Driver-aware charging infrastructure design

Public charging infrastructure plays a crucial role in the context of electrifying the private mobility sector in particular for urban regions. Against this background, we develop a new mathematical model for the optimal placement of public charging stations for electric vehicles in cities. While existing approaches strongly aggregate traffic information or are only applicable to small instances, we formulate the problem as a specific combinatorial optimization problem that incorporates individual demand and temporal interactions of drivers, exact positioning of charging stations, as well as various charging speeds, and realistic charging curves. We show that the problem can be naturally cast as an integer program that, together with different reformulation techniques, can be efficiently solved for large instances. More specifically, we show that our approach can compute optimal placements of charging stations for instances based on traffic data for cities with up to $600\,000$ inhabitants and future electrification rates of up to $15\%$

A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs

Stochastic Programming Models For Electric Vehicles’ Operation: Network Design And Routing Strategies

Logistic and transportation (L&T) activities become a significant contributor to social and economic advances throughout the modern world Road L&T activities are responsible for a large percentage of CO2 emissions, with more than 24% of the total emission, which mostly caused by fossil fuel vehicles. Researchers, governments, and automotive companies put extensive effort to incorporate new solutions and innovations into the L&T system. As a result, Electric Vehicles (EVs) are introduced and universally accepted as one of the solutions to environmental issues. Subsequently, L&T companies are encouraged to adopt fleets of EVs. Integrating the EVs into the logistic and transportation systems introduces new challenges from strategic, planning, and operational perspectives. At the strategical level, one of the main challenges to be addressed to expand the EV charging infrastructures is the location of charging stations. Due to the longer charging time in EVs compared to the conventional vehicles, the parking locations can be considered as the candidate locations for installing charging stations. Another essential factor that should be considered in designing the Electric Vehicle Charging Station (EVCS) network is the size or capacity of charging stations. EV drivers\u27 arrival times in a community vary depending on various factors such as the purpose of the trip, time of the day, and day of the week. So, the capacity of stations and the number of chargers significantly affect the accessibility and utilization of charging stations. Also, the EVCSs can be equipped by distinct types of chargers, which are different in terms of installation cost, charging time, and charging price. City planners and EVCS owners can make low-risk and high-utilization investment decisions by considering EV users charging pattern and their willingness to pay for different charger types. At the operational level, managing a fleet of electric vehicles can offer several incentives to the L&T companies. EVs can be equipped with autonomous driving technologies to facilitate online decision making, on-board computation, and connectivity. Energy-efficient routing decisions for a fleet of autonomous electric vehicles (AEV) can significantly improve the asset utilization and vehicles’ battery life. However, employing AEVs also comes with new challenges. Two of the main operational challenges for AEVs in transport applications is their limited range and the availability of charging stations. Effective routing strategies for an AEV fleet require solving the vehicle routing problem (VRP) while considering additional constraints related to the limited range and number of charging stations. In this dissertation, we develop models and algorithms to address the challenges in integrating the EVs into the logistic and transportation systems

The role of operational research in green freight transportation

Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on ‘greening’ freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed

Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries

Carsharing is an alternative to private car usage. Using electric-vehicles as a substitute to fuel vehicles is a wiser option which leads to lower fuel emissions, more energy savings and decreased oil dependency. However, there are some barriers in using electric vehicles at large scale in carsharing companies. Battery power limitation and lack of sufficient infrastructures are some of them. Accurate demand forecasting is a must for this purpose. In the first part of this thesis, we investigate the demand forecasting problem for carsharing industries and apply four techniques namely simple linear regression, seasonally adjusted forecast, Winter's Model and artificial neural networks to decide the right number of vehicles to be made available at each station to meet the customer requests. The results on randomly generated test datasets show that artificial neural networks perform better over the other three. In the second part, we investigate the location planning problem of recharging stations for electric vehicles. The base model used for this study is the mathematical optimization model proposed by Wang & Lin (2013). Firstly, we improve their MIP model and solve it using AIMMS (Advanced Interactive Multidimensional Modeling System). Secondly, we propose Genetic Algorithm for the same problem and implement it in Matlab. The obtained results are compared with previous work done by Wang and Lin (2013). The comparisons show better performance of the proposed methods

Infrastructure Design for Electric and Autonomous Vehicles

This thesis focuses on infrastructure design for the disruptive transportation technologies of electric vehicles (EVs) and autonomous vehicles (AVs) to enable their adoption at large scale. Particularly, two EV-related problem frameworks concerning the spatial distribution of charging stations and their respective capacity levels are studied, and a new problem is introduced to determine the optimal deployment of AV lanes and staging facilities to enable shared autonomous transportation in urban areas. The first problem is centered around determining optimal locations of fast-charging stations to enable long-distance transportation with EVs. A new mathematical model is developed to address this problem. This model not only determines optimal facility locations but also finds optimal routes for every origin-destination (OD) trip which follows the path that leads to the minimum total en route recharging. Through computational experiments, this model is shown to outperform the widely used maximum and set cover problem settings in the literature in terms of several routing-related performance measures. A Benders decomposition algorithm is developed to solve large-scale instances of the problem. Within this algorithm, a novel subproblem solution methodology is developed to accelerate the performance of the classical Benders implementation. Computational experiments on real-world transportation networks demonstrate the value of this methodology as it turns out to speed the classical Benders up to 900 times and allows solving instances with up to 1397 nodes. The second problem extends the previous one by seeking to determine EV charging station locations and capacities under stochastic vehicle flows and charging times. It also considers the route choice behavior of EV users by means of a bilevel optimization model. This model incorporates a probabilistic service requirement on the waiting time to charge, and it is studied under a framework where charging stations operate as M/M/c queuing systems. A decomposition-based solution methodology, that uses a logic-based Benders algorithm for the location-only problem, is developed to solve the proposed bilevel model. This methodology is designed to be versatile enough to be tailored for the cooperative or uncooperative EV user behavior. Computational experiments are conducted on real-life highway networks to evaluate how service level requirements, deviation tolerance levels, and route choice behavior affect the location and sizing decisions of charging stations. The third problem entails the staging facility location and AV lane deployment problem for shared autonomous transportation. The proposed problem aims to find the optimal locations of staging facilities utilizing a bi-objective model that minimizes total travel distance and the total AV travel not occurring on AV lanes with respect to a given AV lane deployment budget and a number of staging facilities to locate. A Benders decomposition algorithm with Pareto-optimal cuts is developed and the trade-offs with optimal solutions on benchmark instances are evaluated. Computational experiments are performed to analyze the effects of AV lane budget, staging facility count, and the objective preferences of decision makers on optimal solutions

An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time

Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were found to be the most significant component in the objective function, with an 80\% decrease resulting in a 25\% cost reduction. VOT costs decrease substantially as energy costs increase. The number of fast chargers increases as VOT doubles. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible