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A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current

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  • Xu, Tingting
  • Peng, Zhen
  • Wu, Lifeng

Abstract

Accurate health status estimation and capacity prediction of lithium-ion batteries are important means to prevent a series of problems such as capacity loss, driving range and safety accidents caused by the aging of batteries. Research on battery capacity prediction based on constant discharge rate has become increasingly mature. However, as the main power source for electric vehicles, discharge current of lithium-ion battery is constantly changed by the influence of time-varying vehicle speed. Considering the effect of random variable current (RVC) discharge on battery capacity degradation, a novel predicting method for circulating capacity of lithium-ion battery is proposed. Firstly, features are extracted from the battery charging and discharging process. Secondly, the correlation between features and battery capacity is analyzed by using the grey relational analysis, and features with the higher correlation coefficient are selected as final health features. Thirdly, the online sequential extreme learning machine optimized by beetle antenna search is proposed and used to predict capacity of lithium-ion battery. Experimental results show that the minimum battery capacity RMSE predicted is 1.0294, and the cycle capacity error is mostly within the range of -3mAh∼3mAh, which proves that the method can more accurately estimate the capacity of lithium-ion batteries under RVC conditions.

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  • Xu, Tingting & Peng, Zhen & Wu, Lifeng, 2021. "A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220326372
    DOI: 10.1016/j.energy.2020.119530
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    References listed on IDEAS

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    Cited by:

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    5. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. 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.
    7. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    8. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
    9. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    10. Wang, Shunli & Wu, Fan & Takyi-Aninakwa, Paul & Fernandez, Carlos & Stroe, Daniel-Ioan & Huang, Qi, 2023. "Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-curren," Energy, Elsevier, vol. 284(C).
    11. Olabi, A.G. & Wilberforce, Tabbi & Sayed, Enas Taha & Abo-Khalil, Ahmed G. & Maghrabie, Hussein M. & Elsaid, Khaled & Abdelkareem, Mohammad Ali, 2022. "Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission," Energy, Elsevier, vol. 254(PA).
    12. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
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