Battery state of health modeling and remaining useful life prediction through time series model
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DOI: 10.1016/j.apenergy.2020.115338
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- Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
- 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).
- Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
- 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).
- 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).
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- 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).
- Mathieu, Romain & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2021. "Comparison of the impact of fast charging on the cycle life of three lithium-ion cells under several parameters of charge protocol and temperatures," Applied Energy, Elsevier, vol. 283(C).
- Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
- 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.
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- Kai Lv & Zhiqiang Ma & Caijilahu Bao & Guangchen Liu, 2024. "Indirect Prediction of Lithium-Ion Battery RUL Based on CEEMDAN and CNN-BiGRU," Energies, MDPI, vol. 17(7), pages 1-19, April.
- Zhang, Yong & Tu, Lei & Xue, Zhiwei & Li, Sai & Tian, Lulu & Zheng, Xiujuan, 2022. "Weight optimized unscented Kalman filter for degradation trend prediction of lithium-ion battery with error compensation strategy," Energy, Elsevier, vol. 251(C).
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Keywords
Autoregressive; Battery cycle aging; Degradation modeling; Parametric bootstrap; Remaining useful life; State of health;All these keywords.
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