The Energy-Efficient Dynamic Route Planning for Electric Vehicles
Aiming to provide an approach for finding energy-efficient routes in dynamic and stochastic transportation networks for electric vehicles, this paper addresses the route planning problem in dynamic transportation network where the link travel times are assumed to be random variables to minimize tota...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/2607402 |
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| _version_ | 1832550202049822720 |
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| author | Wenjuan Zhou Li Wang |
| author_facet | Wenjuan Zhou Li Wang |
| author_sort | Wenjuan Zhou |
| collection | DOAJ |
| description | Aiming to provide an approach for finding energy-efficient routes in dynamic and stochastic transportation networks for electric vehicles, this paper addresses the route planning problem in dynamic transportation network where the link travel times are assumed to be random variables to minimize total energy consumption and travel time. The changeable signals are introduced to establish state-space-time network to describe the realistic dynamic traffic network and also used to adjust the travel time according to the signal information (signal cycle, green time, and red time). By adjusting the travel time, the electric vehicle can achieve a nonstop driving mode during the traveling. Further, the nonstop driving mode could avoid frequent acceleration and deceleration at the signal intersections so as to reduce the energy consumption. Therefore, the dynamically adjusted travel time can save the energy and eliminate the waiting time. A multiobjective 0-1 integer programming model is formulated to find the optimal routes. Two methods are presented to transform the multiobjective optimization problem into a single objective problem. To verify the validity of the model, a specific simulation is conducted on a test network. The results indicate that the shortest travel time and the energy consumption of the planning route can be significantly reduced, demonstrating the effectiveness of the proposed approaches. |
| format | Article |
| id | doaj-art-34222f1fbdc8416a98de42398425dac2 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-34222f1fbdc8416a98de42398425dac22025-02-03T06:07:21ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/26074022607402The Energy-Efficient Dynamic Route Planning for Electric VehiclesWenjuan Zhou0Li Wang1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaAiming to provide an approach for finding energy-efficient routes in dynamic and stochastic transportation networks for electric vehicles, this paper addresses the route planning problem in dynamic transportation network where the link travel times are assumed to be random variables to minimize total energy consumption and travel time. The changeable signals are introduced to establish state-space-time network to describe the realistic dynamic traffic network and also used to adjust the travel time according to the signal information (signal cycle, green time, and red time). By adjusting the travel time, the electric vehicle can achieve a nonstop driving mode during the traveling. Further, the nonstop driving mode could avoid frequent acceleration and deceleration at the signal intersections so as to reduce the energy consumption. Therefore, the dynamically adjusted travel time can save the energy and eliminate the waiting time. A multiobjective 0-1 integer programming model is formulated to find the optimal routes. Two methods are presented to transform the multiobjective optimization problem into a single objective problem. To verify the validity of the model, a specific simulation is conducted on a test network. The results indicate that the shortest travel time and the energy consumption of the planning route can be significantly reduced, demonstrating the effectiveness of the proposed approaches.http://dx.doi.org/10.1155/2019/2607402 |
| spellingShingle | Wenjuan Zhou Li Wang The Energy-Efficient Dynamic Route Planning for Electric Vehicles Journal of Advanced Transportation |
| title | The Energy-Efficient Dynamic Route Planning for Electric Vehicles |
| title_full | The Energy-Efficient Dynamic Route Planning for Electric Vehicles |
| title_fullStr | The Energy-Efficient Dynamic Route Planning for Electric Vehicles |
| title_full_unstemmed | The Energy-Efficient Dynamic Route Planning for Electric Vehicles |
| title_short | The Energy-Efficient Dynamic Route Planning for Electric Vehicles |
| title_sort | energy efficient dynamic route planning for electric vehicles |
| url | http://dx.doi.org/10.1155/2019/2607402 |
| work_keys_str_mv | AT wenjuanzhou theenergyefficientdynamicrouteplanningforelectricvehicles AT liwang theenergyefficientdynamicrouteplanningforelectricvehicles AT wenjuanzhou energyefficientdynamicrouteplanningforelectricvehicles AT liwang energyefficientdynamicrouteplanningforelectricvehicles |