Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty
<p dir="ltr">Accurate state-of-charge (SoC) estimation is crucial for enhancing the performance, longevity, safety, and reliability of lithium-ion batteries (LiBs) in electric vehicles (EVs). This study presents a comprehensive machine learning (ML)-based approach for SoC estimation...
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2025
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| author | Aya Haraz (22225036) |
| author2 | Khalid Abualsaud (16888701) Ahmed M. Massoud (16896417) |
| author2_role | author author |
| author_facet | Aya Haraz (22225036) Khalid Abualsaud (16888701) Ahmed M. Massoud (16896417) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Haraz (22225036) Khalid Abualsaud (16888701) Ahmed M. Massoud (16896417) |
| dc.date.none.fl_str_mv | 2025-03-04T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3539792 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble_Learning_for_Precise_State-of-Charge_Estimation_in_Electric_Vehicles_Lithium-Ion_Batteries_Considering_Uncertainty/30234037 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Automotive engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Machine learning electric vehicles state-of-charge lithium-ion batteries ensemble model Extra Tree Regressor light gradient boosting machine uncertainty quantification Uncertainty Estimation Accuracy Data models Batteries Reliability Ensemble learning Computational modeling State of charge Robustness |
| dc.title.none.fl_str_mv | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accurate state-of-charge (SoC) estimation is crucial for enhancing the performance, longevity, safety, and reliability of lithium-ion batteries (LiBs) in electric vehicles (EVs). This study presents a comprehensive machine learning (ML)-based approach for SoC estimation of EV LiBs, addressing the challenges of model reliability, uncertainty, and real-world data variability. To ensure the model’s robustness and generalizability, preprocessing techniques, including normalization and scaling, were employed alongside rigorous cross-validation methods. A well-structured ML pipeline was developed to integrate these processes, optimizing the entire model development cycle for efficiency and practical implementation. In the ML pipeline, we utilized Extra Trees Regressor (ETR) and Light Gradient Boosting Machine (LightGBM) and proposed an ensemble model, combining the strengths of ETR and LightGBM, namely ETR-GBM. We benchmarked the model’s performance against other ML models, such as CatBoost and Random Forest (RF). Under uncertain conditions, the proposed model emphasized its reliability and robustness, and its conclusions underscored the efficacy of the SoC estimation approach. The ETR-GBM consistently outperforms the individual models (ETR, LightGBM, XGBoost, CatBoost, Support Vector Regression (SVR), Random Forest (RF), and Bayesian) when noise is added to the training dataset. With a noise standard deviation of 0.1, the ETR-GBM demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 0.41%, surpassing the individual models, which exhibited RMSE values ranging from 0.85% to 0.91%.</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.2025.3539792" target="_blank">https://dx.doi.org/10.1109/access.2025.3539792</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c56f20bcd0a0283210f25bc48ce56dac |
| identifier_str_mv | 10.1109/access.2025.3539792 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30234037 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering UncertaintyAya Haraz (22225036)Khalid Abualsaud (16888701)Ahmed M. Massoud (16896417)EngineeringAutomotive engineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningMachine learningelectric vehiclesstate-of-chargelithium-ion batteriesensemble modelExtra Tree Regressorlight gradient boosting machineuncertainty quantificationUncertaintyEstimationAccuracyData modelsBatteriesReliabilityEnsemble learningComputational modelingState of chargeRobustness<p dir="ltr">Accurate state-of-charge (SoC) estimation is crucial for enhancing the performance, longevity, safety, and reliability of lithium-ion batteries (LiBs) in electric vehicles (EVs). This study presents a comprehensive machine learning (ML)-based approach for SoC estimation of EV LiBs, addressing the challenges of model reliability, uncertainty, and real-world data variability. To ensure the model’s robustness and generalizability, preprocessing techniques, including normalization and scaling, were employed alongside rigorous cross-validation methods. A well-structured ML pipeline was developed to integrate these processes, optimizing the entire model development cycle for efficiency and practical implementation. In the ML pipeline, we utilized Extra Trees Regressor (ETR) and Light Gradient Boosting Machine (LightGBM) and proposed an ensemble model, combining the strengths of ETR and LightGBM, namely ETR-GBM. We benchmarked the model’s performance against other ML models, such as CatBoost and Random Forest (RF). Under uncertain conditions, the proposed model emphasized its reliability and robustness, and its conclusions underscored the efficacy of the SoC estimation approach. The ETR-GBM consistently outperforms the individual models (ETR, LightGBM, XGBoost, CatBoost, Support Vector Regression (SVR), Random Forest (RF), and Bayesian) when noise is added to the training dataset. With a noise standard deviation of 0.1, the ETR-GBM demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 0.41%, surpassing the individual models, which exhibited RMSE values ranging from 0.85% to 0.91%.</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.2025.3539792" target="_blank">https://dx.doi.org/10.1109/access.2025.3539792</a></p>2025-03-04T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3539792https://figshare.com/articles/journal_contribution/Ensemble_Learning_for_Precise_State-of-Charge_Estimation_in_Electric_Vehicles_Lithium-Ion_Batteries_Considering_Uncertainty/30234037CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302340372025-03-04T06:00:00Z |
| spellingShingle | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty Aya Haraz (22225036) Engineering Automotive engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Machine learning electric vehicles state-of-charge lithium-ion batteries ensemble model Extra Tree Regressor light gradient boosting machine uncertainty quantification Uncertainty Estimation Accuracy Data models Batteries Reliability Ensemble learning Computational modeling State of charge Robustness |
| status_str | publishedVersion |
| title | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| title_full | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| title_fullStr | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| title_full_unstemmed | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| title_short | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| title_sort | Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty |
| topic | Engineering Automotive engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Machine learning electric vehicles state-of-charge lithium-ion batteries ensemble model Extra Tree Regressor light gradient boosting machine uncertainty quantification Uncertainty Estimation Accuracy Data models Batteries Reliability Ensemble learning Computational modeling State of charge Robustness |