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Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function

Author

Listed:
  • Yi Chen

    (School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Qiang Miao

    (School of Mechatronics Engineering, University of Electronic Science and Technology of China,Chengdu 611731, China)

  • Bin Zheng

    (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences,Chongqing 401120, China)

  • Shaomin Wu

    (Kent Business School, University of Kent, Canterbury CT2 7PE, UK)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering, University of Maryland, MD 20742, USA)

Abstract

Batteries are one of the most important components in many mechatronics systems, as they supply power to the systems and their failures may lead to reduced performance or even catastrophic results. Therefore, the prediction analysis of remaining useful life (RUL) of batteries is very important. This paper develops a quantitative approach for battery RUL prediction using an adaptive bathtub-shaped function (ABF). ABF has been utilised to model the normalised battery cycle capacity prognostic curves, which attempt to predict the remaining battery capacity with given historical test data. An artificial fish swarm algorithm method with a variable population size (AFSAVP) is employed as the optimiser for the parameter determination of the ABF curves, in which the fitness function is defined in the form of a coefficient of determination (R2). A 4 x 2 cross-validation (CV) has been devised, and the results show that the method can work valuably for battery health management and battery life prediction.

Suggested Citation

  • Yi Chen & Qiang Miao & Bin Zheng & Shaomin Wu & Michael Pecht, 2013. "Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function," Energies, MDPI, vol. 6(6), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:6:p:3082-3096:d:26613
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    Citations

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    Cited by:

    1. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    2. Shuai Wang & Lingling Zhao & Xiaohong Su & Peijun Ma, 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression," Energies, MDPI, vol. 7(10), pages 1-17, October.
    3. Maya Santhira Sekeran & Milan Živadinović & Myra Spiliopoulou, 2022. "Transferability of a Battery Cell End-of-Life Prediction Model Using Survival Analysis," Energies, MDPI, vol. 15(8), pages 1-16, April.
    4. Xuebing Han & Minggao Ouyang & Languang Lu & Jianqiu Li, 2014. "Cycle Life of Commercial Lithium-Ion Batteries with Lithium Titanium Oxide Anodes in Electric Vehicles," Energies, MDPI, vol. 7(8), pages 1-15, July.
    5. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
    6. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    7. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
    8. Su, Laisuo & Zhang, Jianbo & Wang, Caijuan & Zhang, Yakun & Li, Zhe & Song, Yang & Jin, Ting & Ma, Zhao, 2016. "Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments," Applied Energy, Elsevier, vol. 163(C), pages 201-210.

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