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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
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2024
Subjects:
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
Online Access:ONIX_20240514_9783036598758_227
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