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A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

Author

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  • Yuan Chen

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Yigang He

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Zhong Li

    (Jianghuai Automobile Co. Ltd, Hefei 230092, China)

  • Liping Chen

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

Abstract

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.

Suggested Citation

  • Yuan Chen & Yigang He & Zhong Li & Liping Chen, 2019. "A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries," Energies, MDPI, vol. 12(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4376-:d:287978
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    References listed on IDEAS

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    1. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
    2. Ahmadian, Ali & Sedghi, Mahdi & Elkamel, Ali & Fowler, Michael & Aliakbar Golkar, Masoud, 2018. "Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2609-2624.
    3. Su, Laisuo & Zhang, Jianbo & Wang, Caijuan & Zhang, Yakun & Li, Zhe & Song, Yang & Jin, Ting & Ma, Zhao, 2016. "Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments," Applied Energy, Elsevier, vol. 163(C), pages 201-210.
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    Cited by:

    1. Dominik Dvorak & Daniele Basciotti & Imre Gellai, 2020. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-18, October.

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