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A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Author

Listed:
  • Woo-Yong Kim

    (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 291, Korea)

  • Pyeong-Yeon Lee

    (Department of Electric Engineering, Chungnam National University, Daejeon 99, Korea)

  • Jonghoon Kim

    (Department of Electric Engineering, Chungnam National University, Daejeon 99, Korea)

  • Kyung-Soo Kim

    (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 291, Korea)

Abstract

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

Suggested Citation

  • Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3383-:d:263341
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    References listed on IDEAS

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    2. Yong Li & Jue Yang & Wei Long Liu & Cheng Lin Liao, 2020. "Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery," Energies, MDPI, vol. 13(15), pages 1-23, July.

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