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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Vydané v:	Applied energy Ročník 300; s. 117346
Hlavní autori:	Sui, Xin, He, Shan, Vilsen, Søren B., Meng, Jinhao, Teodorescu, Remus, Stroe, Daniel-Ioan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.10.2021
Predmet:	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
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