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Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: [Erscheinungsort nicht ermittelbar] : MDPI - Multidisciplinary Digital Publishing Institute, 2024Beschreibung: 1 Online-Ressource (1 electronic resource 252 p.)ISBN:- 9783036598758
- 9783036598765
- Mathematics and Science
- Mathematics
- Applied mathematics
- ANN
- CFD modelling
- Gaussian process regression
- LSTM
- SOH estimation
- Smart Battery
- aging
- artificial intelligence
- attention mechanism
- battery aging
- battery degradation
- battery management system
- bidirectional long- and short-term memory
- capacitance
- capacity estimation
- convolutional neural network
- data driven
- data mining
- electric vehicle
- extreme temperature
- gated recurrent unit neural network (GRU NN)
- health indicators
- health indicators (HIs)
- health prediction
- lifetime extension
- lifetime prediction
- linear regression
- lithium-ion batteries
- lithium-ion batteries (LIBs)
- lithium-ion battery
- long short-term memory recurrent neural network (LSTM-RNN)
- machine learning
- metabolic even grey model
- multi-output Gaussian process regression
- neural networks
- optimization design
- parameter identification
- pulse current
- real-world data
- remaining-useful-life (RUL)
- second-life applications
- state of charge estimation
- state of health
- state of health (SOH)
- state-of-charge estimation
- state-of-health
- supervised learning
- temporal convolutional
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eng