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Author: Divyamitra Mishra
Publisher:
ISBN:
Category : Battery charging stations (Electric vehicles)
Languages : en
Pages : 114
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Book Description
Although there is economic and marketing evidence of consumers assigning additional benefits to free products and bundles, there is limited research into the behavioral consequences of offering public electric vehicle charging for free. Previous exploratory research by Maness and Lin analyzed possible economic and environmental benefits from offering free public charging infrastructure and policy. Their work found that providing free public charging infrastructure could increase plug-in electric vehicle sales and cause decreased reliance on fossil fuels in the personal transportation sector. However, their study assumed the increased value which consumers would place on free charge events. This project proposes to establish an early estimate of the value of free charging in the United States. To solve the problems such as lack of a control treatment and lack of variability in charging prices, this project proposes to study consumers' responses to a free charging program through a stated preference approach. Under this approach, valuation behaviors were explored through charger choice and vehicle choice experiment. In charger choice scenarios, the respondents were presented with charging location choice where three charging stations were presented with one charger being free and the others having a cost. In vehicle choice scenarios, the respondents were presented with vehicle choice where two EVs were presented with zero, one, two, or three years free charging along with a gasoline vehicle. Various discrete choice models were used for estimation. Determining the zero-price effect entailed adding a dummy variable to the model for when fueling cost was zero. The data used for the estimation was weighted to fit the US population. It should be remarked that the computations are based on Bayesian estimation from Greene (2004). The ZPE values were evaluated concerning individual-level parameters for the parameters that were discovered to be random (varying across individual). The results suggested that the weighted mean zero-price effect for the charger choice is valued at -$1.44 (Mixed logit model) and at -$1.19 (Latent class model) and the mean national ZPE for the entire US population was priced at -$0.96 per charging event. Additionally, the mean value of time (VOT) in regards to charging was valued at $7.66/hr (Mixed logit) and $8.15/hr (Latent class). Similarly, the value of time associated with the detour is found to be valued at $13.46/hr (Mixed Logit) and $11.71/hr (Latent class). The weighted mean zero-price effect for two years of free charging was valued at $3952 for the electric vehicle binary choice and $4200 for the electric and conventional vehicle choice. For three years of free charging, it was valued at around $4709 and $6319. The value of driving range was estimated at $67 and $143. The WTP for the driving range confirmed with the value obtained from the previous research by Dimitropoulos et al. (2013) and Greene et al. (2017). Although a limitation of stated preference studies is in determining the correct scale for the coefficient estimates, the value of time estimated is close to the USDOT personal travel VOT of 50% of hourly median household income.
Author: Divyamitra Mishra
Publisher:
ISBN:
Category : Battery charging stations (Electric vehicles)
Languages : en
Pages : 114
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Book Description
Although there is economic and marketing evidence of consumers assigning additional benefits to free products and bundles, there is limited research into the behavioral consequences of offering public electric vehicle charging for free. Previous exploratory research by Maness and Lin analyzed possible economic and environmental benefits from offering free public charging infrastructure and policy. Their work found that providing free public charging infrastructure could increase plug-in electric vehicle sales and cause decreased reliance on fossil fuels in the personal transportation sector. However, their study assumed the increased value which consumers would place on free charge events. This project proposes to establish an early estimate of the value of free charging in the United States. To solve the problems such as lack of a control treatment and lack of variability in charging prices, this project proposes to study consumers' responses to a free charging program through a stated preference approach. Under this approach, valuation behaviors were explored through charger choice and vehicle choice experiment. In charger choice scenarios, the respondents were presented with charging location choice where three charging stations were presented with one charger being free and the others having a cost. In vehicle choice scenarios, the respondents were presented with vehicle choice where two EVs were presented with zero, one, two, or three years free charging along with a gasoline vehicle. Various discrete choice models were used for estimation. Determining the zero-price effect entailed adding a dummy variable to the model for when fueling cost was zero. The data used for the estimation was weighted to fit the US population. It should be remarked that the computations are based on Bayesian estimation from Greene (2004). The ZPE values were evaluated concerning individual-level parameters for the parameters that were discovered to be random (varying across individual). The results suggested that the weighted mean zero-price effect for the charger choice is valued at -$1.44 (Mixed logit model) and at -$1.19 (Latent class model) and the mean national ZPE for the entire US population was priced at -$0.96 per charging event. Additionally, the mean value of time (VOT) in regards to charging was valued at $7.66/hr (Mixed logit) and $8.15/hr (Latent class). Similarly, the value of time associated with the detour is found to be valued at $13.46/hr (Mixed Logit) and $11.71/hr (Latent class). The weighted mean zero-price effect for two years of free charging was valued at $3952 for the electric vehicle binary choice and $4200 for the electric and conventional vehicle choice. For three years of free charging, it was valued at around $4709 and $6319. The value of driving range was estimated at $67 and $143. The WTP for the driving range confirmed with the value obtained from the previous research by Dimitropoulos et al. (2013) and Greene et al. (2017). Although a limitation of stated preference studies is in determining the correct scale for the coefficient estimates, the value of time estimated is close to the USDOT personal travel VOT of 50% of hourly median household income.
Author:
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ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers' willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
Given the complex issues associated with plug-in electric vehicle (PEV) charging and options in deploying charging infrastructure, there is interest in exploring scenarios of future charging infrastructure deployment to provide insight and guidance to national and regional stakeholders. The complexity and cost of PEV charging infrastructure pose challenges to decision makers, including individuals, communities, and companies considering infrastructure installations. The value of PEVs to consumers and fleet operators can be increased with well-planned and cost-effective deployment of charging infrastructure. This will increase the number of miles driven electrically and accelerate PEV market penetration, increasing the shared value of charging networks to an expanding consumer base. Given these complexities and challenges, the objective of the present study is to provide additional insight into the role of charging infrastructure in accelerating PEV market growth. To that end, existing studies on PEV infrastructure are summarized in a literature review. Next, an analysis of current markets is conducted with a focus on correlations between PEV adoption and public charging availability. A forward-looking case study is then conducted focused on supporting 300,000 PEVs by 2025 in Massachusetts. The report concludes with a discussion of potential methodology for estimating economic impacts of PEV infrastructure growth.
Author: Ranjit R. Desai
Publisher:
ISBN:
Category : Battery charging stations (Electric vehicles)
Languages : en
Pages : 86
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Book Description
"he advent of Electric Vehicles (EV) in the private transportation sector is viewed as a means of reducing emissions and making significant efforts towards reducing climate change impacts. However, when it comes to adopting and/or promoting a new technology through subsidies, the consumers’ needs are seldom given significant attention. Moreover, most analyses informing policy making assess the potential of new and cleaner technologies like EVs based on an average consumer’s needs and behavior. Given heterogeneity, these analyses miss subpopulations that benefit (or lose) more than an average consumer. In fact, private transportation greatly depends upon how the diversity of consumers choose to commute and what kind of vehicles they choose to possess. Especially in the United States of America (U.S.), each consumer faces different needs for their daily commute, which dictates their preferences for vehicles. This behavioral heterogeneity in addition to the geographic locations of consumers makes the U.S. private transportation sector an intricate system. The locations of the U.S. define fuel prices as well as emissions from electricity production. Therefore, these behavioral and geographic heterogeneities are highly crucial while calculating the benefits and potentials of EVs. The analyses conducted for this dissertation consider these heterogeneities to accommodate the nuances in consumers. This consideration of heterogeneities is the most critical aspect of this work. Chapter 2 of this dissertation builds a Marginal Abatement Cost Curve (MACC) for Electric Technology Vehicles (ETVs) which incorporates these heterogeneities, behavioral and geographical. With current gasoline and battery cell prices, result indicate that without federal tax credits, about 1.9% of the population would receive direct financial benefits from purchasing an ETV. This subpopulation drives over 4 times (over 48,000 miles annually) more than the average consumer (11,700 miles). The consideration of the heterogeneities has made it possible to recognize this subpopulation. The scenario analyses are conducted for different fuel and battery cell prices. These analyses shed light on how different subpopulations benefit financially and environmentally from ETVs. In this chapter, the impacts of federal tax credits with and without considering heterogeneities are estimated, suggesting why policy analyses need to incorporate consumer heterogeneities while assessing benefits of government subsidies. Given these results on economic and carbon benefits of ETVs, Chapter 3 builds an integrated model of adoption that includes endogenous technological progress—through learning rates—where due to initial adopters the technology is made cheaper for the future ones. The feedback loop developed in this chapter takes into consideration the cumulative production of the technology and estimates price reductions using learning rates. Reduced capital costs then propel more consumers to adopt ETVs making the technology cheaper, again increasing the consumer base that benefits from them. The economic benefits of buying an ETV versus a conventional one costs depend on battery costs, non-battery EV costs, and the future of conventional vehicles. Results are that the future market penetration (share of consumers economically benefitting) is sensitive to two poorly understood quantities: non-battery EV costs and cost increases in conventional vehicles driven by future emission standards. Federal tax credits are also studied in how they stimulate adoption and in turn technological progress of ETVs. Governments are not only investing in subsidies for consumer purchase of ETVs but also in installing public EV charging stations. These charging stations are expected to motivate consumers to choose ETVs over conventional vehicles and help reduce range-anxiety. In Chapter 4 an assessment is conducted to understand how these public resources are being used. Results reveal the behavior of consumers at the public EV charging stations using empirical data collected in the City of Rochester. A data distillation is first conducted for the raw data to construct the daily charging profiles of the EV users. A pattern analysis is then performed to identify 5 distinct and homogenous clusters of daily charging profiles of the consumers. This work defines the operational inefficiency of the public charging station as the time spent in parking without charging out of the total time a PEV user accessed the public charging station. This analysis uncovers a significant inefficient operation of these public EV charging stations, i.e. EVs remained parked at stations long after charging is finished. An estimation of the opportunity cost of reducing this observed inefficiency in terms of Greenhouse Gas emissions savings is also conducted in this chapter. The main policy takeaways of this dissertation are that identifying key subpopulations who benefit from the ETVs is highly significant and possible only by incorporating behavioral and geographical heterogeneities. This allows a more precise estimation of impacts of policies such as the federal tax credits. Secondly, the initial adopters make the technology cheaper for the latter adopters. However, the future market parity of ETVs with conventional vehicles depends on poorly understood factors such as current costs and learning rates of non-battery EV technologies and future cost increases in conventional vehicles driven by stricter emissions requirements. Lastly, the use of public resources, such as public charging stations needs to be studied. They are expensive to create, and inefficient use may deter possible EV adopters. Furthermore, the possible opportunity cost of reducing emissions by using the charging station more efficiently allows better use of a public resource."--Abstract.
Author: Claudia Peter
Publisher: GRIN Verlag
ISBN: 334658299X
Category : Business & Economics
Languages : en
Pages : 28
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Academic Paper from the year 2021 in the subject Economy - Environment economics, grade: 1,3, The FOM University of Applied Sciences, Hamburg, language: English, abstract: The objective of this assignment is to research charging infrastructure requirements, that potentially increase the demand for electric vehicles. The scope of this work will be limited to the German market, except for the overview of the e-Mobility market development. To examine the objective a secondary research of current literature, statistical reports, media and regulations will be performed. First of all, the author will present theoretical foundations to better understand the subsequent chapters, following by an overview of the global e-Mobility market development. Hereafter, the components of charging infrastructure are examined and the current challenges regarding market penetration, following an in-depth research of the charging infrastructure requirements to increase the demand for electric vehicles. Finally, the author will summarize her conclusion and outlook. Climate change, new technologies and less dependence on fossil fuels are major drivers for the development of electric mobility. Electric mobility produces much less CO2, especially when operated using renewable based electricity and therefore seen as key element of the energy transition towards a CO2 neutral environment. In addition, the batteries of electric vehicles (EV) can be used as energy storage to offset fluctuations in solar and wind power. Thus, electric vehicles foster the market integration and expansion of these volatile energy sources. To support and promote research and development of electric vehicles the Federal Government has adopted a set of measures, e.g., the extension of charging infrastructure. The German Government set ambitious goals for the German charging infrastructure. In order to reach this goal, customers must be convinced that an EV is better than the conventional motor type cars. Slowly, but electric vehicles become more visible nowadays. The major current challenges still pose the range of batteries and the charging management. Through digitalization of traffic systems and the increasing automation of mobility in form of autonomous driving cars, the change will be further accelerated.
Author: David Greene
Publisher:
ISBN:
Category : Battery charging stations (Electric vehicles)
Languages : en
Pages : 10
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Author: Akik Bharat Patel
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Book Description
In 2011, the U.S. Presidential Administration set the goal of having a million electric vehicles in the U.S. by 2015. In order to support these goals, the U.S federal government introduced several incentive programs (includes purchasing tax credits) and policies (installing public charging stations) to encourage EV adoption and ease dependence on gasoline consumption. Since the introduction of these policies and mass-marketing of EVs in 2010-11, the sale of commercial electric vehicles in the U.S between 2011 and 2015 has been more than 300,000. However, EVs accounts for less than 1 percent of total light-duty vehicles sales. One of the reasons for the low adoption rate for EV is “range anxiety”. This is created among consumers due to lack of publicly available charging infrastructure and this prohibits users to travel between and within cities. Thus, in order to promote EVs as a primary vehicle for drivers, more charging stations should be made available to the public. The main objective of this research is to identify suitable locations for installing public Electric Vehicle charging stations in the City of Austin. At present, Austin Energy doesn’t have any standard method to identify demand for public charging stations and locate them appropriately to optimize its usage. In order to determine land parcel suitable for installing public charging stations, a set of geo-spatial data were identified from an extensive review of existing literature and similar studies conducted across the globe. These data sets were then edited to form individual raster layers. Each raster data is further classified by assigning scores to each raster value (within a raster layer) based on simple logic. For example, a higher score will be assigned to a raster cell which is closer to a Food establishment and a lower score as we move further away. The higher score basically defines a higher suitability of installing a charging station and vice versa. Further, a map indicating the optimal parcels in the city for installing EV charging infrastructure is created using map algebra which is based on assigning different weighting factors to each raster layer.
Author: Yanbo Ge
Publisher:
ISBN:
Category :
Languages : en
Pages : 185
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Plug-in Electric Vehicles (PEVs) have the potential of reducing gasoline consumption and greenhouse gas emissions in the transportation sector. The net impacts of PEVs – including upstream emissions from electricity generation and the impact these vehicles place on the electricity grid – depend on both the amount of travel conducted by PEV and locations that those PEVs are charged. This dissertation investigates the vehicle use choices and charging decisions of both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) for both home-based trip tours and long-distance trips using stated preference (SP) data. It presents a novel dynamic discrete choice modeling (DDCM) framework that explicitly accounts for the stochastic nature of the vehicle choice and charging decisions of PEV users: earlier choices on vehicle use and charging influence the utility of the future choices; the expectation of the future options influences those earlier decisions; and choices are made under uncertainty about actual energy consumption and availability of chargers. For home-based trip tours, my results show that BEV users are willing to pay $10-$24 to avoid having to deviate from the originally planned route, which indicates that “range anxiety” of BEV owners – the fear of being stranded in the middle of a trip – is not a crucial issue for home-based trips. Using charging infrastructure development to encourage BEV adoption might be more beneficial than reducing “range anxiety” among the current users, which could entail building charging stations at locations that have more public exposure, such as public parking garages in a city center. When BEVs are on long-distance trips, the cost of deviation is significantly higher: $244, which indicates that BEV owners are likely to be more cautious and view finding a charger off the route much more costly when they are on long-distance trips. Comparing the cost of deviation for home-based tours and long-distance trips, to support the existing users, the most cost-effective places to invest in charging infrastructure are inter-city corridors instead of in-city locations. By comparing the relative size of the coefficient estimates, in this dissertation, I also analyze the monetary value of increasing charging power, moving the charging stations closer to highway exits, and having amenities such as restrooms, restaurants, and Wi-Fi near the charging stations. The comparison between the DDCMs and SDCMs based on simpler decision heuristics shows that for home-based tours, DDCMs only offer a little better prediction rate with a significant cost when it comes to computation time and complexity of model development. For the purpose of demand forecasting of a charging network or site selection for the charging facilities, the SDCMs based on simpler heuristics are recommended for home-based trip tours. For long-distance trips, the charging choices are largely decided by the state of charge (SOC) and deviation, and the characteristics of the charging stations only contribute to a small portion of predictive power. SDCMs outperform the DDCMs for the current sample. However, this could change in the future when the charging network is dense and the characteristics of the charging stations have higher prediction power. For both the home-based tours and long-distance trips, and for both vehicle choices and charging decisions, the decision patterns are likely to be heterogeneous among the PEV owners. The efforts related to the prediction of the future EV charging demand, the policy-making on battery and charging infrastructure development, and the planning/design of the charging network all need to consider these different preferences of the consumers. Due to the heterogeneity of users’ preferences, both increasing battery pack size and reducing station spacing can encourage current BEV owners to use their BEVs for long-distance trips, and one of the two does not substitute the other. Even if a lot of the BEV models offered by the market have 500 miles of range, the density of the public charging network can still play an important role in enabling BEVs for long-distance trips, especially when the battery remains expensive.
Author: Parasto Jabbari
Publisher:
ISBN:
Category :
Languages : en
Pages : 48
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This thesis seeks to improve understanding of the role of market scale in electric vehicle (EV) adoption, by exploring consumer and infrastructure perspectives. First, we use new vehicle post-purchase consumer satisfaction survey to explore the reasons for low EV adoption. We investigate consumers’ level of satisfaction and reasons for rejecting a vehicle using matching method and statistical tests. Results show that plug-in electric vehicle (PEV) purchasers and considerers are less satisfied with their overall purchase experience compared to internal combustion engine (ICE) vehicle purchasers and considerers, but PEV considerers are less likely than ICE considerers to cite the dealer’s attitude as a reason for rejection. Price and value are the most cited reasons and were similarly important for both groups. Reasons related to model availability and vehicle attributes are more often a concern for PEV considerers than ICE considerers. These results suggest that even with existing incentives, the limitations of the current technology, mainly price and range, and variety of available vehicles, are the most important challenges for EV adoption. However, market growth has the potential to resolve most of these barriers. Since range anxiety is still a major barrier for EV adoption, even for those who already are considering purchasing EVs, we take another step to understand impact of market scale on charging infrastructure reliability, utilization and cost effectiveness. We build a queue model informed by the characteristics (e.g. charging rates, battery size, range) of current battery electric vehicles (BEVs) and available DC fast chargers. We use the model to determine how we can expect costs, utilization and availability of chargers to change with respect to each other and find out what the costs are for maintaining satisfactory availability for users. The model shows that for a charging station with few chargers, it is difficult to achieve cost-effective levels of utilization while maintaining reliable access for arriving vehicles. Large numbers of chargers per station make it possible to maintain a high reliability of access for users and a high utilization rate. Also, as the number of EVs on the road increases, the business case for DC fast chargers becomes more attractive.
Author: Yash Babar
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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We study how the placement of electric vehicle (EV) charging stations impacts foot traffic at neighboring brick-and-mortar businesses. Our analysis focuses on the Tesla Supercharger network within the United States. We employ a differences-in-differences design, exploiting the staggered construction of Supercharger stations to quantify the effect. We implement a variety of robustness checks, including alternative estimators and matching techniques. Further, we document heterogeneity in the impact, based on business and Supercharger characteristics, as well as other contextual factors. We estimate that Superchargers yield a 4% increase in average monthly visits to nearby businesses. These effects are primarily attributable to higher-income customers and weekend visits, consistent with the typical Tesla / EV customer profile, and charge pattern. We also find that the effects accrue most heavily to grocery and convenience stores, rather than restaurants. Our study provides novel, robust evidence of the demand spillovers that offline retailers can obtain from EV charging infrastructure. We draw insights and implications for EV networks, retailers, and policymakers around efforts to expand EV charging infrastructure and consumer adoption of EVs.
