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A novel data-model fusion state-of-health estimation approach for lithium-ion batteries

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  • Ma, Zeyu
  • Yang, Ruixin
  • Wang, Zhenpo

Abstract

In order to ensure the efficient, reliable, and safe operation of the lithium-ion battery system, an accurate battery state-of-health estimation is essential and remaining challenges. Here we propose a novel data-model fusion battery state-of-health estimation approach based on open-circuit-voltage parametric modeling considering the correlation between capacity degradation and the open-circuit-voltage changes. An open-circuit-voltage model is built to capture the aging behavior associated with the reactions progress in the cell. Then the battery state-of-health estimation approach is developed based on the correlation between capacity fade and the changes of the open-circuit-voltage model parameters. In addition, a data-driven based method is applied to identify the parameters of the proposed battery model to obtain the open-circuit-voltage online. The proposed state-of-health estimation approach has been verified by the cells experienced different aging paths. The results show that the average relative errors of the state-of-health estimation for all cells are less than 3% against different aging paths and levels.

Suggested Citation

  • Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:836-847
    DOI: 10.1016/j.apenergy.2018.12.071
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    Cited by:

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    3. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    4. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).
    5. Donghoon Shin & Beomjin Yoon & Seungryeol Yoo, 2021. "Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter," Energies, MDPI, vol. 14(2), pages 1-19, January.
    6. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    7. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
    8. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
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    10. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
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    13. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    14. Bai, Shanming & Cui, Juan & Zheng, Yongqiu & Li, Gang & Liu, Tingshan & Liu, Yabing & Hao, Congcong & Xue, Chenyang, 2023. "Electromagnetic-triboelectric energy harvester based on vibration-to-rotation conversion for human motion energy exploitation," Applied Energy, Elsevier, vol. 329(C).
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