xiangwei & Wang, Haoyu, 2024. "A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery," Energy, Elsevier, vol. 292(C).
    2. Daehyun Kim & Keunhwi Koo & Jae Jin Jeong & Taedong Goh & Sang Woo Kim, 2013. "Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model," Energies, MDPI, vol. 6(10), pages 1-14, October.
    3. Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    4. Qiaoyan Chen & Jiuchun Jiang & Haijun Ruan & Caiping Zhang, 2016. "A New Double Sliding Mode Observer for EV Lithium Battery SOC Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, October.
    5. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    6. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    7. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.
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