A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address t...

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Bibliografiset tiedot
Päätekijät: Jun Peng, Zhiyong Zheng, Xiaoyong Zhang, Kunyuan Deng, Kai Gao, Heng Li, Bin Chen, Yingze Yang, Zhiwu Huang
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: MDPI AG 2020-02-01
Sarja:Energies
Aiheet:
lithium-ion battery
remaining useful life
gradient boosting decision trees
the box-cox transformation
time window
particle swarm optimization
Linkit:https://www.mdpi.com/1996-1073/13/3/752
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