Targeted optimal-path problem for electric vehicles with connected charging stations.

Area of Science:

• Transportation Engineering
• Operations Research
• Computer Science

Background:

• Electric vehicles (EVs) face range limitations and

Purpose of the Study:

• To develop a realistic path planning model for electric vehicles (EVs) that addresses limited battery levels and minimizes total travel cost.
• Introduce the electric vehicle targeted optimal-path (EV-TOP) problem, considering charging station waiting times and accurate energy consumption.
• Provide a polynomial-time approximation algorithm for efficient EV route optimization.

Main Methods:

• Decomposition of the EV-TOP into two subproblems: EV1-COP (constrained optimal path) and EV2-SP (shortest path).
• Utilized algorithms: Lagrangian relaxation, Simple Efficient Approximation (SEA), Martins (MS) deletion for EV1-COP, and Dijkstra's algorithm for EV2-SP.
• Incorporated charging station information and real-time traffic data for cost calculation.

Main Results:

• Developed a polynomial-time approximation algorithm for the EV-TOP.
• Demonstrated the performance of the travel cost method in computational studies.
• Validated the EV-TOP model by comparing its performance against a well-known existing method.

Conclusions:

• The proposed EV-TOP model provides a more realistic and efficient approach to electric vehicle path planning.
• The method effectively minimizes travel costs by considering crucial factors like charging wait times.
• The developed algorithm offers a practical solution for mitigating range anxiety in electric vehicles.