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Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization.
Aiping Tan1, Chang Wang1, Yan Wang1
1School of Cyber Science and Engineering, Liaoning University, Shenyang 110036, China.
Sensors (Basel, Switzerland)
|January 11, 2025
Summary
This study introduces electric vehicle charging route planning (EVCRP-UR) to address range anxiety by personalizing routes and charging. An improved ant colony optimization (IACO) algorithm optimizes paths and charging times for efficient electric vehicle travel.
Area of Science:
- Transportation Engineering
- Computer Science
- Sustainable Energy
Background:
- Electric vehicles (EVs) offer environmental benefits but face adoption barriers due to limited range and slow charging.
- Range anxiety remains a significant challenge hindering widespread EV adoption.
- Current EV route optimization lacks personalization for individual user needs and constraints.
Purpose of the Study:
- To propose a novel electric vehicle charging route planning based on user requirements (EVCRP-UR) problem.
- To develop an integrated approach for personalized EV routing and charging optimization.
- To enhance the efficiency and user-centricity of EV navigation systems.
Main Methods:
- Utilized topology optimization to reduce computational complexity in path planning.
- Introduced an improved ant colony optimization (IACO) algorithm with novel heuristics and probability models.
- Developed a discrete electricity dynamic programming (DE-DP) algorithm for optimal charging time determination.
Main Results:
- The proposed IACO algorithm effectively integrates user preferences and multiple constraints for personalized EV routing.
- Topology optimization significantly improved path planning efficiency.
- The combined IACO and DE-DP approach demonstrated superior performance in EV routing and charging optimization.
Conclusions:
- The EVCRP-UR framework successfully addresses range anxiety by offering personalized EV route and charging solutions.
- The IACO algorithm provides an efficient and effective method for optimizing electric vehicle travel.
- This research contributes to overcoming adoption barriers for electric vehicles through advanced planning strategies.
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