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

Edited by
February 2024
252 pages
  • ISBN978-3-0365-9875-8 (Hardback)
  • ISBN978-3-0365-9876-5 (PDF)
https://doi.org/10.3390/books978-3-0365-9876-5
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This book is a reprint of the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries that was published in

Chemistry & Materials Science
Engineering
Physical Sciences
Summary

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 one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery’s state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
thermal management; lithium-ion batteries; CFD modelling; ANN; optimization design; capacitance; state-of-charge estimation; state-of-health; aging; lithium-ion battery; 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; lithium-ion battery; state of health; convolutional neural network; bidirectional long- and short-term memory; attention mechanism; Smart Battery; artificial intelligence; pulse current; lifetime extension; second-life applications; lithium-ion batteries; remaining-useful-life (RUL); gated recurrent unit neural network (GRU NN); real-world data; lithium-ion battery; SOH estimation; artificial intelligence; lifetime prediction; neural networks; supervised learning; LSTM; data mining; battery aging; lithium-ion battery; battery management system; capacity estimation; electric vehicle; battery degradation; lithium-ion battery; metabolic even grey model; parameter identification; state of charge estimation; state of health; lithium-ion battery; machine learning; battery management system; state of health (SOH); lithium-ion batteries (LIBs); long short-term memory recurrent neural network (LSTM-RNN); health indicators (HIs); data driven; state of health; lithium-ion batteries; linear regression; Gaussian process regression; machine learning