2026· IEEE Open Journal of Intelligent Transportation Systems· Vol 7, pp. 1919-1942· 0 citations· 77 references
Abstract
Planning public electric vehicle charging stations (EVCSs) in emerging urban environments requires balancing operator profitability and user accessibility while accounting for traffic congestion and uncertainty in future demand and economic conditions. However, most existing EVCS planning studies focus primarily on identifying nominal optimal solutions and provide limited insight into the reliability of deployment decisions under uncertain conditions. To address this gap, this study develops a congestion-aware bi-objective framework for EVCS siting and sizing that simultaneously maximizes annualized operator profit and minimizes aggregate additional user cost, including travel, queueing, and energy-consumption components. The study integrates metaheuristics with congestion due to peak period travel, queueing-based service assessment, Monte Carlo uncertainty analysis, reliability evaluation, and elasticity-based sensitivity assessment within a unified decision-support methodology. The approach is demonstrated for the city of Guwahati, India represented by a 53-node, 76-link road network. Results highlight that robust EVCS deployment solutions consistently converge to a narrow infrastructure envelope comprising approximately 16–18 charging stations, 366–390 chargers, and a peak demand of 22–23 MW despite uncertainty in key techno-economic parameters. Reliability analysis shows that selected compromise solutions maintain profitability and affordability targets with high probability under stochastic perturbations, while sensitivity analysis identifies charging demand, retail tariffs, and fleet battery composition as the principal drivers of outcome variability. The proposed framework provides a reproducible and reliable methodology for congestion-aware EVCS planning under uncertainty.
As electric vehicle (EV) adoption grows, quantifying the scheduling burden and economic cost of long-distance travel under the existing charging infrastructure becomes increasingly important for infrastructure planning and policy. This paper presents a scalable, optimization-based framework for scheduling EV charging stops along real-world charging stations and simulated long-distance personal vehicle trajectories across the United States using POLARIS. Taking the existing charging network as fixed input, the framework minimizes total detour and queuing costs for each vehicle while respecting plug capacity constraints at each station. The methodology proceeds in three phases: (i) infeasibility pruning via a forward-pass reachability heuristic, (ii) per-vehicle optimal charging schedule computation via dynamic programming on a directed acyclic graph, and (iii) capacity-aware iterative congestion resolution through a penalty-based heuristic that augments detour costs at congested stations, with a first-in, first-out queue fallback. Applied to approximately 2.7M origin--destination vehicle trajectories derived from a 1\% sample of national personal travel demand within the POLARIS agent-based transportation simulation framework and covering 14,260 DC fast charging stations with 68,641 plugs from the Alternative Fuels Station Locator, the framework produces capacity-feasible schedules in under 1.3 hours on a 128-core high-performance computing cluster without requiring any commercial optimization solver. A three-tier economic analysis spanning operational costs, total cost of ownership, and amortized infrastructure investment is conducted to evaluate EV cost competitiveness relative to internal combustion engine vehicles across scenarios.
The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a copula-based stochastic planning framework for the optimal allocation of FCSs while accounting for the correlated uncertainties associated with EV charging behavior. A multivariate copula model is employed to capture the dependency structure among key charging variables and generate realistic stochastic charging scenarios, which are subsequently incorporated into the EV charging load forecasting process over the planning horizon. Based on the resulting stochastic charging demand, a multi-objective optimization model is developed to simultaneously minimize investment costs and EV users’ travel distances, improve distribution network performance, and maximize environmental benefits through decarbonization. In addition, distributed generation (DG) units are optimally integrated to improve voltage profiles and reduce power losses. The proposed framework is implemented using MATLAB R2013a and R.4.0.2 and evaluated using both the IEEE 33-bus test system and a realistic 37-bus coupled transportation–power network in Meshgin-Shahr, Iran. The results demonstrate the effectiveness of the proposed stochastic planning framework in addressing uncertainties in EV charging behavior and identifying robust FCS deployment strategies.
P. Farhadi, S. Moghaddas-Tafreshi, Amir Shahirinia· World Electric Vehicle Journ...· 0 citations
The rapid transition toward sustainable electrified transportation has led to the urgent deployment of electric vehicle (EV) fleets in urban logistics, where limited battery capacity and the need for en-route charging introduce new challenges for EV routing optimization. The final route, travel time, and operational cost are directly affected by the inclusion of charging stations in the routing objective. Unlike conventional vehicle routing problems (VRPs), the electric VRP (EVRP) requires energy feasibility constraints to be tightly integrated with routing decisions. In this study, we introduce a scalable, simulation-based EVRP framework that explicitly incorporates charging-station insertion into a metaheuristic optimization solver. A particle swarm optimization (PSO) approach is adopted to address the combinatorial complexity of large-scale instances, while feasibility is enforced through state-of-charge tracking and adaptive charging decisions. The proposed framework is evaluated and compared with a heuristic baseline algorithm (BR-TSP) under multiple simulation scenarios. Results demonstrate that the proposed approach is fully feasible across all tested scenarios, significantly outperforms classical exact solvers in scalability, and highlights the critical role of explicit charging integration in realistic EV fleet operations. The findings reveal practical strategies for energy-aware routing in large-scale electric mobility applications.
Khaled Bouhadef, José Almeida, Jo˜ao Soares et al.· Proceedings of the Genetic a...· 0 citations
Plug-in electric vehicles (PEVs) feature rapid proliferation as a result of their low carbon emissions, reduced maintenance requirements and cost efficient operation. Nonetheless, increased penetration from PEVs poses significant challenges to distribution networks in terms of higher power losses, voltage stability and harmonic distortion. Moreover, the strong peak charging demand at public charging stations (CSs) causes strong stress at the power grid. Consequently, the best spatial allocation of CSs is important, as it is directly linked to PEV user travel behavior and decisions on charging infrastructure investments. The current work would suggest a new methodology to obtain the best CS positions inside smart cities. The framework devised includes PEV travel times to CSs taking into account horizontal displacement between vehicles and stations and simultaneous integration of traffic conditions for both conventional vehicles and PEVs. Moreover, the queuing time inside Cs is considered in this framework. The CS placement problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved using a hybrid heuristic optimization technique of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Mohammad Aljaidi, Rami Almatarneh, Ghassan Samara· 2026 6th International Confe...· 0 citations
With growing emphasis on green and low-carbon development and rising urban delivery demand, electric vehicles (EVs) have been increasingly adopted in logistics distribution systems. However, their limited driving range, relatively long charging durations, and the limited capacity of charging stations pose substantial challenges to real-world electric delivery operations. When multiple vehicles arrive at a station with a limited number of chargers, queueing delays may disrupt subsequent customer service and increase total operating costs. To address this issue, this study investigates an electric vehicle routing problem with capacitated charging stations and queueing delays. A mixed-integer linear programming model is formulated, and an enhanced adaptive large neighborhood search (ALNS) algorithm is developed to efficiently solve medium- and large-scale instances. In the proposed model, each vehicle visit to a charging station is represented as a charging event, while finite station capacity is enforced through charging-event assignment and temporal non-overlap constraints. Computational results show that the enhanced ALNS matches the proven optimal solution for the 10-customer instance. For the 15- and 20-customer instances, the best objective values obtained by the enhanced ALNS were 0.39% and 4.84% lower than the corresponding time-limited Gurobi incumbents, respectively. For the 30-, 50-, and 100-customer instances, the enhanced ALNS consistently generates feasible solutions within the prescribed computational budget, whereas Gurobi does not obtain a feasible incumbent within substantially longer time limits. Compared with the baseline ALNS, the enhanced version generally achieves lower mean objective values and more favorable convergence behavior. Sensitivity analyses further show that increasing the number of chargers and improving the charging rate can reduce queueing delays and total charging duration. The proposed approach provides practical decision support for reliable and sustainable urban electric freight operations.
Urban waste transportation systems often experience inefficiencies due to uncertainty in daily waste generation, leading to vehicle overloads, increased operational costs, and environmental impacts. This study proposes a robust optimization model for the capacitated vehicle routing problem (RO-CVRP) to explicitly address demand uncertainty in municipal waste collection. A budgeted uncertainty parameter gamma is incorporated to control the level of protection against worst-case deviations. Initial routes are generated using the Clarke-Wright savings (CWS) algorithm and subsequently evaluated within a robust optimization framework. Computational experiments are conducted using real data from temporary disposal sites (TPS) in Tanah Enam Ratus Subdistrict Medan City, with a vehicle capacity of 10 m³. The results show that higher gamma values produce more conservative routing solutions, increasing the number of vehicles while reducing the risk of capacity violations. Price of robustness (PoR) analysis highlights the trade-off between transportation cost and reliability, confirming the model’s effectiveness for resilient waste logistics planning.
