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Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Author

Listed:
  • Mengying Chen

    (School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia)

  • Fengling Han

    (School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia)

  • Long Shi

    (School of Engineering, RMIT University, Melbourne, VIC 3000, Australia)

  • Yong Feng

    (School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Chen Xue

    (School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Weijie Gao

    (Beijing Intell-Sun Technology Limited, Beijing 100012, China)

  • Jinzheng Xu

    (Research and Development Center, Anhui Huasun Energy Co., Ltd., Xuancheng 242000, China)

Abstract

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

Suggested Citation

  • Mengying Chen & Fengling Han & Long Shi & Yong Feng & Chen Xue & Weijie Gao & Jinzheng Xu, 2022. "Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model," Energies, MDPI, vol. 15(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2658-:d:787343
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    References listed on IDEAS

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    Cited by:

    1. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.

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