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Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method

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  • Zhang, Caiping
  • Wang, Yubin
  • Gao, Yang
  • Wang, Fang
  • Mu, Biqiang
  • Zhang, Weige

Abstract

The requirement for energy density of lithium-ion batteries becomes more urgent due to the rising demand for driving range of electric vehicles in recent years. Meanwhile, the performance stability of batteries with high energy densities tends to deteriorate, leading to accelerating degradation and safety issues. As a result, it is critical to explore the reasons that yield the sudden degradation and to recognize the degradation knee point of Nickel-Cobalt-Manganese batteries commonly used for electric vehicles. Existing results have disclosed that the lithium deposition of negative electrode dominates the sudden degradation of battery capacity. This paper extracts key parameters that characterize the aging status to facilitate knee point recognition in engineering practice. Furthermore, a novel method that integrates quantile regression and Monte Carlo simulation method to identify the accelerated fading knee point is introduced. The dynamic safety boundary determination method for the whole battery lifetime is proposed to update and monitor the safety zone. It is verified by experiments that the recognition results of capacity degradation knee point appear within 90–95% capacity range at 25 °C, 35 °C and 45 °C conditions, which can provide an early warning before the battery fails. Using the proposed method for recognizing the sudden degradation of capacity, recognition result is effective even if the input is disturbed and has strong reliability and stability under different conditions. It is helpful to promote the sustainable and stable development of the electric vehicles and improve advanced applied energy technologies.

Suggested Citation

  • Zhang, Caiping & Wang, Yubin & Gao, Yang & Wang, Fang & Mu, Biqiang & Zhang, Weige, 2019. "Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919315284
    DOI: 10.1016/j.apenergy.2019.113841
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    3. Ma, Yan & Li, Jiaqi & Gao, Jinwu & Chen, Hong, 2024. "State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature," Energy, Elsevier, vol. 295(C).
    4. Cai, Mingjing & Wang, Jiahua & Liao, Wei-Hsin, 2020. "Self-powered smart watch and wristband enabled by embedded generator," Applied Energy, Elsevier, vol. 263(C).
    5. Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(C).
    6. Su, Xiaojia & Sun, Bingxiang & Wang, Jiaju & Zhang, Weige & Ma, Shichang & He, Xitian & Ruan, Haijun, 2022. "Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression," Applied Energy, Elsevier, vol. 322(C).
    7. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    8. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    9. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    10. Ma, Jian & Shang, Pengchao & Zou, Xinyu & Ma, Ning & Ding, Yu & Sun, Jinwen & Cheng, Yujie & Tao, Laifa & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2021. "A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries," Applied Energy, Elsevier, vol. 282(PA).
    11. Sieg, Johannes & Schmid, Alexander U. & Rau, Laura & Gesterkamp, Andreas & Storch, Mathias & Spier, Bernd & Birke, Kai Peter & Sauer, Dirk Uwe, 2022. "Fast-charging capability of lithium-ion cells: Influence of electrode aging and electrolyte consumption," Applied Energy, Elsevier, vol. 305(C).
    12. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    13. Lee, Jaewook & Lee, Jay H., 2024. "Simultaneous extraction of intra- and inter-cycle features for predicting lithium-ion battery's knees using convolutional and recurrent neural networks," Applied Energy, Elsevier, vol. 356(C).

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