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Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Author

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  • Shuai Wang

    (Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China)

  • Lingling Zhao

    (Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China)

  • Xiaohong Su

    (Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China)

  • Peijun Ma

    (Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China)

Abstract

Accurate prediction of the remaining useful life ( RUL ) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression ( F-SVR ) and an iterative multi-step prediction model based on support vector regression ( SVR ) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6492-6508:d:41058
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    References listed on IDEAS

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    Cited by:

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    2. Lin, Chun-Pang & Cabrera, Javier & Yang, Fangfang & Ling, Man-Ho & Tsui, Kwok-Leung & Bae, Suk-Joo, 2020. "Battery state of health modeling and remaining useful life prediction through time series model," Applied Energy, Elsevier, vol. 275(C).
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    5. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
    6. Pei Wang & Xue Dan & Yong Yang, 2019. "A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    7. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    8. 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.
    9. Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
    10. 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).
    11. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    12. Zhang, Xiang & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & Wang, Zhenpo, 2023. "A novel battery abnormality detection method using interpretable Autoencoder," Applied Energy, Elsevier, vol. 330(PB).
    13. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Zhengyu Liu & Jingjie Zhao & Hao Wang & Chao Yang, 2020. "A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs," Energies, MDPI, vol. 13(4), pages 1-17, February.
    15. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).

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