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Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

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  • Khaleghi, Sahar
  • Hosen, Md Sazzad
  • Karimi, Danial
  • Behi, Hamidreza
  • Beheshti, S. Hamidreza
  • Van Mierlo, Joeri
  • Berecibar, Maitane

Abstract

Lithium-ion batteries have achieved dominance in energy storage systems. Meanwhile, there is a demand for the reliability of lithium-ion batteries. Battery prognostics and health management (PHM) is a discipline that not only provides accurate, early, and online health diagnosis, but also guarantees a robust and precise prediction of the remaining useful life of lithium-ion batteries, independent of the operating conditions. This paper attempts to develop a novel PHM methodology that addresses the points mentioned above. A large dataset including thirty-eight nickel manganese cobalt oxide battery cells is used. The battery cells have been tested under various test conditions to achieve different aging patterns. Afterward, the health indicators that describe the health trajectory of the battery are extracted from partial charging voltage curves. A recurrent neural network called nonlinear autoregressive with exogenous input is developed to estimate battery state of health (SOH) based on the extracted health indicators. The estimated SOH is used as the prognostic feature to develop a remaining useful life of battery (RUL) prediction model based on the similarity-based model. The proposed methods are validated using untrained data. The results indicate that the proposed PHM methodology can estimate the SOH of untrained battery cells with a maximum RMSE of 0.61. The RUL of battery cells with different aging patterns can be predicted with a maximum absolute error of 110 cycles. It can be concluded that the proposed method has the advantages of high precision in the health diagnosis and prognosis of battery cells regardless of their aging patterns, simplicity, and generalization to untrained data. These advantages point out the feasibility of the proposed method for online prognostics and health management of lithium-ion batteries.

Suggested Citation

  • Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015956
    DOI: 10.1016/j.apenergy.2021.118348
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    Cited by:

    1. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Karimi, Danial & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2023. "A comprehensive coupled 0D-ECM to 3D-CFD thermal model for heat pipe assisted-air cooling thermal management system under fast charge and discharge," Applied Energy, Elsevier, vol. 339(C).
    3. Md Sazzad Hosen & Poonam Yadav & Joeri Van Mierlo & Maitane Berecibar, 2023. "A Post-Mortem Study Case of a Dynamically Aged Commercial NMC Cell," Energies, MDPI, vol. 16(3), pages 1-14, January.
    4. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    6. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    7. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
    8. Danial Karimi & Hamidreza Behi & Joeri Van Mierlo & Maitane Berecibar, 2022. "An Experimental Study on Thermal Performance of Graphite-Based Phase-Change Materials for High-Power Batteries," Energies, MDPI, vol. 15(7), pages 1-13, March.
    9. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).
    11. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    12. Bockrath, Steffen & Lorentz, Vincent & Pruckner, Marco, 2023. "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles," Applied Energy, Elsevier, vol. 329(C).

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