Joint prediction of state of health and remaining useful life for lithium-ion batteries based on health features optimization and multi-model fusion
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11581-024-05700-4.pdf
Reference35 articles.
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3. Yang P, Yang HD, Meng XB et al (2024) Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model. J Energy Storage 75:109741
4. Pang H, Chen KQ, Geng YF et al (2024) Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter. Energy 293:130555
5. Wei YP, Wu DZ (2024) State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network. Expert Syst Appl 238:122041
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