Sustainable EV routing using spectral clustering and fuzzy reinforcement learning with energy constrained A* under mobility index and waiting constraints
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an advanced electric vehicle (EV) routing framework that significantly reduces travel time and improves energy efficiency by integrating spectral clustering, fuzzy reinforcement learning, and enhanced pathfinding. The novel system optimizes routes considering real-world factors like traffic, elevation, and charging station availability, reducing battery violations and range anxiety.
Area Of Science
- Computer Science, Artificial Intelligence, Transportation Engineering
Background
- Existing electric vehicle (EV) routing solutions often optimize metrics independently, failing to model dynamic interdependencies and adapt to real-time traffic and charging conditions.
- Current topological modeling lacks geographic scalability, and evaluations are often restricted to simplified environments, limiting the practical application of EV routing frameworks.
Purpose Of The Study
- To develop a comprehensive EV routing framework that addresses fragmented optimization, limited real-time adaptability, inadequate topological modeling, and constrained evaluation environments.
- To enhance EV navigation by integrating advanced algorithms for optimal route computation under real-world constraints including battery limitations, traffic dynamics, terrain elevation, and charging station delays.
Main Methods
- A topologically adaptive clustering mechanism using spectral clustering with geodesic distance metrics and elliptical regional modeling.
- A time-dependent arrival simulation model for accurate charging station occupancy prediction, incorporating temporal demand and station-specific dynamics.
- A fuzzy reinforcement learning-based charging station evaluator considering spatial density, occupancy trends, and temporal availability.
- An enhanced A* algorithm integrating elevation-aware energy profiling, real-time traffic sensitivity, and adaptive state-of-charge (SOC) constraint modeling.
Main Results
- Achieved a 22.8% reduction in total journey time, 19.6% improvement in energy efficiency, and a 63.3% decrease in charging station waiting time compared to traditional routing.
- Reduced battery violations by 90.0%, effectively mitigating range anxiety through improved SOC-aware planning.
- Demonstrated superior performance across diverse topographies (urban, rural, elevation-intensive) with up to 27.2% improvement over conventional algorithms.
Conclusions
- The proposed framework offers a unified and effective approach to EV routing, outperforming established algorithms and recent multi-objective solutions.
- The system's ability to handle real-world constraints and dynamic conditions advances EV navigation reliability and efficiency.
- Contributes to Sustainable Development Goals 7 and 11 by enabling energy-efficient, reliable EV navigation, supporting clean transportation and smart city integration.
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