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A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Author

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  • Shaofei Qu

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Yongzhe Kang

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Pingwei Gu

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Chenghui Zhang

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Bin Duan

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China
    State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

Abstract

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage ( dQ / dV ) at different states of charge (SoC). This method estimates SoH using arbitrary dQ / dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

Suggested Citation

  • Shaofei Qu & Yongzhe Kang & Pingwei Gu & Chenghui Zhang & Bin Duan, 2019. "A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis," Energies, MDPI, vol. 12(17), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3333-:d:262112
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    References listed on IDEAS

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    1. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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    5. Pingwei Gu & Zhongkai Zhou & Shaofei Qu & Chenghui Zhang & Bin Duan, 2019. "Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery," Energies, MDPI, vol. 12(7), pages 1-19, March.
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    Cited by:

    1. Binghong Han & Jonathon R. Harding & Johanna K. S. Goodman & Zhuhua Cai & Quinn C. Horn, 2022. "End-of-Charge Temperature Rise and State-of-Health Evaluation of Aged Lithium-Ion Battery," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Li, Alan G. & Wang, Weizhong & West, Alan C. & Preindl, Matthias, 2022. "Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning," Applied Energy, Elsevier, vol. 315(C).
    3. Abdelghani Djeddi & Djalel Dib & Ahmad Taher Azar & Salem Abdelmalek, 2019. "Fractional Order Unknown Inputs Fuzzy Observer for Takagi–Sugeno Systems with Unmeasurable Premise Variables," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    4. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 12(19), pages 1-22, September.

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