Modelling Inductive Charging of Battery Electric Vehicles using an Agent-Based Approach

The introduction of battery electric vehicles (BEVs) could help to reduce dependence on fossil fuels and emissions from transportation and as such increase energy security and foster sustainable use of energy resources. However a major barrier to the introduction of BEVs is their limited battery capacity and long charging durations. To address these issues of BEVs several solutions are proposed such as battery swapping and fast charging stations. However apart from these stationary modes of charging, recently a new mode of charging has been introduced which is called inductive charging. This allows charging of BEVs as they drive along roads without the need of plugs, using induction. But it is unclear, if and how such technology could be utilized best. In order to investigate the possible impact of the introduction of such inductive charging infrastructure, its potential and its optimal placement, a framework for simulating BEVs using a multi-agent transport simulation was used. This framework was extended by an inductive charging module and initial test runs were performed. In this paper we present the simulation results of these preliminary tests together with analysis which suggests that battery sizes of BEVs could be reduced even if inductive charging technology is implemented only at a small number of high traffic volume links. The paper also demonstrates that our model can effectively support policy and decision making for deploying inductive charging infrastructure

Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed

This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie

A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification

In recent years, electrified transportation, be it in the form of buses, trains, or cars have become an emerging form of mobility. Electric vehicles (EVs), especially, are set to expand the amount of electric miles driven and energy consumed. Nevertheless, the question remains as to whether EVs will be technically feasible within infrastructure systems. Fundamentally, EVs interact with three interconnected systems: the (physical) transportation system, the electric power grid, and their supporting information systems. Coupling of the two physical systems essentially forms a nexus, the transportation-electricity nexus (TEN). This paper presents a hybrid dynamic system assessment methodology for multi-modal transportation-electrification. At its core, it utilizes a mathematical model which consists of a marked Petri-net model superimposed on the continuous time microscopic traffic dynamics and the electrical state evolution. The methodology consists of four steps: (1) establish the TEN structure; (2) establish the TEN behavior; (3) establish the TEN Intelligent Transportation-Energy System (ITES) decision-making; and (4) assess the TEN performance. In the presentation of the methodology, the Symmetrica test case is used throughout as an illustrative example. Consequently, values for several measures of performance are provided. This methodology is presented generically and may be used to assess the effects of transportation-electrification in any city or area; opening up possibilities for many future studies

A traffic-aware electric vehicle charging management system for smart cities

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The expected increase in the number of electric vehicles (EVs) in the coming years will contribute to reducing CO2 pollution in our cities. Currently, EVs' users may suffer from distress due to long charging service times and overloaded charging stations (CSs). Critical traffic conditions (e.g., traffic jams) affect EVs' trip time (TT) towards CSs and thus influence the total trip duration. With this concern, Intelligent transport systems (ITS) and more specifically connected vehicle technologies, can leverage an efficient real-time EV charging service by jointly considering CSs status and traffic conditions in the city. In this work, we propose a scheme to manage EVs' charging planning, focusing on the selection of a CS for the energy-requiring EV. The proposed scheme considers anticipated charging slots reservations performed through a vehicular ad hoc network (VANET), which has been regarded as a cost-efficient communication framework. In specific, we consider two aspects: 1) the EV's total trip time towards its destination considering an intermediate charging at each candidate CS, and 2) the communication delay of the VANET routing protocol. First, in order to estimate the EV's total trip time, our CS selection scheme takes into account the average road speed, traffic lights, and route distance, along the path of the EV. The optimal CS that produces the minimum total charging service time (including the TT) is suggested to that energy-requiring EV. Then, we introduce two communication modes based on geographical routing protocols for VANETs to attain an anticipated charging slot reservation. Simulation results show that with our charging scheme EVs' charging service time is reduced and more EVs are successfully charged.Peer ReviewedPostprint (author's final draft

Life Cycle Analysis and Optimization of Wireless Charging Technology to Enhance Sustainability of Electric and Autonomous Vehicle Fleets

The transportation sector is undergoing a major transformation. Emerging technologies play indispensable roles in driving this mobility shift, including vehicle electrification, connection, and automation. Among them, wireless power transfer (WPT) technology, or commonly known as wireless charging technology, is in the spotlight in recent years for its applicability in charging electric vehicles (EVs). On one hand, WPT for EVs can solve some of the key challenges in EV development, by: (1) reducing range anxiety of EV owners by allowing “charging while driving”; and (2) downsizing the EV battery while still fulfilling the same trip distance. More en-route wireless charging opportunities result in battery downsizing, which reduces the high EV price and vehicle weight and improves fuel economy. On the other hand, WPT infrastructure deployment is expensive and resource-intensive, and results in significant economic, environmental, and energy burdens, which can offset these benefits. This research aims to develop and apply a life cycle analysis and optimization framework to examine the role of wireless charging technology in driving sustainable mobility. This research highlights the technology trade-offs and bridges the gap between technology development and deployment by establishing an integrated life cycle assessment and life cycle cost (LCA-LCC) model framework to characterize and evaluate the economic, environmental, and energy performance of WPT EV systems vs. conventional plug-in charging EV systems. Life cycle optimization (LCO) techniques are used to improve the life cycle performance of WPT EV fleets. Based on case studies, this research draws observations and conditions under which wireless charging technology has potential to improve life cycle environmental, energy, and economic performance of electric vehicle fleets. This study begins with developing LCA-LCC and LCO models to evaluate stationary wireless power transfer (SWPT) for transit bus systems. Based on a case study of Ann Arbor bus systems, the wirelessly charged battery can be downsized to 27–44% of a plug-in charged battery, resulting in vehicle lightweighting and fuel economy improvement in the use phase that cancels out the burdens of large-scale infrastructure. Optimal siting strategies of WPT bus charging stations reduced life cycle costs, greenhouse gases (GHG), and energy by up to 13%, 8%, and 8%, respectively, compared to extreme cases of “no charger at any bus stop” and “chargers at every stop”. Next, the LCA-LCC and LCO model framework is applied to evaluate the economic, energy, and environmental feasibility of dynamic wireless power transfer (DWPT) for charging passenger cars on highways and urban roadways. A case study of Washtenaw County indicates that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48% compared to the non-DWPT scenarios and boosts EV market penetration to around 50% of all vehicles in 20 years. Finally, synergies of WPT and autonomous driving technologies in enhancing sustainable mobility are demonstrated using the LCA framework. Compared to a plug-in charging battery electric vehicle system, a wireless charging and shared automated battery electric vehicle (W+SABEV) system will pay back GHG emission burdens of additional infrastructure deployment within 5 years if the wireless charging utility factor is above 19%.PHDNatural Resources & EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147602/1/bizc_1.pd

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system