Driving range estimation for electric bus based on atomic orbital search and back propagation neural network
Abstract As urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiet...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Intelligent Transport Systems |
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| Online Access: | https://doi.org/10.1049/itr2.12592 |
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| author | Hanchen Ke Jun Bi Yongxing Wang Yu Zhang |
| author_facet | Hanchen Ke Jun Bi Yongxing Wang Yu Zhang |
| author_sort | Hanchen Ke |
| collection | DOAJ |
| description | Abstract As urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiety is one of the main factors limiting its popularization. To solve this problem, an accurate estimation method for the driving range of electric buses based on atomic orbital search (AOS) algorithm and back propagation neural network (BPNN) was used, in which a long‐term bus operation dataset under different driving conditions is utilized to train BPNN, and then weight and bias are taken as the first generation provided for AOS approach to find a more appropriate parameter combination. Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet. |
| format | Article |
| id | doaj-art-ee61bb8bbf8c43268ec19495c15ce2ea |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-ee61bb8bbf8c43268ec19495c15ce2ea2025-08-20T01:58:12ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-12-0118S12884289510.1049/itr2.12592Driving range estimation for electric bus based on atomic orbital search and back propagation neural networkHanchen Ke0Jun Bi1Yongxing Wang2Yu Zhang3School of Traffic and Transportation Beijing Jiaotong University Beijing P. R. ChinaSchool of Traffic and Transportation Beijing Jiaotong University Beijing P. R. ChinaSchool of Traffic and Transportation Beijing Jiaotong University Beijing P. R. ChinaSchool of Traffic and Transportation Beijing Jiaotong University Beijing P. R. ChinaAbstract As urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiety is one of the main factors limiting its popularization. To solve this problem, an accurate estimation method for the driving range of electric buses based on atomic orbital search (AOS) algorithm and back propagation neural network (BPNN) was used, in which a long‐term bus operation dataset under different driving conditions is utilized to train BPNN, and then weight and bias are taken as the first generation provided for AOS approach to find a more appropriate parameter combination. Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet.https://doi.org/10.1049/itr2.12592electric vehiclesenergy consumptionenergy management systems |
| spellingShingle | Hanchen Ke Jun Bi Yongxing Wang Yu Zhang Driving range estimation for electric bus based on atomic orbital search and back propagation neural network IET Intelligent Transport Systems electric vehicles energy consumption energy management systems |
| title | Driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| title_full | Driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| title_fullStr | Driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| title_full_unstemmed | Driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| title_short | Driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| title_sort | driving range estimation for electric bus based on atomic orbital search and back propagation neural network |
| topic | electric vehicles energy consumption energy management systems |
| url | https://doi.org/10.1049/itr2.12592 |
| work_keys_str_mv | AT hanchenke drivingrangeestimationforelectricbusbasedonatomicorbitalsearchandbackpropagationneuralnetwork AT junbi drivingrangeestimationforelectricbusbasedonatomicorbitalsearchandbackpropagationneuralnetwork AT yongxingwang drivingrangeestimationforelectricbusbasedonatomicorbitalsearchandbackpropagationneuralnetwork AT yuzhang drivingrangeestimationforelectricbusbasedonatomicorbitalsearchandbackpropagationneuralnetwork |