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Area of Science:

  • Sustainable Urban Logistics
  • Electric Vehicle Technology
  • Energy Systems Engineering

Background:

  • Urban logistics face challenges in efficiency and sustainability.
  • Electric vehicles (EVs) offer a sustainable solution but require optimized charging infrastructure.
  • Integrating EVs into logistics necessitates managing charging, power exchange, and battery degradation.

Purpose of the Study:

  • To develop a novel framework for sustainable urban logistics and distribution systems.
  • To formulate an innovative electric vehicle path optimization model minimizing operational costs.
  • To integrate Vehicle-to-Grid (V2G) technology for enhanced resource utilization and grid load balancing.

Main Methods:

  • Developed an electric vehicle path optimization model.
  • Integrated V2G technology for slow charging and discharging management.
  • Employed a genetic algorithm to solve the V2G framework and logistics distribution model.
  • Developed an algorithm for estimating battery degradation costs considering temperature and discharge depth.

Main Results:

  • The V2G model significantly enhances operational flexibility in EV logistics distribution.
  • Substantial cost reductions were achieved through optimized EV operations.
  • Peak and off-peak grid loads were effectively balanced, improving grid resilience.
  • Sensitivity analysis revealed temperature and discharge depth significantly impact battery degradation.

Conclusions:

  • The V2G framework optimizes urban logistics, reducing costs and improving grid stability.
  • Strategic management of slow charging and discharging is crucial for distribution system performance.
  • Environmental factors like temperature and discharge depth critically influence EV battery lifespan, informing maintenance strategies.