Plug-in vs. Wireless Charging: Life Cycle Energy and Greenhouse Gas Emission Analysis of an Electric Bus System

Vehicle electrification through implementation of electric vehicles (EVs) with rechargeable batteries has the potential to significantly reduce the greenhouse gas emissions compared to a fleet of internal combustion engine vehicles (ICEVs). Wireless charging, as opposed to plug-in charging, is an alternative charging method for electric vehicles (EVs) with rechargeable batteries and can be applicable to EVs with fixed routes, such as transit buses. This thesis study adds to the current research of EV wireless charging by utilizing the Life Cycle Assessment (LCA) to provide a comprehensive framework for comparing the life cycle energy demand and greenhouse gas emissions associated with a stationary wireless charging all-electric bus system to a plug-in charging all-electric bus system. Life cycle inventory analysis of both plug-in and wireless charging hardware was conducted, and battery downsizing, vehicle lightweighting and use-phase energy consumption are modeled. A bus system in Ann Arbor and Ypsilanti area in Michigan is used as the basis for bus system modeling. Results show that the wirelessly charged battery can be downsized to 27-44% of a plug-in charged battery. The associated reduction of 12-16% in bus weight for the wireless buses can induce a reduction of 5.4-7.0% in battery-to-wheel energy consumption. In the base case, the wireless charging system is comparable to the plug-in charging system in terms of life cycle energy consumption and greenhouse gas emissions. To further improve the energy and environmental performance of a wireless charging electric bus system, it is important to focus on key parameters including carbon intensity of the electric grid and wireless charging efficiency. If the wireless charging efficiency is improved to the same level as the assumed plug-in charging efficiency (90%), the wireless charging system would emit 6.3% less greenhouse gases than the plug-in charging system. Keywords: Wireless charging; Plug-in charging; Life cycle assessment; VehicleMaster of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/110984/1/Bi, Zicheng (Kevin) - Thesis April 2015.pd

Challenges and Barriers of Wireless Charging Technologies for Electric Vehicles

Electric vehicles could be a significant aid in lowering greenhouse gas emissions. Even though extensive study has been done on the features and traits of electric vehicles and the nature of their charging infrastructure, network modeling for electric vehicle manufacturing has been limited and unchanging. The necessity of wireless electric vehicle charging, based on magnetic resonance coupling, drove the primary aims for this review work. Herein, we examined the basic theoretical framework for wireless power transmission systems for EV charging and performed a software-in-the-loop analysis, in addition to carrying out a performance analysis of an EV charging system based on magnetic resonance. This study also covered power pad designs and created workable remedies for the following issues: (i) how power pad positioning affected the function of wireless charging systems and (ii) how to develop strategies to keep power efficiency at its highest level. Moreover, safety features of wireless charging systems, owing to interruption from foreign objects and/or living objects, were analyzed, and solutions were proposed to ensure such systems would operate as safely and optimally as possible

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

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

CleanTX Analysis on the Smart Grid

The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut

Review on Key Factors of Wireless Power Transfer Technology for Electric Vehicles

Electric vehicles (EVs) have become an alternative option for a clean energy society. A new charging technology which is wireless charging has been developed to satisfy the limitations of EVs which are the electric drive range and battery storage. Companies like Tesla, BMW, and Nissan have already started to develop wireless charging for EVs. This paper presents a literature review on wireless charging of EVs. The existing technologies for Wireless Power Transfer (WPT) system are summarized for different power applications. Coil design plays the most vital role in the WPT system so the different coil design with the transferred efficiency is reviewed. The other important parameters and technical components like significant factors of WPT system, track layout of dynamic wireless charging, foreign object detection method, and position alignment method that are affecting the efficiency of the wireless charging system are also discussed. Lastly, health and safety concerns for human beings and living things are investigated

Vehicular Energy Network

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Experimental review of an improving system on wireless power transfer via auto tuning of frequency

Wireless power transfer for electric vehicles is focused because these vehicles cannot run long distance without frequently charging. If these vehicles are charged from outside wirelessly, for example an alternating current (AC) power supply is embed under road, the problem is going to be solved. However, efficiency of wireless power transfer depends on various factors, therefore many contrivances should be considered to realize optimal transfer. In this paper, we focused on frequency of inverter, and created auto tuning system of it in response to the distance of inductors. On this system, frequency was modified automatically by a microcontroller and sensor at the same time position of a load changed. Finally, we confirmed that voltage of light emitting diode (LED) was improved by utilizing our system compared with non-tuning frequency