Adoption Rate Of Electric Vehicles Research Articles

Influence of Increasing Electrification of Passenger Vehicle Fleet on Carbon Dioxide Emissions in Finland

Different estimations have been presented for the amount of electric vehicles in the future. These estimations rarely take into account any realistic dynamics of the vehicle fleet. The objective of this paper is to analyze recently presented future scenarios about the passenger vehicle fleet estimations and create a foundation for the development of a fleet estimation model for passenger cars dedicated to the Finnish vehicle market conditions. The specific conditions of the Finnish light-duty vehicle fleet are taken into account as boundary conditions for the model development. The fleet model can be used for the estimation of emissions-optimal future vehicle fleets and the evaluation of the carbon dioxide emissions of transportation. The emission analysis was done for four different scenarios of the passenger vehicle fleet development in Finland. The results show that the high average age of the fleet and high number of older gasoline vehicles will slow down the reduction of carbon dioxide emissions during the next five to ten years even with a high adoption rate of electric vehicles. It can be concluded that lowering the average age, increasing biofuel mixing ratios, and increasing the amount of rechargeable electric vehicles are the most effective measures to reduce carbon dioxide emissions of the Finnish passenger vehicle fleet in the future.

Analysis of the impact of policies intervention on electric vehicles adoption considering information transmission—based on consumer network model

Electric vehicles (EVs), which can relieve the pressure of energy and environment and revitalize the automotive industry, are paid great attention by the government. Existing research on the dynamic adoption process of EVs from the perspective of consumers based on complex social networks is insufficient. Based on the consumer network with dual attributes, i.e., positive/negative attitude and whether or not to adopt, this paper explores the impact of economic and information policies on EVs adoption rate by establishing a decision-making model of EVs adoption for consumers considering the effects of friend attitudes and power consumption information. The results show that: (1) The joint implementation of economic policies has a prominent effect on the adoption rate of EVs that is least affected by the subsidy cut policy when charging at home. (2) Enhancing popular science propaganda promotes the EVs adoption rate by increasing the initial proportion of positive attitudes in the consumer network. However, the promotion effect will be weakened if it exceeds a certain threshold. (3) Encouraging social discussion by expanding the number of friends has two sides to the EVs adoption rate. Finally, policymakers should consider the impact of consumers with different risk preferences on EVs adoption.

Coordinated Management and Ratio Assessment of Electric Vehicle Charging Facilities

With the rapid increase in electric vehicle adoption rate, both domains of power grid and transport system are facing operational challenges in the coordination and assessment of electric vehicle charging facilities. These facilities include not just public charging stations but also swapping stations and home charging stations that perform roles catering to different driver behaviors. If provided with sustainable strategies, these facilities can act as aggregators to help manage sudden peak loads. These different facilities will someday co-exist in the future, and it is crucial to plan and assess their interactions. Currently, there is limited research in exploring the coordination and ratio assessment of multiple charging facilities. Moreover, a model that addresses the complexity of charging facility decisions and the communication between multiple stakeholders still needs further investigated. The main stakeholders should not be limited to the station owners but should also include electric vehicle drivers and distributing company operators. This article proposes a layered decomposition model that considers decisions made by the main stakeholders in coordinating power transfers and assessing charging facility ratios. The Australian case studies for charging facility ratios in different renewable energy mix environments demonstrate the suitability of the proposed strategy.

Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution System Using Teaching Learning Based Optimization

In recent times, the adoption rate of Electric Vehicles (EVs) in the transportation sector has been increased significantly across the world towards sustainability. On the other side, the increasing EV load penetration in the electric power sector can cause for the generation-demand imbalance, real power loss increment, weak voltage profile and consequently voltage stability margin decrement. It is essential to integrate EV Charging Stations (CSs) at appropriate locations to mitigate the impact of increasing EV load penetration on radial distribution systems (RDS). In this paper, the teaching-learning based optimization (TLBO) algorithm is applied to determine the optimal locations of EV-CSs considering multiple objectives, i.e., real power loss, average voltage deviation index and voltage stability index. The simulation studies are performed on standard IEEE 33-bus and 69-bus test systems. The results have highlighted the need for optimal allocation of EV-CSs for maintaining the system performance as better as possible even under increased loading conditions due to EV-CSs. Also, TLBO has shown its ability over other heuristic algorithms namely particle swarm optimization (PSO), ant lion Optimizer (ALO), flower pollination algorithm (FPA) and cuckoo search algorithm (CSA) by providing the optimal value consistently in solving the complex non-linear multi-objective optimization problem.

Augmentation of charging infrastructure for electric vehicles growth in India

India, one of the fastest growing economies, has been a front-runner in fulfilling its commitments towards a cleaner Earth. In this pursuit, it intends to adopt electric vehicles (EVs) to the tune of 30% by 2030. Central and state governments of India have launched several financial incentives to encourage adoption of EVs. While considering various adoption rates of EVs from 5% to 30%, the requirements of additional amounts of electricity and various types of charging stations to feed power to EVs are estimated. At 30% adoption rate, about 140 million EVs (include different categories of vehicles studied in this paper) may become the part of transportation system and correspondingly need about 24,600 MWh of electricity daily. In the high density areas of EVs, such as bus depots, residential complexes, malls etc., large power may be needed at a single point. Thus, it may require sufficient receiving substation capacity and an equivalent high-voltage distribution network.

Can product bundling increase the joint adoption of electric vehicles, solar panels and battery storage? Explorative evidence from a choice-based conjoint study in Austria

Although electric vehicle (EV) sales have recently been increasing, EVs can only contribute to mitigating climate change if the power they require is generated from renewable energy sources. Hence, a product bundle of EVs with photovoltaic (PV) solar panels in combination with battery storage (BS) for households could be instrumental in improving EV adoption rates and thus also their carbon footprint. We conducted a choice-based conjoint experiment with 393 respondents in Austria to investigate the effect of EV-PV-BS product bundles on purchase intention. Our data show that a majority of potential EV drivers, given the choice, would prefer to purchase an EV in such a bundle. Further, the purchase intention for a PV and BS is twice as high in a bundle with an EV than standalone. Segmentation analysis identified four potential customer segments, which we labelled “Price-Sensitive Non-Owners”, “Energy Self-Sufficient Owners”, “Economically Rational Owners” and “Likely Non-Adopters”. The segments specifically differ in their product preferences, which highlights a need for designing customized bundle offerings. Moreover, we show that policy incentives are more effective when product bundles are labelled with price tags already discounted by subsidies. We draw implications for practitioners and policymakers, as well as proposing areas of further research.

A stochastic programming approach for electric vehicle charging station expansion plans

The projected and current adoption rates of electric vehicles are increasing. Electric vehicles need to be recharged continually over time, and the energy required to ensure that is immense and growing. Given that existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. In this paper, we present a two-stage stochastic programming approach that can be used to determine a power grid expansion plan that supports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. The first stage determines where to expand the power grid, and the second stage determines where to locate charging stations. The key link between the first and second stage decisions is that charging stations can only be located in areas with sufficient power supply enabled by an expanded power grid. To solve the model, we utilize a hybrid approach that combines Sample Average Approximation and an enhanced Progressive Hedging algorithm. We enhance the Progressive hedging algorithm by applying rolling horizon and variable fixing techniques. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.

On the road to electrification – A qualitative comparative analysis of urban e-mobility policies in 15 European cities

The adoption rate of Electric Vehicles (EV's) is influenced by a considerable number of factors on both local and national levels. Most of the research aimed at understanding under what conditions that adoption thrives focuses on either level. There is also relatively little attention for the configurational nature of its uptake. We present a Qualitative Comparative Analysis (QCA) of e-mobility policies in 15 European cities in order to identify policy configurations on the national and local, urban level that lead to favorable results, i.e. the successful promotion and consequent adoption of EV's.The results confirm the configurational characteristic of e-mobility policies. We identified in particular one configuration that is sufficient for the favorable outcome to occur: policies that have a stronger effect on the total cost of ownership of EV's in conjunction with incentivizing the installation of home chargers or charging points on private parking space in addition to the setting-up of a public charging grid in conjunction with push-factors that de-incentivize the usage of conventional cars lead to favorable results. Thus, our findings lead the focus on policy approaches that treat EV adoption in a holistic and dynamic way by properly linking incentives with de-incentives.

Vehicle grid integration for demand response with mixture user model and decentralized optimization

With the rapidly growing electric vehicle adoption rate and increasing number of public electric vehicle charging stations in recent years, electric vehicle becomes more and more critical in the demand response programs. Development in Vehicle to Grid technology has converted electric vehicle to distributed energy resources. Using the Electric Vehicle Smart Charging Infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of electric vehicle usage, based on which, we proposed optimal bi-directional charging control strategies to integrate electric vehicle in commercial and public parking facilities into the power grid as distributed energy resources for demand response programs by two-stage distributed optimization and water-filling algorithm. Driver behavioral uncertainties have been considered in our approach. Specifically, electric vehicle users are clustered by their behavioral patterns using a modified Latent Semantic Analysis. The first-stage optimization is performed to minimize energy cost using day-ahead wholesale energy price with predictions on energy demand and electric vehicle availability which generated by a mixture user model. Decentralized optimization (second-stage) is carried out on the next day in real-time to control individual electric vehicle so that the aggregated load can follow the first-stage optimal profile. As an alternative, a fast converging water filling algorithm is proposed and compared with two-stage optimization. Extensive simulation results show that proposed charging controls can utilize electric vehicle as distributed energy resource to accommodate demand response program while satisfying electric vehicle energy demands and providing significant energy cost savings.

Electric vehicle adoption in Sweden and the impact of local policy instruments

A transition towards a higher share of electric vehicles has the potential to significantly reduce greenhouse gas emissions. The adoption rate of electric vehicles in Sweden is however relatively slow and varies substantially across municipalities. This study empirically examines the impact of local policy instruments designed to promote the adoption of electric vehicles. We use panel data between 2010 and 2016 to estimate the effect of local policy instruments on the share of newly registered battery electric vehicles in Swedish municipalities. We find that an increased number of public charging points increases the adoption rate, especially in urban municipalities. The results further suggest that public procurement of battery electric vehicles has the potential to be an effective policy instrument. Finally, we find that by adjusting policy instruments to the specific characteristics of municipalities and making them visible to the public, their effectiveness can be increased.

Analysis of the Electric Vehicles Adoption over the United States

Abstract: Increasing the use of electric vehicles (EVs) has been suggested as a possible method to decrease fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In this study, the relationship between the market share of electric vehicles and the presence of government incentives, and other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The results demonstrated that electricity prices were negatively associated with EV use while urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with theories about diffusion of new technology.

Investigating factors affecting electric vehicles adoption: an aggregated panel data analysis over U.S. states

Increasing the usage of electric vehicles has been proposed as a policy to decrease aggregate fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In order to increase the attraction of electric vehicles for consumers, governments have employed a number of incentives. In this study, the relationship between shares of electric vehicle and the presence of government incentives as well as other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The model was estimated using different panel data methods and the results were compared. The results demonstrated that electricity prices were negatively associated with EV use while, urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. According to the sensitivity analysis of electric vehicle adoption rate, state of Vermont has the most sensitivity with respect to electricity price and New Jersey is the most sensitive state with respect to urban roads and incentives. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with diffusion of new technology theory.

Electric vehicle charging to support renewable energy integration in a capacity constrained electricity grid

Digby, Nova Scotia, is a largely rural area with a wealth of renewable energy resources, principally wind and tidal. Digby’s electrical load is serviced by an aging 69kV transmission line that often operates at the export capacity limit because of a local wind energy converter (WEC) field. This study examines the potential of smart charging of electric vehicles (EVs) to achieve two objectives: (1) add load so as to increase export capacity; (2) charge EVs using renewable energy.Multiple survey instruments were used to determine transportation energy needs and travel timing. These were used to create EV charging load timeseries based on “convenience”, “time-of-day”, and idealized “smart” charging. These charging scenarios were evaluated in combination with high resolution data of generation at the wind field, electrical flow through the transmission system, and electricity load.With a 10% adoption rate of EVs, time-of-day charging increased local renewable energy usage by 20% and enables marginal WEC upgrading. Smart charging increases charging by local renewable energy by 73%. More significantly, it adds 3MW of load when power exports face constraints, allowing enough additional renewable electricity generation capacity to fully power those vehicles.

Hybrid Electric Vehicle Ownership and Fuel Economy across Texas

Policymakers, transport planners, automobile manufacturers, and others are interested in the factors that affect adoption rates of electric vehicles and other fuel-efficient vehicles. With tract-level data from the 2010 census and registered vehicle counts from Texas counties in 2010, this study investigated the impact of various built environment and demographic attributes, including land use balance, employment density, population density, median age, gender, race, education, household size, and income. Spatial autocorrelation (across census tracts) in unobserved components of vehicle counts by tract and cross-response correlation (both spatial and local–aspatial in nature) was allowed for by the estimation of models of ownership levels (vehicle counts by vehicle type and fuel economy level) with bivariate and trivariate Poisson–lognormal conditional autoregressive models. The presence of high spatial autocorrelations and local cross-response correlations was consistent in all models across all counties studied. Ownership rates for fuel-efficient vehicles were found to rise with household income, resident education levels, and the share of male residents and to fall in the presence of larger household sizes and higher job densities. The average fuel economy of each tract's light-duty vehicles was also analyzed with a spatial error model across all Texas tracts, and this variable was found to depend most on educational attainment levels, median age, income, and household size variables, though all covariates used were statistically significant. If households registering more fuel-efficient vehicles, including hybrid electric vehicles, are also more inclined to purchase plug-in electric vehicles, these findings can assist in spatial planning of charging infrastructure as well as other calculations (such as implications for the revenue from gas tax).

The influence of financial incentives and other socio-economic factors on electric vehicle adoption

Electric vehicles represent an innovation with the potential to lower greenhouse gas emissions and help mitigate the causes of climate change. However, externalities including the appropriability of knowledge and pollution abatement result in societal/economic benefits that are not incorporated in electric vehicle prices. In order to address resulting market failures, governments have employed a number of policies. We seek to determine the relationship of one such policy instrument (consumer financial incentives) to electric vehicle adoption. Based on existing literature, we identified several additional socio-economic factors that are expected to be influential in determining electric vehicle adoption rates. Using multiple linear regression analysis, we examined the relationship between those variables and 30 national electric vehicle market shares for the year 2012. The model found financial incentives, charging infrastructure, and local presence of production facilities to be significant and positively correlated to a country׳s electric vehicle market share. Results suggest that of those factors, charging infrastructure was most strongly related to electric vehicle adoption. However, descriptive analysis suggests that neither financial incentives nor charging infrastructure ensure high electric vehicle adoption rates.

Locating Electric Vehicle Charging Stations

Access to electric vehicle (EV) charging stations will affect EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. Parking information from more than 30,000 records of personal trips in the Puget Sound, Washington, Regional Council's 2006 Household Activity Survey is used to determine public (nonresidential) parking locations and durations. Regression equations predict parking demand variables (i.e., total vehicle hours per zone or neighborhood and parked time per vehicle trip) as a function of site accessibility, local job and population density, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs to a mixed-integer programming problem developed to determine optimal location assignments for EV charging stations. The algorithm minimizes EV users' costs for station access while penalizing unmet demand. This useful specification is used to determine the top locations for installing a constrained number of charging stations within 10 mi of the downtown area of Seattle, Washington, and shows how the access costs of charging location schemes respond to parking demand and station location. The models developed here are generalizable to data sets available for almost any region and can be used to make more informed decisions about station locations around the world.

Infrastructure Planning for Electric Vehicles with Battery Swapping

The transportation sector is a major source of greenhouse gas (GHG) emissions. As a step toward a greener environment, solutions involving electric vehicles (EVs) have been proposed and discussed. When powered by electricity from efficient and environmentally-friendly generators, EVs have significantly lower per-mile running costs compared to gasoline cars, while generating lower emissions. Unfortunately, due to the limited capacity of batteries, typical EVs can only travel for about 100 miles on a single charge. Because recharging takes several hours, it is impossible to recharge an EV in the middle of a long (round) trip exceeding 100 miles. Better Place (BP), a start-up based in Palo Alto, CA, proposed a novel strategy that potentially overcomes the recharging problem. In the plan, in addition to charging adaptors at homes, work places and shopping malls, stations'', at which depleted batteries can be exchanged for recharged ones in the middle of long trips, will be located at strategic locations along freeways. With its battery swapping equipment, BP has demonstrated how to effectively refuel an EV in less than two minutes. The possible success of EV solutions based on the idea of battery swapping hinges on the ability of the charging service provider (BP or other similar firms) to deploy a cost-effective infrastructure network with comprehensive coverage. Unfortunately, since the adoption rate of electric vehicles, and thus demand for swapping service, is still highly uncertain, the service provider must make deployment plans with incomplete information on hand. In this paper, we develop models that aid the planning process for deploying battery swapping infrastructure, based on a robust optimization framework. We further show that our models can be tightly approximated by mixed-integer second-order cone programs (MISOCPs), which are readily solvable by commercial solvers. Using these models, we demonstrate the potential impacts of battery standardization and various technology advancements on the optimal infrastructure deployment strategy.

Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

Vehicle-to-grid (V2G) has been proposed as a way to increase the adoption rate of electric vehicles (EVs). Unidirectional V2G is especially attractive because it requires little if any additional infrastructure other than communication between the EV and an aggregator. The aggregator in turn combines the capacity of many EVs to bid into energy markets. In this work an algorithm for unidirectional regulation is developed for use by an aggregator. Several smart charging algorithms are used to set the point about which the rate of charge varies while performing regulation. An aggregator profit maximization algorithm is formulated with optional system load and price constraints analogous to the smart charging algorithms. Simulations on a hypothetical group of 10 000 commuter EVs in the Pacific Northwest verify that the optimal algorithms increase aggregator profits while reducing system load impacts and customer costs.