Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but o...

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Format:	Online
Langue:	anglais
Publié:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Sujets:	thermal management lithium-ion batteries CFD modelling ANN optimization design capacitance state-of-charge estimation state-of-health aging lithium-ion battery health indicators state of health multi-output Gaussian process regression health prediction machine learning state of charge estimation temporal convolutional network extreme temperature convolutional neural network bidirectional long- and short-term memory attention mechanism Smart Battery artificial intelligence pulse current lifetime extension second-life applications remaining-useful-life (RUL) gated recurrent unit neural network (GRU NN) real-world data SOH estimation lifetime prediction neural networks supervised learning LSTM data mining battery aging battery management system capacity estimation electric vehicle battery degradation metabolic even grey model parameter identification state of health (SOH) lithium-ion batteries (LIBs) long short-term memory recurrent neural network (LSTM-RNN) health indicators (HIs) data driven linear regression Gaussian process regression thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Accès en ligne:	ONIX_20240514_9783036598758_227
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Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

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