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...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
|---|---|
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20240514_9783036598758_227 |
| ট্যাগগুলো: |
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| _version_ | 1869524282345783296 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-137629 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1376292024-05-14T13:48:53Z Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries Teodorescu, Remus Sui, Xin 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 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. 2024-05-14T13:48:43Z 2024-05-14T13:48:43Z 2024 book ONIX_20240514_9783036598758_227 9783036598758 9783036598765 https://directory.doabooks.org/handle/20.500.12854/137629 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8825 https://mdpi.com/books/pdfview/book/8825 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-9876-5 10.3390/books978-3-0365-9876-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036598758 9783036598765 252 open access |
| spellingShingle | 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 Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title | Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title_full | Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title_fullStr | Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title_full_unstemmed | Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title_short | Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries |
| title_sort | artificial intelligence based state of health estimation of lithium ion batteries |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20240514_9783036598758_227 |