A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

•A comprehensive review of non-probabilistic machine learning for battery SOH estimation is presented.•For every algorithm, the principle derivation process is provided followed by flow charts with a unified form.•The challenges and unresolved issues of battery SOH estimation using machine learning...

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Veröffentlicht in:	Applied energy Jg. 300; S. 117346
Hauptverfasser:	Sui, Xin, He, Shan, Vilsen, Søren B., Meng, Jinhao, Teodorescu, Remus, Stroe, Daniel-Ioan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.10.2021
Schlagworte:	Battery management system Deep learning durability energy Health monitoring lithium batteries Lithium-ion battery Machine learning neural networks State of health support vector machines Lithium-ion battery ANN DE CNN DNN State of health LR ELM DL LDA Battery management system SVM Deep learning LS-SVM RNN GD ML Health monitoring K-NN FFNN CC ESN LSTM SBS EL SOH PSO PCA PCC SCC CV RF Machine learning GRA
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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