Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

A multi-scale framework for fuel station location: from highways to street intersections

Electric drive vehicles (plug-in electric vehicle or hydrogen fuel cell vehicles) have been promoted by governments to foster a more sustainable transportation future. Wider adoption of these vehicles, however, depends on the availability of a convenient and reliable refueling/recharging infrastructure. This paper introduces a path-based, multi-scale, scenario-planning modeling framework for locating a system of alternative-fuel stations. The approach builds on (1) the Flow Refueling Location Model (FRLM), which assumes that drivers stop along their origin-destination routes to refuel, and checks explicitly whether round trips can be completed without running out of fuel, and (2) the Freeway Traffic Capture Method (FTCM), which assesses the degree to which drivers can conveniently reach sites on the local street network near freeway intersections. This paper extends the FTCM to handle cases involving clusters of nearby freeway intersections, which is a limitation of its previous specification. Then, the cluster-based FTCM (CFTCM) is integrated with the FRLM and the DFRLM (FRLM with Deviations) to better conduct detailed geographic optimization of this multi-scale location planning problem. The main contribution of this research is the introduction of a framework that combines multi-scale planning methods to more effectively inform the early development stage of hydrogen refueling infrastructure planning. The proposed multi-scale modeling framework is applied to the Hartford, Connecticut region, which is one of the next areas targeted for fuel-cell vehicle (FCV) market and infrastructure expansion in the United States. This method is generalizable to other regions or other types of fast-fueling alternative fuel vehicles

Alternative-fuel station network design under impact of station failures

In this paper, we have formulated a mixed-integer non-linear programming model for alternative-fuel station location problem in which each station can fail with a site-specific probability. The model aims to maximise the total expected traffic volume that can be refuelled by the unreliable alternative-fuel stations. Based on the linearisation techniques, i.e., probability chains and piecewise-linear functions, we linearise the non-linearity of compound probability terms in the non-linear model to solve this problem efficiently. An efficient Tabu search algorithm is also developed to solve the large-size instances. In addition, we extend the model to deal with reliable multi-period alternative-fuel station network design. Computational experiments, carried out on the well-known benchmark instances where the probability of station failures is uniformly generated, show that the proposed models and algorithm can obtain the optimal solutions within a reasonable computation time. Compared to a standard station location model that disregards the potential for station failures, our model designs more reliable alternative-fuel station network under risk of station failures. A sensitivity analysis of failure probabilities in the station network design is investigated to demonstrate the robustness of our model and study how variability in the probability of station failure affects solution robustness

15-08 Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: \u3c/i\u3e Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and use of Public Transportation\u3c/i\u3e

A major challenge for achieving large-scale adoption of EVs is an accessible infrastructure for the communities. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations due to mutual dependence of EV sales and public infrastructure deployment. Such infrastructure deployment also presents a number of unique opportunities for promoting livability while helping to reduce the negative side-effects of transportation (e.g., congestion, emissions, and noise pollution). In this phase, we develop a modeling framework (MF) to consider various factors and their associated uncertainties for an optimal network design for electrified vehicles. The factors considered in the study include: state of charge, dwell time, Origin-Destination (OD) pair

An efficient heuristic algorithm for the alternative-fuel station location problem

We have developed an efficient heuristic algorithm for location of alternative-fuel stations. The algorithm is constructed based on solving the sequence of subproblems restricted on a set of promising station candidates, and fixing a number of the best promising station locations. The set of candidates is initially determined by solving a relaxation model, and then modified by exchanging some stations between the promising candidate set and the remaining station set. A number of the best station candidates in the promising candidate set can be fixed to improve computation time. In addition, a parallel computing strategy is integrated into solving simultaneously the set of subproblems to speed up computation time. Experimental results carried out on the benchmark instances show that our algorithm outperforms genetic algorithm and greedy algorithm. As compared with CPLEX solver, our algorithm can obtain all the optimal solutions on the tested instances with less computation time

LNG Bunkering Network Design in Inland Waterways

Growing awareness of the environment and new regulations of the International Maritime Organization and the European Union are forcing ship-owners to reduce pollution. The use of liquefied natural gas (LNG) is one of the most promising options for achieving a reduction in pollution for inland shipping and short sea shipping. However, the infrastructure to facilitate the broad use of LNG is yet to be developed. We advance and analyze models that suggest LNG infrastructure development plans for refueling stations that support pipeline-to-ship and truck-to-ship bunkering, specifying locations, types, and capacities, and that take into account the characteristics of LNG, such as boil-off during storage and loading. We develop an effective primal heuristic, based on Lagrangian relaxation, for the solution of the models. We validate our approach by performing a computational study for the waterway network in the Arnhem-Nijmegen region in the West-European river network, including, among others, multi-year scenarios in which capacity expansion and reduction are possible

Infrastructure planning for electrified transportation

Due to the climate crisis, the importance of reducing greenhouse gas (GHG) has been recognized by governments, private companies and the general public alike. Yet carbon capturing-based approaches are difficult to integrate with transportation, which is one of the largest GHG producing sectors, Therefore, electrification is the only viable approach to reduce emissions from transportation, by greatly increasing the market share of electric vehicles (EVs). However, the mass adoption of either (or both) of battery EVs (BEVs) and fuel cell EVs (FCEVs) require a large amount of supporting infrastructures, particularly the construction of EV charging stations (EVCSs) for BEVs and hydrogen refuelling stations (HRSs) for FCEVs. The goal of this study is to provide effective approaches for the sizing and sitting of EVCSs and HRSs to facilitate the deployment of BEVs and FCEVs. The background and an overview of the thesis are provided in Chapter 1, where the gaps in the current research are pointed out and the objectives of the thesis are formulated. Chapter 2 reviewed the current state of technologies regarding the hydrogen life cycle as well as the popular planning models for EVCSs and HRSs. In Chapter 3, to achieve a competitive strategy from the perspective of private companies, a market-based framework is proposed for the problem of EVCS planning by leveraging Graph Convolutional Network (GCN) and game theory. In Chapter 4, a multi-objective planning model is developed for EVCSs and the expansion of distribution network with significant renewable components while considering uncertainties in EV charging behaviour. Additionally, in Chapter 5, a planning model of HRS maximises the long-term profit while considering different practical constraints. The HRS planning model also addresses short-term demand uncertainty via redistribution. The models that are developed in this study are validated using either synthetic or real-world case studies, and the simulation results showed the effectiveness of the proposed models. Finally Chapter 6 summarises the major achievements of the thesis and provides directions for further research

Enabling long journeys in electric vehicles:design and demonstration of an infrastructure location model

This research develops a methodology and model formulation which suggests locations for rapid chargers to help assist infrastructure development and enable greater battery electric vehicle (BEV) usage. The model considers the likely travel patterns of BEVs and their subsequent charging demands across a large road network, where no prior candidate site information is required. Using a GIS-based methodology, polygons are constructed which represent the charging demand zones for particular routes across a real-world road network. The use of polygons allows the maximum number of charging combinations to be considered whilst limiting the input intensity needed for the model. Further polygons are added to represent deviation possibilities, meaning that placement of charge points away from the shortest path is possible, given a penalty function. A validation of the model is carried out by assessing the expected demand at current rapid charging locations and comparing to recorded empirical usage data. Results suggest that the developed model provides a good approximation to real world observations, and that for the provision of charging, location matters. The model is also implemented where no prior candidate site information is required. As such, locations are chosen based on the weighted overlay between several different routes where BEV journeys may be expected. In doing so many locations, or types of locations, could be compared against one another and then analysed in relation to siting practicalities, such as cost, land permission and infrastructure availability. Results show that efficient facility location, given numerous siting possibilities across a large road network can be achieved. Slight improvements to the standard greedy adding technique are made by adding combination weightings which aim to reward important long distance routes that require more than one charge to complete

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

New Perspectives on Electric Vehicles

Modern transportation systems have adverse effects on the climate, emitting greenhouse gases and polluting the air. As such, new modes of non-polluting transportation, including electric vehicles and plug-in hybrids, are a major focus of current research and development. This book explores the future of transportation. It is divided into four sections: “Electric Vehicles Infrastructures,” “Architectures of the Electric Vehicles,” “Technologies of the Electric Vehicles,” and “Propulsion Systems.” The chapter authors share their research experience regarding the main barriers in electric vehicle implementation, their thoughts on electric vehicle modelling and control, and network communication challenges