Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries
Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime, which is 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 t...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20260416T142754_9783725861873_50 |
| الوسوم: |
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| _version_ | 1869523937218527232 |
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| collection | Directory of Open Access Books |
| description | Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime, which is 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. Therefore, this Special Issue showcase manuscripts showing efficient SOH estimation methods using AI which exhibit good performance, such as high accuracy, high robustness against the changes in working conditions, good generalization, etc. |
| format | Online |
| id | doab-20.500.12854ir-175145 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1751452026-04-16T19:10:00Z Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries Teodorescu, Remus Sui, Xin Artificial intelligence Lithium-ion batteries State-of-health estimation Physics-informed machine learning Battery diagnostics Degradation modeling Smart battery management thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime, which is 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. Therefore, this Special Issue showcase manuscripts showing efficient SOH estimation methods using AI which exhibit good performance, such as high accuracy, high robustness against the changes in working conditions, good generalization, etc. 2026-04-16T19:09:52Z 2026-04-16T19:09:52Z 2025 book ONIX_20260416T142754_9783725861873_50 9783725861873 9783725861880 https://directory.doabooks.org/handle/20.500.12854/175145 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12057 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6188-0 10.3390/books978-3-7258-6188-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725861873 9783725861880 184 CH open access |
| spellingShingle | Artificial intelligence Lithium-ion batteries State-of-health estimation Physics-informed machine learning Battery diagnostics Degradation modeling Smart battery management thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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 | Artificial intelligence Lithium-ion batteries State-of-health estimation Physics-informed machine learning Battery diagnostics Degradation modeling Smart battery management thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | Artificial intelligence Lithium-ion batteries State-of-health estimation Physics-informed machine learning Battery diagnostics Degradation modeling Smart battery management thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20260416T142754_9783725861873_50 |