Novel control strategies for electric vehicle charging stations using stochastic modeling and queueing analysis

Area of Science:

• Operations Research
• Transportation Engineering
• Queueing Theory

Background:

• Electric vehicle (EV) charging infrastructure deployment faces challenges due to unpredictable user behavior and queueing dynamics.
• Traditional models often neglect critical customer behaviors such as impatience and balking, leading to suboptimal network design.

Purpose of the Study:

• To develop a comprehensive analytical framework for modeling EV charging infrastructures.
• To incorporate realistic customer behavioral dynamics into queueing models for improved accuracy.
• To provide a decision-support tool for optimizing EV charging network capacity and management.

Main Methods:

• Stochastic queueing-theoretic approach with a focus on customer behavioral dynamics.
• Formulation of system dynamics using a continuous-time Markov chain (CTMC).
• Matrix-analytic solution techniques to obtain steady-state probabilities and performance metrics.

Main Results:

• The model successfully incorporates customer impatience, balking, feedback, and state-dependent service thresholds.
• Key performance metrics like system occupancy, server utilization, abandonment rates, and throughput were computed.
• Numerical simulations validated the model and revealed interdependencies between customer tolerance, service quality, and operational performance.

Conclusions:

• The developed framework offers valuable insights for capacity planning, congestion control, and service optimization in EV charging networks.
• The study provides a rigorous decision-support system for managing EV charging infrastructure under dynamic user behavior.
• Findings highlight the importance of considering customer behavior for enhancing the efficiency and adaptability of smart charging infrastructures.