Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
Route planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-...
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MDPI AG
2025-02-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/3/97 |
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| author | Tal Gaon Yovel Gabay Miri Weiss Cohen |
| author_facet | Tal Gaon Yovel Gabay Miri Weiss Cohen |
| author_sort | Tal Gaon |
| collection | DOAJ |
| description | Route planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-world applicable routing solutions. This framework incorporates practical challenges, such as charging station queue lengths, which significantly influence travel time and energy consumption. Using K-means clustering, the methodology groups charging stations based on geographical proximity, allowing for optimal stop selection and minimizing unnecessary detours. GA optimization is used to refine these routes by evaluating key factors, including travel distance, queue dynamics, and time, to determine paths with the fewest charging stops while maintaining efficiency. By integrating these two techniques, the proposed framework achieves a balance between computational simplicity and adaptability to changing conditions. A series of experiments have demonstrated the framework’s ability to identify the shortest and least congested routes with strategically placed charging stops. The dynamic nature of the model ensures adaptability to evolving real-world scenarios, such as fluctuating queue lengths and travel demands. This research demonstrates the effectiveness of this approach for identifying the shortest, least congested routes with the most optimal charging stations, resulting in significant advancements in sustainable transportation and EV route optimization. |
| format | Article |
| id | doaj-art-012f1938c9aa40f4ad9b24fd12b4413c |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-012f1938c9aa40f4ad9b24fd12b4413c2025-08-20T03:43:31ZengMDPI AGFuture Internet1999-59032025-02-011739710.3390/fi17030097Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic AlgorithmsTal Gaon0Yovel Gabay1Miri Weiss Cohen2Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, IsraelDepartment of Software Engineering, Braude College of Engineering, Karmiel 2161002, IsraelDepartment of Software Engineering, Braude College of Engineering, Karmiel 2161002, IsraelRoute planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-world applicable routing solutions. This framework incorporates practical challenges, such as charging station queue lengths, which significantly influence travel time and energy consumption. Using K-means clustering, the methodology groups charging stations based on geographical proximity, allowing for optimal stop selection and minimizing unnecessary detours. GA optimization is used to refine these routes by evaluating key factors, including travel distance, queue dynamics, and time, to determine paths with the fewest charging stops while maintaining efficiency. By integrating these two techniques, the proposed framework achieves a balance between computational simplicity and adaptability to changing conditions. A series of experiments have demonstrated the framework’s ability to identify the shortest and least congested routes with strategically placed charging stops. The dynamic nature of the model ensures adaptability to evolving real-world scenarios, such as fluctuating queue lengths and travel demands. This research demonstrates the effectiveness of this approach for identifying the shortest, least congested routes with the most optimal charging stations, resulting in significant advancements in sustainable transportation and EV route optimization.https://www.mdpi.com/1999-5903/17/3/97electric vehicle routingdynamic recharging queueGenetic AlgorithmK-means |
| spellingShingle | Tal Gaon Yovel Gabay Miri Weiss Cohen Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms Future Internet electric vehicle routing dynamic recharging queue Genetic Algorithm K-means |
| title | Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms |
| title_full | Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms |
| title_fullStr | Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms |
| title_full_unstemmed | Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms |
| title_short | Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms |
| title_sort | optimizing electric vehicle routing efficiency using k means clustering and genetic algorithms |
| topic | electric vehicle routing dynamic recharging queue Genetic Algorithm K-means |
| url | https://www.mdpi.com/1999-5903/17/3/97 |
| work_keys_str_mv | AT talgaon optimizingelectricvehicleroutingefficiencyusingkmeansclusteringandgeneticalgorithms AT yovelgabay optimizingelectricvehicleroutingefficiencyusingkmeansclusteringandgeneticalgorithms AT miriweisscohen optimizingelectricvehicleroutingefficiencyusingkmeansclusteringandgeneticalgorithms |