Research on charging behavior of electric vehicles based on multiple objectives

Area of Science:

• * Electric Vehicle Charging Infrastructure
• * Smart Grid Management
• * Operations Research

Background:

• * Existing electric vehicle (EV) charging modes (random, tariff-guided, stop-and-charge) face challenges with imbalanced vehicle-to-pile ratios.
• * EV charging can cause significant load fluctuations on the power grid.
• * Addressing queuing is crucial for practical EV charging solutions.

Purpose of the Study:

• * To develop a multi-objective queuing charging strategy for EVs.
• * To integrate common charging modes while considering real-world vehicle-to-pile imbalances.
• * To optimize charging based on public interest metrics, aiming for low cost and high travel rates.

Main Methods:

• * Development of a sophisticated travel model for EVs.
• * Creation of a comprehensive charging model incorporating queuing dynamics.
• * Integration of random, tariff-guided, and stop-and-charge modes into a unified strategy.

Main Results:

• * The proposed strategy achieved the highest comprehensive evaluation index in experimental tests.
• * Demonstrated effectiveness in lowering charging costs and increasing travel rates for EV users.
• * Successfully smoothed the EV charging load curve, mitigating grid strain.

Conclusions:

• * The multi-objective queuing strategy offers a practical solution for EV charging challenges.
• * It enhances user convenience and reduces overall charging expenses.
• * The strategy contributes to grid stability by reducing charging load fluctuations, benefiting both consumers and the grid.