State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches
<p dir="ltr">Precise estimation of both state-of-charge (SoC) and state-of-health (SoH) is crucial for optimizing electric vehicle (EV) performance and enhancing the battery lifetime, safety, and reliability, where machine learning (ML) plays a vital role in this regard. While existi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513539998220288 |
|---|---|
| author | Aya Haraz (22225036) |
| author2 | Khalid Abualsaud (16888701) Ahmed Massoud (16875996) |
| author2_role | author author |
| author_facet | Aya Haraz (22225036) Khalid Abualsaud (16888701) Ahmed Massoud (16875996) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Haraz (22225036) Khalid Abualsaud (16888701) Ahmed Massoud (16875996) |
| dc.date.none.fl_str_mv | 2024-10-28T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3486989 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/State-of-Health_and_State-of-Charge_Estimation_in_Electric_Vehicles_Batteries_A_Survey_on_Machine_Learning_Approaches/30095005 |
| 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 Machine learning Machine learning electric vehicles state-of-health state-of-charge lithium-ion batteries Batteries Estimation Maximum likelihood estimation Monitoring Surveys Lithium-ion batteries Battery management systems Safety Lead |
| dc.title.none.fl_str_mv | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Precise estimation of both state-of-charge (SoC) and state-of-health (SoH) is crucial for optimizing electric vehicle (EV) performance and enhancing the battery lifetime, safety, and reliability, where machine learning (ML) plays a vital role in this regard. While existing surveys explore ML applications in EVs, they often need to address ML approaches for SoC and SoH estimation. This paper bridges this gap by comprehensively reviewing how ML is utilized for SoC and SoH estimation, analyzing their strengths and weaknesses across different battery chemistries. Our review offers a systematic breakdown of critical areas: fundamental concepts and functionalities of prominent ML techniques for estimating SoC and SoH, a comparative evaluation of ML techniques applied to diverse EV battery types, an exploration of SoC and SoH estimation using modeling approaches within EV battery systems, and the critical role of dataset quality and model evaluation criteria. Moreover, this paper addresses ML tools developed for lithium-ion batteries (LiBs), image processing applications in EV batteries, and an in-depth investigation of the system model for ML-based SoH and SoC estimation. Furthermore, we present key concepts and methods for SoH and SoC estimation utilizing ML, compare input features, metrics, hyperparameters, and datasets, and demonstrate ML-based system models for EV battery estimation. By conducting this thorough analysis, we aim to close the existing gap and stimulate future progress in ML for SoH and SoC estimation, primarily focusing on LiBs across different EV applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/access.2024.3486989" target="_blank">https://dx.doi.org/10.1109/access.2024.3486989</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_63ed234ecb605f575ecf8d3c88014c16 |
| identifier_str_mv | 10.1109/access.2024.3486989 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30095005 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning ApproachesAya Haraz (22225036)Khalid Abualsaud (16888701)Ahmed Massoud (16875996)EngineeringElectrical engineeringInformation and computing sciencesMachine learningMachine learningelectric vehiclesstate-of-healthstate-of-chargelithium-ion batteriesBatteriesEstimationMaximum likelihood estimationMonitoringSurveysLithium-ion batteriesBattery management systemsSafetyLead<p dir="ltr">Precise estimation of both state-of-charge (SoC) and state-of-health (SoH) is crucial for optimizing electric vehicle (EV) performance and enhancing the battery lifetime, safety, and reliability, where machine learning (ML) plays a vital role in this regard. While existing surveys explore ML applications in EVs, they often need to address ML approaches for SoC and SoH estimation. This paper bridges this gap by comprehensively reviewing how ML is utilized for SoC and SoH estimation, analyzing their strengths and weaknesses across different battery chemistries. Our review offers a systematic breakdown of critical areas: fundamental concepts and functionalities of prominent ML techniques for estimating SoC and SoH, a comparative evaluation of ML techniques applied to diverse EV battery types, an exploration of SoC and SoH estimation using modeling approaches within EV battery systems, and the critical role of dataset quality and model evaluation criteria. Moreover, this paper addresses ML tools developed for lithium-ion batteries (LiBs), image processing applications in EV batteries, and an in-depth investigation of the system model for ML-based SoH and SoC estimation. Furthermore, we present key concepts and methods for SoH and SoC estimation utilizing ML, compare input features, metrics, hyperparameters, and datasets, and demonstrate ML-based system models for EV battery estimation. By conducting this thorough analysis, we aim to close the existing gap and stimulate future progress in ML for SoH and SoC estimation, primarily focusing on LiBs across different EV applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/access.2024.3486989" target="_blank">https://dx.doi.org/10.1109/access.2024.3486989</a></p>2024-10-28T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3486989https://figshare.com/articles/journal_contribution/State-of-Health_and_State-of-Charge_Estimation_in_Electric_Vehicles_Batteries_A_Survey_on_Machine_Learning_Approaches/30095005CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300950052024-10-28T15:00:00Z |
| spellingShingle | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches Aya Haraz (22225036) Engineering Electrical engineering Information and computing sciences Machine learning Machine learning electric vehicles state-of-health state-of-charge lithium-ion batteries Batteries Estimation Maximum likelihood estimation Monitoring Surveys Lithium-ion batteries Battery management systems Safety Lead |
| status_str | publishedVersion |
| title | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| title_full | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| title_fullStr | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| title_full_unstemmed | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| title_short | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| title_sort | State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches |
| topic | Engineering Electrical engineering Information and computing sciences Machine learning Machine learning electric vehicles state-of-health state-of-charge lithium-ion batteries Batteries Estimation Maximum likelihood estimation Monitoring Surveys Lithium-ion batteries Battery management systems Safety Lead |