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State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction

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  • Zhang, Yongzhi
  • Xiong, Rui
  • He, Hongwen
  • Qu, Xiaobo
  • Pecht, Michael

Abstract

Capacity loss modeling is required for accurate and reliable lifetime evaluation of lithium-ion batteries. The current capacity loss model parameters cannot be identified online. To address this problem, this paper has developed a capacity loss model based on the aging mechanisms of solid electrolyte interface layer growth and active material loss. Experimental results show that capacity loss due to solid electrolyte interface growth is independent of state of charge ranges during cycling, whereas capacity loss due to active material loss depends on the state of charge ranges. A comprehensive aging model is thus developed, combined with the recursive least squares method to identify the model parameters in realtime. In our case studies, the estimation errors of the capacity loss model are within 1% under different state of charge ranges. To avoid the modeling error caused by cell characteristic inconsistencies, model parameters are further updated adaptively based on online data for predicting the accurate lifetime of the specific cell.

Suggested Citation

  • Zhang, Yongzhi & Xiong, Rui & He, Hongwen & Qu, Xiaobo & Pecht, Michael, 2019. "State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315053
    DOI: 10.1016/j.apenergy.2019.113818
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    Citations

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    Cited by:

    1. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
    2. He, Guannan & Ciez, Rebecca & Moutis, Panayiotis & Kar, Soummya & Whitacre, Jay F., 2020. "The economic end of life of electrochemical energy storage," Applied Energy, Elsevier, vol. 273(C).
    3. 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.
    4. Tian, Jiaqiang & Xu, Ruilong & Wang, Yujie & Chen, Zonghai, 2021. "Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries," Energy, Elsevier, vol. 221(C).
    5. Zhou, Yu & Ong, Ghim Ping & Meng, Qiang, 2023. "The road to electrification: Bus fleet replacement strategies," Applied Energy, Elsevier, vol. 337(C).
    6. Fan, Chuanxin & O’Regan, Kieran & Li, Liuying & Higgins, Matthew D. & Kendrick, Emma & Widanage, Widanalage D., 2022. "Data-driven identification of lithium-ion batteries: A nonlinear equivalent circuit model with diffusion dynamics," Applied Energy, Elsevier, vol. 321(C).
    7. Song, Aoye & Zhou, Yuekuan, 2023. "A hierarchical control with thermal and electrical synergies on battery cycling ageing and energy flexibility in a multi-energy sharing network," Renewable Energy, Elsevier, vol. 212(C), pages 1020-1037.
    8. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Zhang, Le & Wang, Shuaian & Qu, Xiaobo, 2021. "Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).

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