Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Electrochemistry,Condensed Matter Physics,General Materials Science,Energy Engineering and Power Technology,Materials Chemistry
Link
https://link.springer.com/content/pdf/10.1007/s10008-023-05650-3.pdf
Reference37 articles.
1. Zhao S, Guo Z, Yan K et al (2020) Towards high-energy-density lithium-ion batteries: strategies for developing high-capacity lithium-rich cathode materials
2. Zhang H, Liu X, Li HH et al (2021) Challenges and strategies for high-energy aqueous electrolyte rechargeable batteries. Angewandte Chemie-International Edition 60(2):598–616
3. Moosavi A, Ljung AL, Lundström TS (2021) Design considerations to prevent thermal hazards in cylindrical lithium-ion batteries: an analytical study. J Energy Storage 3:102525.
4. He B, Liu L, Zhang D (2021) Digital twin-driven remaining useful life prediction for gear performance degradation: a review. J Comput Inform Sci Eng 21(3)
5. Hasib SA, Islam S, Chakrabortty RK et al (2021) A Comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. IEEE Access 9:86166–86193
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