Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study
<p dir="ltr">Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513545524215808 |
|---|---|
| author | Mohamed Massaoudi (16888710) |
| author2 | Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author2_role | author author |
| author_facet | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| dc.date.none.fl_str_mv | 2024-01-31T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojia.2024.3354899 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advancing_Lithium-Ion_Battery_Health_Prognostics_With_Deep_Learning_A_Review_and_Case_Study/29445725 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Deep learning (DL) health and life-cycle analysis lithium-ion battery (LIB) management system prognostics and health management (PHM) remaining useful life (RUL) prediction state of charge (SOC) estimation Batteries Data models Estimation Mathematical models Aging Integrated circuit modeling State of charge |
| dc.title.none.fl_str_mv | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review article analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of Industry Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojia.2024.3354899" target="_blank">https://dx.doi.org/10.1109/ojia.2024.3354899</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1bc31b355d64a386f475a68f0515ab51 |
| identifier_str_mv | 10.1109/ojia.2024.3354899 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445725 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case StudyMohamed Massaoudi (16888710)Haitham Abu-Rub (16855500)Ali Ghrayeb (16864266)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceDeep learning (DL)health and life-cycle analysislithium-ion battery (LIB) management systemprognostics and health management (PHM)remaining useful life (RUL) predictionstate of charge (SOC) estimationBatteriesData modelsEstimationMathematical modelsAgingIntegrated circuit modelingState of charge<p dir="ltr">Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review article analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of Industry Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojia.2024.3354899" target="_blank">https://dx.doi.org/10.1109/ojia.2024.3354899</a></p>2024-01-31T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojia.2024.3354899https://figshare.com/articles/journal_contribution/Advancing_Lithium-Ion_Battery_Health_Prognostics_With_Deep_Learning_A_Review_and_Case_Study/29445725CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294457252024-01-31T06:00:00Z |
| spellingShingle | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study Mohamed Massaoudi (16888710) Engineering Electrical engineering Information and computing sciences Artificial intelligence Deep learning (DL) health and life-cycle analysis lithium-ion battery (LIB) management system prognostics and health management (PHM) remaining useful life (RUL) prediction state of charge (SOC) estimation Batteries Data models Estimation Mathematical models Aging Integrated circuit modeling State of charge |
| status_str | publishedVersion |
| title | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| title_full | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| title_fullStr | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| title_full_unstemmed | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| title_short | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| title_sort | Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Deep learning (DL) health and life-cycle analysis lithium-ion battery (LIB) management system prognostics and health management (PHM) remaining useful life (RUL) prediction state of charge (SOC) estimation Batteries Data models Estimation Mathematical models Aging Integrated circuit modeling State of charge |