Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

As one of the key technologies in battery management system, accurate remaining useful life (RUL) prediction is critical to guarantee the reliability and safety for electrical equipment. However, the generalization and robustness of a single method are limited. A novel fusion data‐driven RUL predict...

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Vydané v:	Quality and reliability engineering international Ročník 40; číslo 4; s. 1833 - 1854
Hlavní autori:	Zheng, Xiujuan, Wu, Fei, Tao, Liujun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bognor Regis Wiley Subscription Services, Inc 01.06.2024
Predmet:	Aeronautics Artificial neural networks chaotic sparrow search algorithm Charging Correlation coefficients Degradation Discharge Electric equipment Errors extreme learning machine fusion data‐driven algorithm least squares support vector regression Lithium-ion batteries Machine learning random forest regressor remaining useful life Search algorithms Support vector machines Weighting
ISSN:	0748-8017, 1099-1638
On-line prístup:	Získať plný text
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