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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-current variations

Author

Listed:
  • Wang, Shunli
  • Wu, Fan
  • Takyi-Aninakwa, Paul
  • Fernandez, Carlos
  • Stroe, Daniel-Ioan
  • Huang, Qi

Abstract

For the development of low-temperature power systems in aviation, the transport synergistic carrier optimization of lithium-ions and electrons is conducted to improve the low-temperature adaptability of lithium-ion batteries. In this paper, an improved robust multi-time scale singular filtering-Gaussian process regression-long short-term memory (SF-GPR-LSTM) modeling method is proposed for the remaining capacity estimation. The optimized multi-task training strategy is constructed for the rapid battery performance evaluation, realizing the refined mathematical dynamic characterization for the mapping relationship of the physical carrier transports to obtain the simultaneous improvement of multi-dimensional physical features and a spiral-up iterative optimization scheme. The adaptability of the model is verified using datasets from the whole-life-cycle test conducted on two batteries, and evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared metrics. Even when only using 55% of the datasets to estimate the remaining capacity, the estimation has good effects, with RMSE of 2.3484%, MAE of 0.82526%, MAPE of 0.90716%, and R-squared value of 92.457%. The proposed SF-GPR-LSTM model enables the carrier transport synergistic optimization effectively, laying a theoretical foundation for the whole-life-cycle battery remaining capacity estimation at extremely low temperatures.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223020716
    DOI: 10.1016/j.energy.2023.128677
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    1. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    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).
    3. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    5. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Zhengxin, Jiang & Qin, Shi & Yujiang, Wei & Hanlin, Wei & Bingzhao, Gao & Lin, He, 2021. "An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery," Energy, Elsevier, vol. 230(C).
    7. 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).
    8. Zonghai Chen & Yang Ren & Andrew N. Jansen & Chi-kai Lin & Wei Weng & Khalil Amine, 2013. "New class of nonaqueous electrolytes for long-life and safe lithium-ion batteries," Nature Communications, Nature, vol. 4(1), pages 1-8, June.
    9. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. 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).
    13. Wang, Wenya & Fan, L.W. & Zhou, P., 2022. "Evolution of global fossil fuel trade dependencies," Energy, Elsevier, vol. 238(PC).
    14. 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).
    15. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    16. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. 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).
    18. Vasilyev, A. & Andrews, J. & Dunnett, S.J. & Jackson, L.M., 2021. "Dynamic Reliability Assessment of PEM Fuel Cell Systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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