Vehicles In Transportation Networks Research Articles

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.

Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networks

We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm.

Stochastic Scheduling of Mobile Energy Storage in Coupled Distribution and Transportation Networks for Conversion Capacity Enhancement

Mobile energy storage systems (MESSs) is a promising solution to enhancing the operational flexibility of coupled distribution and transportation networks (CDTNs), as well as the conversion capacities of hybrid AC/DC microgrids (MGs). To achieve the coordination among MESSs, hybrid AC/DC microgrids and CDTNs, while considering the system uncertainties from variable renewable energy (VRE) sources and the daily traffic demands of common vehicles in transportation networks, a two-stage stochastic management scheme is proposed to minimize the expected system operational cost. In the first-stage, the vehicle scheduling problem of MESSs is modeled and optimized based on the proposed user-equilibrium-based multi-layer multi-timescale time-space network model to fully utilize the mobility of MESSs under traffic uncertainties, i.e., MESSs are scheduled to travel within CDTNs for energy transfer or local energy scheduling. In the second-stage, the actual charging/discharging behaviors of MESSs, outputs of VRE sources and the power conversions at each MG are adjusted in accordance with uncertainty realizations. The proposed model is verified on a test system with Sioux Falls transportation network and a 33-bus distribution system. The results demonstrate the effectiveness of MESSs mobility to serve as potential power conversion enhancements for MGs with mismatched generation and conversion capabilities.

Traffic Equilibrium for Mixed Traffic Flows of Human-Driven Vehicles and Connected and Autonomous Vehicles in Transportation Networks under Tradable Credit Scheme

In this paper, the traffic equilibriums for mixed traffic flows of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV) under a tradable credit scheme (TCS) are established and formulated as two variational inequality (VI) problems with exogenous and endogenous CAV penetration rate, respectively. A modified Lagrangian dual (MLD) method embedded with a revised Smith’s route-swapping (RSRS) algorithm is proposed to solve the problems. Based on the numerical analysis, the impacts of CAV penetration and the extra expense of using a CAV on network performance are investigated. A novel driveway management, autonomous vehicle/credit charge (AVCC) link, is put forward to improve the efficiency of TCS. Under the TCS with exogenous CAV penetration rate, a logit-based model is applied to describe the stochastic user equilibrium for mixed traffic flow. It is found that the penetration of CAV gives rise to a better network performance and it can be further improved by the deployment of AVCC link. Under the TCS with endogenous penetration rate, a nested-logit model is applied to describe travelers’ choices of vehicle types and routes. It is found that the deployment of AVCC links can slow down the decline rate of CAV penetration with increasing expense and thus ensure a lower average travel time for CAVs. In both cases, the deployment of AVCC links can stimulate credit trading and drop down its unit price.

Optimal Routing of Energy-Aware Vehicles in Transportation Networks With Inhomogeneous Charging Nodes

We study the problem of routing for energy-aware battery-powered vehicles (BPVs) in networks with charging nodes. The objective is to minimize the total elapsed time, including travel and recharging time at charging stations, so that the vehicle reaches its destination without running out of energy. Relaxing the homogeneity of charging stations, and here, we investigate the routing problem for BPVs through a network of “inhomogeneous” charging nodes. We study two versions of the problem: the single-vehicle (user-centric) routing problem and the multiple-vehicle (system-centric) routing problem. For the former, we formulate a mixed-integer nonlinear programming (NLP)problem for obtaining an optimal path and charging policy simultaneously. We then reduce its computational complexity by decomposing it into two linear programming problems. For the latter, we use a similar approach by grouping vehicles into “subflows” and formulating the problem at a subflow-level with the inclusion of traffic congestion effects. We also propose an alternative NLP formulation obtaining near-optimal solutions with orders of magnitude reduction in the computation time. We have applied our optimal routing approach to a subnetwork of the eastern Massachusetts transportation network using actual traffic data provided by the Boston Region Metropolitan Planning Organization. Using these data, we estimate cost (congestion) functions and investigate the optimal solutions obtained under different charging station and energy-aware vehicle loads.

Optimal deployment of charging lanes for electric vehicles in transportation networks

Given the rapid development of charging-while-driving technology, we envision that charging lanes for electric vehicles can be deployed in regional or even urban road networks in the future and thus attempt to optimize their deployment in this paper. We first develop a new user equilibrium model to describe the equilibrium flow distribution across a road network where charging lanes are deployed. Drivers of electric vehicles, when traveling between their origins and destinations, are assumed to select routes and decide battery recharging plans to minimize their trip times while ensuring to complete their trips without running out of charge. The battery recharging plan will dictate which charging lane to use, how long to charge and at what speed to operate an electric vehicle. The speed will affect the amount of energy recharged as well as travel time. With the established user equilibrium conditions, we further formulate the deployment of charging lanes as a mathematical program with complementarity constraints. Both the network equilibrium and design models are solved by effective solution algorithms and demonstrated with numerical examples.

Package routing in transportation networks with fixed vehicle schedules

We consider a special case of the general problem involving the routing of packages among vehicles in transportation networks. In this special case, the schedules of the vehicles are fixed and packages are routed by transferring them between vehicles as these vehicles make stops according to their fixed schedules. Since this problem is NP-complete, we explore approximation algorithms for its solution. In particular, we cast this problem as a multicommodity flow problem with a mixed integer/linear program formulation. We then apply combinatorial optimization techniques based on solving the relaxed linear programming formulation of the problem to obtain a solution with provable constraint violation bounds and expected performance guarantees, where performance is measured in terms of the sum of the time in transit over all packages. We investigate the sensitivity of the performance guarantees to certain scaling factors and other limitations of this technique.