Modeling, Reliability and Health Management of Lithium-Ion Batteries
As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal c...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Online pristup: | ONIX_20260416T142754_9783725861996_51 |
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| _version_ | 1869516488199634944 |
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| collection | Directory of Open Access Books |
| description | As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal challenges. This Reprint, representing the second edition of the Special Issue "Modeling, Reliability, and Health Management of Lithium-Ion Batteries," compiles eight significant research contributions and a comprehensive editorial that define the current state of the art in this field. The collected works address three interconnected themes. In the domain of modeling, authors introduce efficient parameter extraction methods for equivalent circuit models and solid-phase diffusion models for high-power LTO batteries. Reliability and safety are addressed through novel investigations into thermal runaway mechanisms, including pressure dynamics in prismatic cells and environmental impact evaluations, alongside robust fault diagnosis techniques utilizing relative entropy. Finally, the Reprint showcases breakthroughs in health management, featuring physics-guided machine learning for capacity fading prediction, autoencoder-based SOH estimation for small data samples. |
| format | Online |
| id | doab-20.500.12854ir-175146 |
| 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-1751462026-04-16T19:10:20Z Modeling, Reliability and Health Management of Lithium-Ion Batteries Feng, Fei Ling, Rui Xie, Yi Wang, Shunli Meng, Jinhao Xie, Jiale Fault detection Sliding windows Relative entropy SOC estimation Short-circuit resistance estimation Battery model Lithium titanium oxide (LTO) batteries Rate characteristics Fast charging Multi-stage constant current (MCC) charging Li-plating SOC Aging Li–ion batteries Capacity prediction Feature extraction Data–driven Machine learning Li-ion battery Thermal runaway Operation environment Mathematical equation Evaluation modeling MATLAB SIMULINK Equivalent circuit model Lithium-ion battery Battery model parametrization Autoregressive exogenous model Least squares linear regression Optimization Electric vehicles Internal pressure Simulation Modeling Small-sample data Battery state of health Deep learning N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology As the global transition toward carbon neutrality accelerates, Lithium-ion batteries (LIBs) have become the cornerstone of electric mobility and renewable energy storage. However, ensuring their safety and maximizing their lifespan requires precise solutions for complex electrochemical and thermal challenges. This Reprint, representing the second edition of the Special Issue "Modeling, Reliability, and Health Management of Lithium-Ion Batteries," compiles eight significant research contributions and a comprehensive editorial that define the current state of the art in this field. The collected works address three interconnected themes. In the domain of modeling, authors introduce efficient parameter extraction methods for equivalent circuit models and solid-phase diffusion models for high-power LTO batteries. Reliability and safety are addressed through novel investigations into thermal runaway mechanisms, including pressure dynamics in prismatic cells and environmental impact evaluations, alongside robust fault diagnosis techniques utilizing relative entropy. Finally, the Reprint showcases breakthroughs in health management, featuring physics-guided machine learning for capacity fading prediction, autoencoder-based SOH estimation for small data samples. 2026-04-16T19:10:11Z 2026-04-16T19:10:11Z 2025 book ONIX_20260416T142754_9783725861996_51 9783725861996 9783725862009 https://directory.doabooks.org/handle/20.500.12854/175146 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12058 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6200-9 10.3390/books978-3-7258-6200-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725861996 9783725862009 158 CH open access |
| spellingShingle | Fault detection Sliding windows Relative entropy SOC estimation Short-circuit resistance estimation Battery model Lithium titanium oxide (LTO) batteries Rate characteristics Fast charging Multi-stage constant current (MCC) charging Li-plating SOC Aging Li–ion batteries Capacity prediction Feature extraction Data–driven Machine learning Li-ion battery Thermal runaway Operation environment Mathematical equation Evaluation modeling MATLAB SIMULINK Equivalent circuit model Lithium-ion battery Battery model parametrization Autoregressive exogenous model Least squares linear regression Optimization Electric vehicles Internal pressure Simulation Modeling Small-sample data Battery state of health Deep learning N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title | Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title_full | Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title_fullStr | Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title_full_unstemmed | Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title_short | Modeling, Reliability and Health Management of Lithium-Ion Batteries |
| title_sort | modeling reliability and health management of lithium ion batteries |
| topic | Fault detection Sliding windows Relative entropy SOC estimation Short-circuit resistance estimation Battery model Lithium titanium oxide (LTO) batteries Rate characteristics Fast charging Multi-stage constant current (MCC) charging Li-plating SOC Aging Li–ion batteries Capacity prediction Feature extraction Data–driven Machine learning Li-ion battery Thermal runaway Operation environment Mathematical equation Evaluation modeling MATLAB SIMULINK Equivalent circuit model Lithium-ion battery Battery model parametrization Autoregressive exogenous model Least squares linear regression Optimization Electric vehicles Internal pressure Simulation Modeling Small-sample data Battery state of health Deep learning N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | Fault detection Sliding windows Relative entropy SOC estimation Short-circuit resistance estimation Battery model Lithium titanium oxide (LTO) batteries Rate characteristics Fast charging Multi-stage constant current (MCC) charging Li-plating SOC Aging Li–ion batteries Capacity prediction Feature extraction Data–driven Machine learning Li-ion battery Thermal runaway Operation environment Mathematical equation Evaluation modeling MATLAB SIMULINK Equivalent circuit model Lithium-ion battery Battery model parametrization Autoregressive exogenous model Least squares linear regression Optimization Electric vehicles Internal pressure Simulation Modeling Small-sample data Battery state of health Deep learning N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20260416T142754_9783725861996_51 |