Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning
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- Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
- Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
- Weiping Diao & Chetan Kulkarni & Michael Pecht, 2021. "Development of an Informative Lithium-Ion Battery Datasheet," Energies, MDPI, vol. 14(17), pages 1-19, September.
- Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
- Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
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Keywords
lithium-ion batteries; cycle life; temperature; C-rate; accelerated testing; machine learning;All these keywords.
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