A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data
Electric vehicles (EVs) are rising in the automotive industry, replacing combustion engines and increasing their global market presence. These vehicles offer zero emissions during operation and more straightforward maintenance. However, for such systems that rely heavily on battery capacity, precise...
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MDPI AG
2024-12-01
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| Series: | Journal of Low Power Electronics and Applications |
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| Online Access: | https://www.mdpi.com/2079-9268/14/4/59 |
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| author | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié |
| author_facet | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié |
| author_sort | Salma Ariche |
| collection | DOAJ |
| description | Electric vehicles (EVs) are rising in the automotive industry, replacing combustion engines and increasing their global market presence. These vehicles offer zero emissions during operation and more straightforward maintenance. However, for such systems that rely heavily on battery capacity, precisely determining the battery’s state of charge (SOC) presents a significant challenge due to its essential role in lithium-ion batteries. This research introduces a dual-phase testing approach, considering factors like HVAC use and road topography, and evaluating machine learning models such as linear regression, support vector regression, random forest regression, and neural networks using datasets from real-world driving conditions in European (Germany) and African (Morocco) contexts. The results validate that the proposed neural networks model does not overfit when evaluated using the dual-phase test method compared to previous studies. The neural networks consistently show high predictive precision across different scenarios within the datasets, outperforming other models by achieving the lowest mean squared error (MSE) and the highest R<sup>2</sup> values. |
| format | Article |
| id | doaj-art-f2cbaf5cb9834862987bf9f176f0b2e0 |
| institution | Kabale University |
| issn | 2079-9268 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Low Power Electronics and Applications |
| spelling | doaj-art-f2cbaf5cb9834862987bf9f176f0b2e02024-12-27T14:32:42ZengMDPI AGJournal of Low Power Electronics and Applications2079-92682024-12-011445910.3390/jlpea14040059A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving DataSalma Ariche0Zakaria Boulghasoul1Abdelhafid El Ouardi2Abdelhadi Elbacha3Abdelouahed Tajer4Stéphane Espié5Université Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, 91110 Gif-sur-Yvette, FranceEngineering Systems and Applications (LISA), Cadi Ayyad University, Marrakech 40000, MoroccoUniversité Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, 91110 Gif-sur-Yvette, FranceEngineering Systems and Applications (LISA), Cadi Ayyad University, Marrakech 40000, MoroccoEngineering Systems and Applications (LISA), Cadi Ayyad University, Marrakech 40000, MoroccoUniversité Gustave Eiffel, SATIE, 91190 Gif-sur-Yvette, FranceElectric vehicles (EVs) are rising in the automotive industry, replacing combustion engines and increasing their global market presence. These vehicles offer zero emissions during operation and more straightforward maintenance. However, for such systems that rely heavily on battery capacity, precisely determining the battery’s state of charge (SOC) presents a significant challenge due to its essential role in lithium-ion batteries. This research introduces a dual-phase testing approach, considering factors like HVAC use and road topography, and evaluating machine learning models such as linear regression, support vector regression, random forest regression, and neural networks using datasets from real-world driving conditions in European (Germany) and African (Morocco) contexts. The results validate that the proposed neural networks model does not overfit when evaluated using the dual-phase test method compared to previous studies. The neural networks consistently show high predictive precision across different scenarios within the datasets, outperforming other models by achieving the lowest mean squared error (MSE) and the highest R<sup>2</sup> values.https://www.mdpi.com/2079-9268/14/4/59battery electric vehiclesstate of chargeonline estimationreal driving datamachine learningneural networks |
| spellingShingle | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data Journal of Low Power Electronics and Applications battery electric vehicles state of charge online estimation real driving data machine learning neural networks |
| title | A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data |
| title_full | A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data |
| title_fullStr | A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data |
| title_full_unstemmed | A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data |
| title_short | A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data |
| title_sort | comparative study of electric vehicles battery state of charge estimation based on machine learning and real driving data |
| topic | battery electric vehicles state of charge online estimation real driving data machine learning neural networks |
| url | https://www.mdpi.com/2079-9268/14/4/59 |
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