• DOI:10.1109/TITS.2020.2979363
• Corpus ID: 211082826

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

@article{Fazeli2020TwoStageSC,
  title={Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities},
  author={Seyed Sajjad Fazeli and Saravanan Venkatachalam and Ratna Babu Chinnam and Alper Ekrem Murat},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2020},
  volume={22},
  pages={3038-3053}
}

• S. S. Fazeli, Saravanan Venkatachalam, +1 author A. Murat
• Published 11 February 2020
• Computer Science
• IEEE Transactions on Intelligent Transportation Systems

Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers’ behaviors. Some of the key random data parameters considered in this study are: EV’s dwell time at parking location, battery’s state of charge, distance from home, willingness to walk, drivers’ arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.