Data-driven full life-cycle state parameter assessment of Li-ion batteries
Author:
Publisher
Science China Press., Co. Ltd.
Subject
Multidisciplinary
Link
https://engine.scichina.com/doi/pdf/106D2F5D3E684226831E85C3EEE44AA6
Reference71 articles.
1. Peng J, Zhou Z, Wang J. Residual remaining useful life prediction method for lithium-ion batteries in satellite with incomplete healthy historical data. IEEE Access, 2019, 7: 127788-127799.
2. Wang C F, Cao F, Li M J. Research status and future development of thermalmanagement system for new energy vehicles underthe background of carbon neutrality (in Chinese). Chin Sci Bull, 2021, 66: 4112-4128.
3. Kim T, Song W, Son D Y. Lithium-ion batteries: Outlook on present, future, and hybridized technologies. J Mater Chem A, 2019, 7: 2942-2964.
4. Hu X, Feng F, Liu K. State estimation for advanced battery management: Key challenges and future trends. Renew Sustain Energy Rev, 2019, 114:
5. Sidhu M S, Ronanki D, Williamson S, et al. State of charge estimation of lithium-ion batteries using hybrid machine learning technique. In: 45th Annual Conference of the IEEE Industrial Electronics Society. 2019. 2732–2737.
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