Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm-Reference-Cited by-同舟云学术

Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm

Published:2024-04 Issue: Volume:244 Page:109913
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Guo Yongfang,Yu Xiangyuan,Wang Yashuang,Huang Kai^ORCID

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference70 articles.

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4. Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network;Xu;Reliab Eng Syst Saf,2023

5. Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection;Che;Reliab Eng Syst Saf,2024

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4. A comprehensive review of hybrid battery state of charge estimation: Exploring physics-aware AI-based approaches;Journal of Energy Storage;2024-10

5. Joint prediction of state of health and remaining useful life for lithium-ion batteries based on health features optimization and multi-model fusion;Ionics;2024-07-19