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Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model

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  • Xu, Zhicun
  • Xie, Naiming
  • Diao, Huakang

Abstract

Accurate assessment of the state of health of lithium-ion batteries using relevant factors is crucial for the maintenance of lithium-ion batteries in electric vehicles. Firstly, data features are extracted from University of Maryland public dataset and dataset is pre-processed. Secondly, the extracted features were analysed using a grey relational analysis model to identify the most significant factors affecting the state of health. Thirdly, this paper proposed an adaptive variable fractional order multivariate grey prediction model to accurately estimate the state of health of lithium-ion batteries. The comparative results demonstrate the overall superiority of the proposed model.

Suggested Citation

  • Xu, Zhicun & Xie, Naiming & Diao, Huakang, 2023. "Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025616
    DOI: 10.1016/j.energy.2023.129167
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    References listed on IDEAS

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    1. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
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    3. Li, Yihuan & Li, Kang & Liu, Xuan & Li, Xiang & Zhang, Li & Rente, Bruno & Sun, Tong & Grattan, Kenneth T.V., 2022. "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements," Applied Energy, Elsevier, vol. 325(C).
    4. Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
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