The Effects of the “White Phase” on Intersection Performance with Mixed-Autonomy Traffic Stream

Niroumand, Ramin; Tajalli, Mehrdad; Hajibabai, Leila; Hajbabaie, Ali

doi:10.1109/itsc45102.2020.9294741

Cited by 16 publications

(12 citation statements)

References 37 publications

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“…Furthermore, the impact of various EV penetration rates can be incorporated in the study to obtain more realistic resolutions for the existing traffic conditions. The impact of charging decisions on traffic system dynamics, also considering automated traffic streams [56], [57], [58], [59], [60], [61], as well as traffic control decisions [62], [63] on drivers' charging attempts are other interesting directions.…”

Section: Discussionmentioning

confidence: 99%

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Mirheli

Hajibabai

2023

IEEE Trans. Intell. Transport. Syst.

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This paper aims to investigate the electric vehicle (EV) charging network design and utilization management considering user-centric decisions. A hierarchical formulation is developed with the EV charging network design and demanddriven pricing scheme in the upper level and users' charging decisions to minimize their own travel costs and charging expenses in the lower level. The model aims to minimize the facility deployment cost and maximize the charging income of the network operator while minimizing the user-centric costs. We have converted the proposed bi-level formulation into an equivalent single-level model using the lower-level objective function as complementary equations. Then, we have developed an iterative active-set based solution technique to determine the strategic decisions on charging network design. To partially overcome the computational burden, the arc travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a system-level bi-level optimization benchmark. Our experiments show a CPU time of 2.3 hr for the proposed approach compared to 173.1 hr of the benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.

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Section: Discussionmentioning

confidence: 99%

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Mirheli

Hajibabai

2023

IEEE Trans. Intell. Transport. Syst.

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“…The distribution of a central optimization problem into EV user level optimizations may introduce infeasible solutions due to the relaxation of the user connection constraints. Given charging location and duration decision pair ( y z , n z ) is bounded, Proposition 2 shows that the solution obtained by GNE with an embedded consensus-based coordination scheme [e.g., [40], [44], [45], and [46], [47]] converges to the solution of (2a)-(2f) for sufficiently large z.…”

Section: B Monte Carlo Tree Searchmentioning

confidence: 99%

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Hajibabai

Mirheli

2022

IEEE Trans. Intell. Transport. Syst.

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Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

show abstract

“…The distribution of a central optimization problem into EV user level optimizations may introduce infeasible solutions due to the relaxation of the user connection constraints. Given charging location and duration decision pair (y z , n z ) is bounded, Proposition 2 shows that the solution obtained by GNE with an embedded consensus-based coordination scheme [e.g., 39,43,44,45] converges to the solution of (2a)-(2f) for sufficiently large z.…”

Section: B Monte Carlo Tree Searchmentioning

confidence: 99%

A Game-theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Hajibabai¹,

Mirheli²

2022

Preprint

Self Cite

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Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a realtime, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

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The Effects of the “White Phase” on Intersection Performance with Mixed-Autonomy Traffic Stream

Cited by 16 publications

References 37 publications

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

A Game-theoretic Approach for Dynamic Service Scheduling at Charging Facilities

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