Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty
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 ch...
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2025-01-01
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| author | Aya Haraz Khalid Abualsaud Ahmed M. Massoud |
| author_facet | Aya Haraz Khalid Abualsaud Ahmed M. Massoud |
| author_sort | Aya Haraz |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-2e14aeeeb5f7460d896580f0b5bfd89c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-2e14aeeeb5f7460d896580f0b5bfd89c2025-08-20T03:15:27ZengIEEEIEEE Access2169-35362025-01-0113379903800110.1109/ACCESS.2025.353979210904247Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering UncertaintyAya Haraz0https://orcid.org/0009-0001-2801-1508Khalid Abualsaud1https://orcid.org/0000-0002-6693-3386Ahmed M. Massoud2https://orcid.org/0000-0001-9343-469XDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarAccurate 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%.https://ieeexplore.ieee.org/document/10904247/Machine learningelectric vehiclesstate-of-chargelithium-ion batteriesensemble modelExtra Tree Regressor |
| spellingShingle | Aya Haraz Khalid Abualsaud Ahmed M. Massoud Ensemble Learning for Precise State-of-Charge Estimation in Electric Vehicles Lithium-Ion Batteries Considering Uncertainty IEEE Access Machine learning electric vehicles state-of-charge lithium-ion batteries ensemble model Extra Tree Regressor |
| 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 | Machine learning electric vehicles state-of-charge lithium-ion batteries ensemble model Extra Tree Regressor |
| url | https://ieeexplore.ieee.org/document/10904247/ |
| work_keys_str_mv | AT ayaharaz ensemblelearningforprecisestateofchargeestimationinelectricvehicleslithiumionbatteriesconsideringuncertainty AT khalidabualsaud ensemblelearningforprecisestateofchargeestimationinelectricvehicleslithiumionbatteriesconsideringuncertainty AT ahmedmmassoud ensemblelearningforprecisestateofchargeestimationinelectricvehicleslithiumionbatteriesconsideringuncertainty |