A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses
The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/4/911 |
| _version_ | 1797298340289839104 |
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| author | Zvonimir Dabčević Branimir Škugor Ivan Cvok Joško Deur |
| author_facet | Zvonimir Dabčević Branimir Škugor Ivan Cvok Joško Deur |
| author_sort | Zvonimir Dabčević |
| collection | DOAJ |
| description | The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R<sup>2</sup> value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. |
| first_indexed | 2024-03-07T22:33:32Z |
| format | Article |
| id | doaj.art-5b89a6690caf46dfb42ad6b5f3ac6a2a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:33:32Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5b89a6690caf46dfb42ad6b5f3ac6a2a2024-02-23T15:15:25ZengMDPI AGEnergies1996-10732024-02-0117491110.3390/en17040911A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City BusesZvonimir Dabčević0Branimir Škugor1Ivan Cvok2Joško Deur3Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, CroatiaFaculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, CroatiaFaculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, CroatiaFaculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, CroatiaThe paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R<sup>2</sup> value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model.https://www.mdpi.com/1996-1073/17/4/911city busesbattery electric vehiclesdata-driven modelingbattery energy consumptionpredictionfeature selection |
| spellingShingle | Zvonimir Dabčević Branimir Škugor Ivan Cvok Joško Deur A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses Energies city buses battery electric vehicles data-driven modeling battery energy consumption prediction feature selection |
| title | A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses |
| title_full | A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses |
| title_fullStr | A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses |
| title_full_unstemmed | A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses |
| title_short | A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses |
| title_sort | trip based data driven model for predicting battery energy consumption of electric city buses |
| topic | city buses battery electric vehicles data-driven modeling battery energy consumption prediction feature selection |
| url | https://www.mdpi.com/1996-1073/17/4/911 |
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