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Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery

Author

Listed:
  • Daehyun Kim

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea)

  • Taedong Goh

    (Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea)

  • Minjun Park

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea)

  • Sang Woo Kim

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea)

Abstract

We propose a state-of-charge (SOC) estimation method for Li-ion batteries that combines a fuzzy sliding mode observer (FSMO) with grey prediction. Unlike the existing methods based on a conventional first-order sliding mode observer (SMO) and an adaptive gain SMO, the proposed method eliminates chattering in SOC estimation. In this method, which uses a fuzzy inference system, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test). The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy.

Suggested Citation

  • Daehyun Kim & Taedong Goh & Minjun Park & Sang Woo Kim, 2015. "Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery," Energies, MDPI, vol. 8(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12327-12428:d:58213
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    References listed on IDEAS

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    1. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    2. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

    1. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    3. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
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    5. Xiaodong Chang & Jinquan Huang & Feng Lu, 2017. "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine," Energies, MDPI, vol. 10(7), pages 1-19, July.
    6. Truong Quang Dinh & James Marco & Hui Niu & David Greenwood & Lee Harper & David Corrochano, 2017. "A Novel Method for Idle-Stop-Start Control of Micro Hybrid Construction Equipment—Part A: Fundamental Concepts and Design," Energies, MDPI, vol. 10(7), pages 1-24, July.
    7. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    8. Robert Salas-Puente & Silvia Marzal & Raul Gonzalez-Medina & Emilio Figueres & Gabriel Garcera, 2018. "Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization," Energies, MDPI, vol. 11(7), pages 1-31, July.

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