DOCSLIB.ORG

Measuring Range Anxiety: the Substitution-Emergency-Detour (SED) Method

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Measuring Range Anxiety: the Substitution-Emergency-Detour (SED) Method World Electric Vehicle Journal Vol. 5 - ISSN 2032-6653 - © 2012 WEVA Page 0008 EVS26 Los Angeles, California, May 6-9, 2012 Measuring Range Anxiety: the Substitution-Emergency-Detour (SED) Method Zhenhong Lin Oak Ridge National Laboratory, 2360 Cherahala Boulevard, Knoxville, TN 37932, USA Abstract Range anxiety has been widely recognized as a critical barrier for battery electric vehicles (BEV), but its measurement method is lacking. Such a knowledge gap makes it difficult to analyse the competiveness of and the demand for BEVs. This study develops the Substitution-Emergency-Detour (SED) method to measure the range anxiety cost, and conducts sensitivity analysis of range anxiety cost with respect to nine factors. It is found that the most effective ways to reduce range anxiety are reducing driving intensity, increasing the vehicle range, extending the vehicle range with better charging infrastructure. Better household vehicle flexibility and less range uncertainty can also significantly reduce range anxiety. The SED method and the numerical results are expected to contribute to better understanding of the range anxiety barrier and the BEV demand. Keywords: EV (electric vehicle), range, market 1 Introduction 2 Concept and Formulation Range anxiety has been widely recognized as a The SED method is based on the notion that range critical barrier for battery electric vehicles anxiety can be theoretically compensated with no- (BEV), but has yet to be properly measured [1]- cost, hassle-free vehicle substitution, emergency [3]. This makes it difficult to analyse the roadside service (ERS), and detours to access competiveness of and the demand for BEVs. A public chargers. In reality, these activities cost good method to measure range anxiety could measurable money, time and inconvenience, thus allow an improved understanding of market providing a framework to formulate the range barriers, optimization of the BEV range, and anxiety cost as a function of value of time, exploration of solutions to reduce range anxiety. household vehicle ownership, charging infrastructure coverage and driving patterns. This study develops the Substitution-Emergency- Detour (SED) method to monetize the range anxiety. The concept of the SED method is explained in the next section, followed by some ∑ ( ) numerical examples that illustrate how the method can be used and how range anxiety cost Figure 1: the Substitution-Emergency-Detour method can be sensitive to the chosen 9 factors. EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 1 World Electric Vehicle Journal Vol. 5 - ISSN 2032-6653 - © 2012 WEVA Page 0009 For further formulation, 4 random variables are When xe< Re, the driver would operate the BEV, considered—the expected daily driving distance hoping the uncertain actual daily distance (x) not (xe), the unexpected deviation (xd) of actual daily to exceed the uncertain actual vehicle range (r). distance (x) from xe, the unexpected deviation But once the BEV leaves home, the actual daily (rd) of the actual vehicle range (r or rr, explained distance (x) may deviate from xe due to unplanned later) from the expected vehicle range (Re), the errands or other reasons, and the actual vehicle probability (Pe) of using emergency roadside range (r) may also deviate from Re as caused by service as opposed to detouring to the nearest on-road speeds, use of air conditioner, congestion, charging station under the condition that the and other reasons. These situations result in the actual vehicle range is less than the actual daily possibility (Ped) of the actual daily distance distance. When no detour is taken, the actual exceeding the actual vehicle range (i.e. x>r) and vehicle range (r) is the sum of the expected the need to either call for emergency roadside vehicle range (Re) and the unexpected range services or detour to a public charger. Such deviation (rd). When a detour is taken, the actual possibility is expressed in the equation (8), where vehicle range (rr) will include an additional g(x) and h(r) are the probability density function of extended range (Rb) resulting from the detour x and r, respectively. and recharge. This detour-extended range (R ) is b (7) a function of the BEV range (R ), the detour 0 ( ) ( ) ( ) recharge ratio (E ) and the wasted range (L) in ∫ ∫ ∫ b reaching and returning from the charger (5). The expected vehicle range (Re) is the product of the BEV range (R0) and the expected extension ratio Let E0 and D0 respectively be the cost of one (E) that reflects the regular charging activities roadside service and one detour for recharging. anticipated by the driver. The relationships Between these two options, the probability (Pe) of among these variables are illustrated in the calling for roadside services can be expressed as a equations (1)—(5), with lowercase letters function, as in the equation (8), of E0, D0 and the representing random variables and uppercase price coefficient (β) that reflects the choice letters representing deterministic variables or elasticity. constant parameters. (8) (1) (2) The annual cost of emergency roadside services (E) and detour charging (D) can be expressed in the (3) equation (9) and (10). (4) (9) (5) ( ) (10) Logically, a vehicle substitution occurs when the expected daily distance exceeds the expected vehicle range (i.e. xe> Re). The cost of obtaining 3 Example and using a substitution vehicle (S0) could be as low as the daily depreciation of another vehicle To apply the SED method established by the in the household and as high as a daily rate of a equation (1)—(10), a Base example is created rental vehicle plus fees of home delivery. based on the following assumptions. Therefore, the annual substitution cost (S) can be The cost of one roadside service (E ) is assumed to expressed in the equation (6), where f(xe) is the 0 be $62 for each service call, based on rates of fuel probability density function of xe. delivery services by rental car companies [4]. (6) ∫ ( ) The cost per detour for recharging (D0) depends on the availability of public chargers, time value and charging time. It is estimated to be $63 based on EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 2 World Electric Vehicle Journal Vol. 5 - ISSN 2032-6653 - © 2012 WEVA Page 0010 the assumptions of the wage rate at $21/hour, the 4 Sensitivity Analysis BEV range at 100 miles, the detour charging ratio at 100%, the charging availability at 0.5% Sensitivity of the range anxiety cost is analysed (equivalent percentage of existing gasoline with respect to the following 9 factors, each stations), and the charging power at 60 kW. changed by 50% up and down. Vehicle Substitution Cost ($/each) The price coefficient (β) is assumed to be -0.325, Emergency Roadside Service Cost ($/each) based on the assumption of price elasticity at -10. Value of Time ($/hour) Daily VMT Mode (miles) The probability density function f(xe) is assumed Annual VMT (miles) to be a Gamma distribution where the driver Vehicle Range (miles) drives 16000 miles per year and most frequently Expected Extension Ratio (100%) 20 miles per day. The form of distribution has Range St.D. (miles) been adopted for plug-in electric vehicle analysis Charger Availability (100%) [5][6] and validated with real-world travel data [7]. The probability density function g(x) is 4.1 Vehicle Substitution Cost assumed to be a Gamma distribution with its Vehicle substitution cost indicates the easiness to mean at x and its standard deviation at 2.5% of e obtain a backup vehicle when the expected daily x . The probability density function h(r) is e distance exceeds the expected vehicle range. It assumed to be a Gamma distribution with its depends on household vehicle flexibility and can mean at R and its standard deviation at 20 miles, e range from $15 to $50, where the lower bound based on real-world BEV data. Figure 2 shows reflects the cost of using an easily available vehicle the distribution of x and r and the distribution of e in the household by considering vehicle x on the days when x =70 miles. The overlap e depreciation and the upper bound reflects the cost between the distributions of x (subject to x =70) e of a delivered rental vehicle. As shown in Figure 3, and r indicates the chance of the actual daily the vehicle substitution cost only affects the distance exceeding the actual vehicle range, even Substitution component of range anxiety. Higher though the expected daily distance (70 miles) is vehicle substitution cost leads to higher range much below the expected vehicle range (100 anxiety cost. High-income consumers are more miles). likely to have high vehicle ownership and thus better vehicle flexibility. Because of this, they may perceive less range anxiety. However, they may have higher value of time and perceive a higher cost for detour recharges. Figure 2: Variation of daily distance and range Both the detour recharge ratio (Eb) and the expected extension ratio (E) are assumed to be 100%, meaning that the driver can use up to 100% of the BEV range each day and extend the BEV range by another 100% when detouring to Figure 3: Range anxiety sensitivity to vehicle recharge. substitution cost Based on the Base assumptions above, the range 4.2 Emergency Roadside Service Cost cost is estimated to be $1309/year, 53% of which is for vehicle substitution, 28% for emergency The ERS cost rate indicates the penalty each time a roadside service, and 19% for detour charging. BEV is out of range on the road and the driver EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 3 World Electric Vehicle Journal Vol. 5 - ISSN 2032-6653 - © 2012 WEVA Page 0011 calls for ERS.
Load more

Recommended publications
  • A Review of Range Extenders in Battery Electric Vehicles: Current Progress and Future Perspectives
    Review A Review of Range Extenders in Battery Electric Vehicles: Current Progress and Future Perspectives Manh-Kien Tran 1,* , Asad Bhatti 2, Reid Vrolyk 1, Derek Wong 1 , Satyam Panchal 2 , Michael Fowler 1 and Roydon Fraser 2 1 Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada; [email protected] (R.V.); [email protected] (D.W.); [email protected] (M.F.) 2 Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada; [email protected] (A.B.); [email protected] (S.P.); [email protected] (R.F.) * Correspondence: [email protected]; Tel.: +1-519-880-6108 Abstract: Emissions from the transportation sector are significant contributors to climate change and health problems because of the common use of gasoline vehicles. Countries in the world are attempting to transition away from gasoline vehicles and to electric vehicles (EVs), in order to reduce emissions. However, there are several practical limitations with EVs, one of which is the “range anxiety” issue, due to the lack of charging infrastructure, the high cost of long-ranged EVs, and the limited range of affordable EVs. One potential solution to the range anxiety problem is the use of range extenders, to extend the driving range of EVs while optimizing the costs and performance of the vehicles. This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro Citation: Tran, M.-K.; Bhatti, A.; gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent Vrolyk, R.; Wong, D.; Panchal, S.; Fowler, M.; Fraser, R.
    [Show full text]
  • Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact
    Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact _______________ Buket AVCI Karan GIROTRA Serguei NETESSINE 2012/18/TOM Electronic copy available at: http://ssrn.com/abstract=2005092 Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact Buket Avci* Karan Girotra** Serguei Netessine*** This paper can be downloaded without charge from the Social Science Research Network electronic library at: http://ssrn.com/abstract=2005092 * PhD Candidate in Technology and Operations Management at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex, France. Email: [email protected] ** Assistant Professor of Technology and Operations Management at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex, France. Email: [email protected] *** The Timken Chaired Professor of Global Technology and Innovation, Professor of Technology and Operations Management at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex, France. Email: [email protected] A Working Paper is the author’s intellectual property. It is intended as a means to promote research to interested readers. Its content should not be copied or hosted on any server without written permission from [email protected] Find more INSEAD papers at http://www.insead.edu/facultyresearch/research/search_papers.cfm Electronic copy available at: http://ssrn.com/abstract=2005092 ELECTRIC VEHICLES WITH A BATTERY SWITCHING STATION: ADOPTION AND ENVIRONMENTAL IMPACT Abstract. Widespread adoption of Electric Vehicles can limit the environmental impact of trans- portation and reduce oil dependence. However, limited range and high upfront battery costs have limited consumer adoption. A novel switching-station-based solution is extensively touted as a promis- ing remedy that resolves range anxiety.
    [Show full text]
  • Electric Vehicle Adoption: an Analysis of Best Practice and Pitfalls for Policy Making from Experiences of Europe and the US
    Received: 4 May 2017 Revised: 17 September 2017 Accepted: 23 November 2017 DOI: 10.1111/gec3.12358 ARTICLE Electric vehicle adoption: An analysis of best practice and pitfalls for policy making from experiences of Europe and the US Gail Helen Broadbent | Danielle Drozdzewski | Graciela Metternicht University of New South Wales, Sydney, Australia Abstract Correspondence Accelerating the rate of electric vehicle (EV) adoption is an objective Gail Helen Broadbent, University of New of many countries to mitigate and ameliorate negative externalities South Wales. arising from the use of fossil fuels for personal motorised transporta- tion including: greenhouse gas (GHG) emissions, air pollution and noise, as well as increasing energy security and reducing budget deficits. Within the dynamic field of EVs, this paper highlights strategic directions for policy makers to increase EV uptake. The paper critically reviews measures adopted by some industrialised countries to motivate consumer purchase of EVs rather than conventional internal combustion vehicles (ICVs). A key focus is the role of financial and soft incentives to encourage EV adoption. The analysis reveals that not all incentives are equally effective; an adequate recharger network appears to be a common concerning factor for EV adoption due to customer anxiety and vehicle limitations. Best practice strategies that could foster a faster transition to EV adoption include appropriate legislation, installation and maintenance of an adequate public recharger network, government procurement programs, and investment in information programs to accelerate the transition towards fossil free driving. The paper evidences how implementation of these strategies can affect overall adoption rates. 1 | INTRODUCTION In 2010, the transport sector contributed 23% of total energy‐related CO2 emissions (UN Climate Summit, 2014), thus making it a driver of climate change as well as agent for responses towards its mitigation.
    [Show full text]
  • EV White Paper
    2 | Page UAW Research EXECUTIVE SUMMARY: STRATEGIES FOR A FAIR EV FUTURE .................................................................................. 4 COMING SHIFT TO EVS .................................................................................................................................................... 4 DISRUPTIVE IMPLICATIONS OF EVS ..................................................................................................................................... 4 WILL THE U.S. FALL BEHIND? ........................................................................................................................................... 5 CREATING AN INDUSTRIAL POLICY TO LEAD .......................................................................................................................... 5 WHAT IS AN EV? WHY EVS? ................................................................................................................................... 6 CLIMATE CONCERNS POINT TO EVS .................................................................................................................................... 6 DIFFERENCES BETWEEN EVS AND ICES ............................................................................................................................... 7 THE COMING EV POWERTRAIN DISRUPTION ......................................................................................................... 8 ELECTRIC VEHICLE PRICES TO BECOME COMPETITIVE ............................................................................................................
    [Show full text]
  • Electric Vehicle Tour Planning Considering Range Anxiety
    sustainability Article Electric Vehicle Tour Planning Considering Range Anxiety Rui Chen 1,2 , Xinglu Liu 3, Lixin Miao 2,3 and Peng Yang 2,* 1 Department of Industrial Engineering, Tsinghua University, Beijing 100084, China; [email protected] 2 Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China; [email protected] 3 Intelligent Transportation and Logistics Systems Laboratory, Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, China; [email protected] * Correspondence: [email protected] Received: 23 March 2020; Accepted: 28 April 2020; Published: 2 May 2020 Abstract: In this study, the tour planning problem for electric vehicles is investigated. We aim to derive the optimal route and thus, to maximize profitability and minimize range anxiety within the time horizon. To solve this problem, a bi-objective mixed integer model is proposed. Specifically, we first introduced the reliability of route planning and quantified it as a cost with specific functions. The nonlinear model was then converted into a bi-objective mixed integer linear program, and an interactive branch and bound algorithm was adopted. Numerical experiments conducted on different networks have shown that the model that considers range anxiety offers more effective solutions. This means that our model is able to plan the routes with high reliability and low risk of profit loss and accidents. Keywords: electric vehicle; tour planning; range anxiety; bi-objective programming 1. Introduction Currently, about 86% of global primary energy demand depends on fossil fuels, in which coal, gas and oil account for 23%, 27% and 36%, respectively. Based on current information about reserves and daily production, oil will be depleted in 2066 [1].
    [Show full text]
  • Calculating Electric Drive Vehicle Greenhouse Gas Emissions
    VEHICLE ELECTRIFICatION POLICY STUDY TSA K 5 REPORT AUGUST 2012 © 2012 INTERNATIONAL COUNCIL ON CLEAN TRANSPORTATION Calculating Electric Drive Vehicle Greenhouse Gas Emissions BY ED PIKE www.theicct.org [email protected] BEIJING | BERLIN | BRUSSELS | SAN FRANCISCO | WASHINGTON The author appreciates the helpful review comments provided by: Kate Blumberg, ICCT Joshua Cunningham, Plug-in Electric Vehicle Collaborative David Ganss, U.S. EPA Pierre Gaudillat John German, ICCT Zoltan Jung, U.S. EPA Li Lili, State Grid Energy Research Institute Alan Lloyd, ICCT Peter Mock, ICCT Ed Nam, U.S. EPA Martin Rocholl, European Climate Foundation Chuck Shulock, Shulock Consulting Luke Tonachel, NRDC Nic Lutsey Michael Wang, Argonne National Laboratory Funding for this work was generously provided by the ClimateWorks Foundation, the William and Flora Hewlett Foundation, and the Energy Foundation. © 2012 The International Council on Clean Transportation 1225 I Street NW, Suite 900, Washington DC 20005 CALCULATING ELECTRIC DRIVE VEHICLE GREENHOUSE GAS EMISSIONS EXECUTIVE SUMMARY The object of this paper is to present a methodology for calculating the greenhouse gas (GHG) emissions attributable to the production of electricity and hydrogen used to power electric drive (edrive) vehicles such as battery electric (BEV), plug-in hybrid electric (PHEV), and fuel cell electric (FCEV) vehicles. Calculating these emissions is important for understanding the potential role of edrive vehicles in meeting GHG reduction goals, implementing passenger vehicle GHG standards, and providing consumer information about energy efficiency and emissions. This paper identifies methods ot determine edrive vehicle efficiency, energy supply “well-to-tank” GHG intensity, edrive vehicle miles traveled, and mode split for plug-in hybrids, all of which can serve as a basis for calculating edrive upstream emissions.
    [Show full text]
  • An Innovative Approach to Range Anxiety in Electric Vehicles Ian
    1 Range extenders: an innovative approach to range anxiety in electric vehicles Ian Clarke* and Athena Piterou University of Greenwich, Old Royal Navy College, 30 Park Row, SE10 9LS, London Email: [email protected] Email: [email protected] *Corresponding author Abstract: Electric vehicles have been a source of major innovation but the problem of vehicle range persists. Range extenders, auxiliary power units that can be attached to vehicles as a trailer, seek to solve the problem by providing additional range during longer journeys. This study uses patent data, social network analysis and qualitative data to examine the development of this technology. It finds that small firms have made significant progress in technological terms and have also designed credible business models. They have met resistance from larger vehicle manufacturers, however, whose preference is to wait for developments in battery density despite great uncertainty as to when these will come. Keywords: electric vehicles; innovation; range anxiety; range extenders. Biographical notes: Ian Clarke is a Senior Lecturer in Innovation and Enterprise at the University of Greenwich. His research interests have focused on the role of intermediary organisations in a number of different settings, including developing economy clusters. He is interested in innovation and sustainability and is researching the development and adoption of electric vehicles, particularly in relation to the problem of vehicle range. A more recent research interest has been the re-emergence of tramways and light rail in a number of different European contexts. Athena Piterou is a Senior Lecturer in Sustainability at the University of Greenwich. Her past research projects include a study of the emergence of electronic books as a prospective transition pathway in the print-on-paper regime and an examination of community renewable energy in London.
    [Show full text]
  • Stretching, Embeddedness, and Scripts in a Sociotechnical Transition: MARK Explaining the Failure of Electric Mobility at Better Place (2007–2013)
    Technological Forecasting & Social Change 123 (2017) 24–34 Contents lists available at ScienceDirect Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore Stretching, embeddedness, and scripts in a sociotechnical transition: MARK Explaining the failure of electric mobility at Better Place (2007–2013) ⁎ Benjamin K. Sovacoola,b, , Lance Noela, Renato J. Orsatoc a Center for Energy Technologies, Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, DK-7400 Herning, Denmark b Science Policy Research Unit (SPRU), School of Business, Management, and Economics, University of Sussex, United Kingdom c São Paulo School of Management (EAESP), Getúlio Vargas Foundation (FGV), Sao Paulo, Brazil ARTICLE INFO ABSTRACT Keywords: Based on field research, interviews, and participant observation, this study explores the failure of Better Place—a Electric vehicles now bankrupt company—to successfully demonstrate and deploy battery swapping stations and electric vehicle Energy transitions charging infrastructure. To do so, it draws from concepts in innovation studies, sociotechnical transitions, Fit-stretch management science, organizational studies, and sociology. The study expands upon the notion of “fit-stretch”, Sociotechnical systems which explains how innovations can move from an initial “fit” (with existing user practices, discourses, technical form) to a subsequent “stretch” (as the technology further develops, new functionalities are opened up, etc.) in the process of long-term transitions. It also draws from the “dialectical issue life cycle model” or “triple em- beddedness framework” to explain the process whereby incumbent industry actors can introduce defensive in- novations to “contain” a new niche from expanding. It lastly incorporates elements from design-driven in- novation and organizational learning related to schemas and scripts, concepts that illustrate the vision- dependent and discursive elements of the innovation process.
    [Show full text]
  • Plug-In Hybrids As Measured on T&E’S Tests
    Transport & Environment Published: November 2020 In-house analysis by Transport & Environment, testing of cars undertaken by Emissions Analytics on behalf of Transport Environment. Author: Anna Krajinska Expert group: Julia Poliscanova, Lucien Mathieu, Carlos Calvo Ambel Editeur responsable: William Todts, Executive Director © 2020 European Federation for Transport and Environment AISBL To cite this study Transport & Environment (2020). A new Dieselgate in the making. ​ Further information Anna KRAJINSKA Emissions Engineer Transport & Environment [email protected] Mobile: +44 (0)7761536337 Square de Meeûs, 18 – 2nd floor | B-1050 | Brussels | Belgium www.transportenvironment.org | @transenv | fb: Transport & Environment Acknowledgements The authors kindly acknowledge the external peer reviewers Peter Mock and Georg Bieker from the International Council on Clean Transportation and Julius Jöhrens from the Institute for Energy and Environmental Research, Heidelberg. Particular thanks goes to Patrick Plötz from the Fraunhofer Institute for providing the real world utility factors used in this report. The findings and views put forward in this publication are the sole responsibility of the authors listed above. The same applies to any potential factual errors or methodology flaws. A study by 2 Executive Summary Plug-in hybrid vehicle (PHEV) sales are skyrocketing in Europe with half a million expected to be sold this year alone. Carmakers need to sell low emission vehicles to comply with the 2020/21 EU car CO2 standard which took effect in January 2020. But are these cars as low emission in the real world as in carmaker test labs? Or are PHEVs high emitting vehicles that carmakers sell as a compliance trick to meet the CO2 targets? To find out, Transport & Environment has commissioned Emissions Analytics to test three of the most popular PHEVs sold in 2019: a BMW X5 (longest EV range PHEV available), a Volvo XC60 and the Mitsubishi ​ ​ ​ ​ Outlander.
    [Show full text]
  • Accelerating Electric Vehicle Adoption
    CUSTOMER SOLUTIONS ELECTRIC TRANSPORTATION Accelerating Electric Vehicle Adoption February 2018 CUSTOMER SOLUTIONS ELECTRIC TRANSPORTATION Accelerating Electric Vehicle Adoption Prepared by: Kellen Schefter Becky Knox EEI Customer Solutions February 2018 © 2018 by the Edison Electric Institute (EEI). All rights reserved. Published 2018. Printed in the United States of America. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechani- cal, including photocopying, recording, or any information storage or retrieval system or method, now known or hereinafter invented or adopted, without the express prior written permission of the Edison Electric Institute. Attribution Notice and Disclaimer This work was prepared by the Edison Electric Institute (EEI). When used as a reference, attribution to EEI is re- quested. EEI, any member of EEI, and any person acting on its behalf (a) does not make any warranty, express or implied, with respect to the accuracy, completeness or usefulness of the information, advice or recommendations contained in this work, and (b) does not assume and expressly disclaims any liability with respect to the use of, or for damages resulting from the use of any information, advice or recommendations contained in this work. Published by: Edison Electric Institute 701 Pennsylvania Avenue, N.W. Washington, D.C. 20004-2696 Phone: 202-508-5000 Web site: www.eei.org Accelerating Electric Vehicle Adoption Table of Contents Executive Summary ................................................................................................................................
    [Show full text]
  • A Study of Electric Vehicle Charging Patterns and Range Anxiety
    UPTEC STS13 015 Examensarbete 15 hp 4 Juni 2013 A study of electric vehicle charging patterns and range anxiety Daniel Knutsen Oscar Willén Abstract A study of electric vehicle charging patterns and range anxiety Daniel Knutsen, Oscar Willén Teknisk- naturvetenskaplig fakultet UTH-enheten Range anxiety is a relatively new concept which is defined as the fear of Besöksadress: running out of power when driving an Ångströmlaboratoriet Lägerhyddsvägen 1 electric vehicle. To decrease range Hus 4, Plan 0 anxiety you can increase the battery size or decrease the minimum state of Postadress: charge, the least amount of power that Box 536 751 21 Uppsala can be left in the battery, or to expand the available fast charging Telefon: infrastructure. But is that economical 018 – 471 30 03 feasible or even technically possible in Telefax: today’s society? In this project we have 018 – 471 30 00 used a theoretical model for estimating range anxiety and have simulated the Hemsida: average electricity consumption using http://www.teknat.uu.se/student two different kinds of electric vehicles, to see how often they reach range anxiety according to a specific definition of range anxiety implemented in this model. The simulations were performed for different scenarios in order to evaluate the effect of different parameters on range anxiety. The result that we got were that range anxiety can be decreased with bigger batteries but to get range anxiety just a few times a year you have to use battery sizes which aren’t economical feasible today. Despite the shortcomings of todays electric vehicles there are promising new and future technologies such as better batteries which might help alleviate range anxiety for electric vehicle owner.
    [Show full text]
  • User Guide of the ORNL MA3T Model (Working Version)
    User Guide of the ORNL MA3T Model (V20130729) Zhenhong Lin David Greene Oak Ridge National Laboratory Jake Ward U.S. Department of Energy Table of Contents List of Abbreviations .......................................................................................................... 2 1 Introduction ................................................................................................................. 3 2 File structure ................................................................................................................ 4 3 Running the model....................................................................................................... 5 4 Viewing and using the results ...................................................................................... 7 4.1 Result by choice ................................................................................................... 7 4.2 Result by consumer .............................................................................................. 8 5 Changing inputs ........................................................................................................... 8 5.1 Scenario ................................................................................................................ 9 5.2 Market segmentation .......................................................................................... 12 5.3 Energy prices ...................................................................................................... 14 5.4 Policy .................................................................................................................
    [Show full text]