Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration
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DOI: 10.1016/j.energy.2024.130849
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- Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
- de Hoog, Joris & Timmermans, Jean-Marc & Ioan-Stroe, Daniel & Swierczynski, Maciej & Jaguemont, Joris & Goutam, Shovon & Omar, Noshin & Van Mierlo, Joeri & Van Den Bossche, Peter, 2017. "Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation," Applied Energy, Elsevier, vol. 200(C), pages 47-61.
- Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2023. "Planning of static and dynamic charging facilities for electric vehicles in electrified transportation networks," Energy, Elsevier, vol. 263(PE).
- Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
- Fan, Chuanxin & Liu, Kailong & Zhu, Tao & Peng, Qiao, 2024. "Understanding of Lithium-ion battery degradation using multisine-based nonlinear characterization method," Energy, Elsevier, vol. 290(C).
- Feng, Fei & Yang, Rui & Meng, Jinhao & Xie, Yi & Zhang, Zhiguo & Chai, Yi & Mou, Lisha, 2022. "Electrochemical impedance characteristics at various conditions for commercial solid–liquid electrolyte lithium-ion batteries: Part. 2. Modeling and prediction," Energy, Elsevier, vol. 243(C).
- Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2023. "A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris," Energy, Elsevier, vol. 285(C).
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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Keywords
Energy storage; Battery management; Health management; Data science;All these keywords.
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