Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization
Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasin...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/176 |
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| author | Aiping Tan Chang Wang Yan Wang Chenglong Dong |
| author_facet | Aiping Tan Chang Wang Yan Wang Chenglong Dong |
| author_sort | Aiping Tan |
| collection | DOAJ |
| description | Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization. |
| format | Article |
| id | doaj-art-5f5dd79a22024ca7956e137486905bc2 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5f5dd79a22024ca7956e137486905bc22025-01-10T13:21:08ZengMDPI AGSensors1424-82202024-12-0125117610.3390/s25010176Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony OptimizationAiping Tan0Chang Wang1Yan Wang2Chenglong Dong3School of Cyber Science and Engineering, Liaoning University, Shenyang 110036, ChinaSchool of Cyber Science and Engineering, Liaoning University, Shenyang 110036, ChinaSchool of Cyber Science and Engineering, Liaoning University, Shenyang 110036, ChinaSchool of Cyber Science and Engineering, Liaoning University, Shenyang 110036, ChinaElectric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization.https://www.mdpi.com/1424-8220/25/1/176electric vehiclesroute planningant colony optimization |
| spellingShingle | Aiping Tan Chang Wang Yan Wang Chenglong Dong Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization Sensors electric vehicles route planning ant colony optimization |
| title | Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization |
| title_full | Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization |
| title_fullStr | Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization |
| title_full_unstemmed | Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization |
| title_short | Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization |
| title_sort | electric vehicle charging route planning for shortest travel time based on improved ant colony optimization |
| topic | electric vehicles route planning ant colony optimization |
| url | https://www.mdpi.com/1424-8220/25/1/176 |
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