Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus
The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data prep...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/12/329 |
| _version_ | 1827573281022017536 |
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| author | Xinxin Zhao Ming Zhang Guangyu Xue |
| author_facet | Xinxin Zhao Ming Zhang Guangyu Xue |
| author_sort | Xinxin Zhao |
| collection | DOAJ |
| description | The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles. |
| first_indexed | 2024-03-08T20:15:59Z |
| format | Article |
| id | doaj.art-64cfbb17c9af4c60ab49f6535c8f7f73 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-08T20:15:59Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-64cfbb17c9af4c60ab49f6535c8f7f732023-12-22T14:50:05ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-11-01141232910.3390/wevj14120329Data-Driven Algorithm Based on Energy Consumption Estimation for Electric BusXinxin Zhao0Ming Zhang1Guangyu Xue2Department of Vehicle Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Vehicle Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Vehicle Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles.https://www.mdpi.com/2032-6653/14/12/329SOClong short-term memorydata mininghyperparameter tuning |
| spellingShingle | Xinxin Zhao Ming Zhang Guangyu Xue Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus World Electric Vehicle Journal SOC long short-term memory data mining hyperparameter tuning |
| title | Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus |
| title_full | Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus |
| title_fullStr | Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus |
| title_full_unstemmed | Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus |
| title_short | Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus |
| title_sort | data driven algorithm based on energy consumption estimation for electric bus |
| topic | SOC long short-term memory data mining hyperparameter tuning |
| url | https://www.mdpi.com/2032-6653/14/12/329 |
| work_keys_str_mv | AT xinxinzhao datadrivenalgorithmbasedonenergyconsumptionestimationforelectricbus AT mingzhang datadrivenalgorithmbasedonenergyconsumptionestimationforelectricbus AT guangyuxue datadrivenalgorithmbasedonenergyconsumptionestimationforelectricbus |