State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture
To enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range predic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/13/2197 |
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| author | Min Wei Yuhang Liu Haojie Wang Siquan Yuan Jie Hu |
| author_facet | Min Wei Yuhang Liu Haojie Wang Siquan Yuan Jie Hu |
| author_sort | Min Wei |
| collection | DOAJ |
| description | To enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range prediction, improving drivers’ travel planning and vehicle efficiency. A PCA-GA-K-Means-based driving cycle clustering method is introduced, followed by driving style feature extraction using a GMM to capture behavioral differences. A coupled library of twelve typical driving cycle style combinations is constructed to handle complex correlations among driving style, operating conditions, and range. To mitigate multicollinearity and nonlinear feature redundancies, a Pearson-DII-based feature extraction method is proposed. A stacking ensemble model, integrating Random Forest, CatBoost, XGBoost, and SVR as base models with ElasticNet as the meta model, is developed for robust prediction. Validated with real-world vehicle data across −21 °C to 39 °C and four driving cycles, the model significantly improves SOC prediction accuracy, offering a reliable solution for EV range estimation and enhancing user trust in EV technology. |
| format | Article |
| id | doaj-art-692a74bdaa09400e896a29e5ea935670 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-692a74bdaa09400e896a29e5ea9356702025-08-20T02:36:30ZengMDPI AGMathematics2227-73902025-07-011313219710.3390/math13132197State of Charge Prediction for Electric Vehicles Based on Integrated Model ArchitectureMin Wei0Yuhang Liu1Haojie Wang2Siquan Yuan3Jie Hu4School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaTo enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range prediction, improving drivers’ travel planning and vehicle efficiency. A PCA-GA-K-Means-based driving cycle clustering method is introduced, followed by driving style feature extraction using a GMM to capture behavioral differences. A coupled library of twelve typical driving cycle style combinations is constructed to handle complex correlations among driving style, operating conditions, and range. To mitigate multicollinearity and nonlinear feature redundancies, a Pearson-DII-based feature extraction method is proposed. A stacking ensemble model, integrating Random Forest, CatBoost, XGBoost, and SVR as base models with ElasticNet as the meta model, is developed for robust prediction. Validated with real-world vehicle data across −21 °C to 39 °C and four driving cycles, the model significantly improves SOC prediction accuracy, offering a reliable solution for EV range estimation and enhancing user trust in EV technology.https://www.mdpi.com/2227-7390/13/13/2197pure electric vehiclebattery state estimationdata-driven approachremaining driving range predictionstacking model |
| spellingShingle | Min Wei Yuhang Liu Haojie Wang Siquan Yuan Jie Hu State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture Mathematics pure electric vehicle battery state estimation data-driven approach remaining driving range prediction stacking model |
| title | State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture |
| title_full | State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture |
| title_fullStr | State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture |
| title_full_unstemmed | State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture |
| title_short | State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture |
| title_sort | state of charge prediction for electric vehicles based on integrated model architecture |
| topic | pure electric vehicle battery state estimation data-driven approach remaining driving range prediction stacking model |
| url | https://www.mdpi.com/2227-7390/13/13/2197 |
| work_keys_str_mv | AT minwei stateofchargepredictionforelectricvehiclesbasedonintegratedmodelarchitecture AT yuhangliu stateofchargepredictionforelectricvehiclesbasedonintegratedmodelarchitecture AT haojiewang stateofchargepredictionforelectricvehiclesbasedonintegratedmodelarchitecture AT siquanyuan stateofchargepredictionforelectricvehiclesbasedonintegratedmodelarchitecture AT jiehu stateofchargepredictionforelectricvehiclesbasedonintegratedmodelarchitecture |