Consumption Of Electric Vehicle Research Articles

Strategically Targeting Plug-In Electric Vehicle Rebates and Outreach Using “EV Convert” Characteristics

To expand markets for plug-in electric vehicles (EVs) beyond enthusiastic early adopters, investments must be strategic. This research characterizes a segment of EV adoption that points the way toward the mainstream: EV consumers with low or no initial interest in EVs, or “EV Converts.” Logistic regression is utilized to profile EV Convert demographic, household, and regional characteristics; vehicle-transaction details; and purchase motivations—based on 2016–2017 survey data characterizing 5447 rebated California EV consumers. Explanatory factors are rank-ordered—separately for battery EVs (BEVs) and plug-in hybrid EVs (PHEVs), to inform targeted outreach and incentive design. EV Converts tend to have relatively “lower” values on factors that might have otherwise “pre-converted” them to EV interest: hours researching EVs online; motivation from environmental impacts and carpool-lane access; and solar ownership. PHEV Converts more closely resemble new-car buyers than other EV adopters, and BEV Converts actually tend to be younger and less-frequently white/Caucasian than new-car buyers. BEV Converts also tend to: lack workplace charging, be moderately motivated by energy independence, and reside in Southern California or the Central Valley. Predictors that not only help target consumers, but also help convert them, include rebates for BEV consumers and, modestly, fuel-cost savings for PHEV consumers.

Superhydrophobic heat exchangers delay frost formation and enhance efficency of electric vehicle heat pumps

The number of electric vehicles has rapidly expanded as high-performance automotive lithium-ion batteries have become more affordable. However, the range of electric vehicles decreases in cold climates partly because of the additional thermal loads associated with cabin heating. One way to improve the efficiency of cabin heating is to replace resistive heating elements with an air-source heat pump system. However, to gain the full benefit of heat pumping, frost formation on the outdoor heat exchanger must be minimized. In this work, we modified the surface wettability of aluminum louvered-fin automotive heat pump evaporators and tested them under realistic operating conditions in a transcritical carbon dioxide (CO2) heat pump. Each heat exchanger underwent several consecutive frosting and defrosting cycles to understand the cyclic performance of the system. The heat exchanger with a superhydrophobic outer surface was able to delay frost formation and maintain higher heat transfer rates when compared to heat exchangers with higher surface energies (hydrophilic). The delayed frost formation resulted in a system efficiency benefit in the first few frosting cycles but diminished in later cycles due to water retention and incomplete defrosting. However, for most automotive applications the superhydrophobic heat exchanger showed substantial benefits for normal driving trips. The scalable and optimized superhydrophobic heat exchangers developed here have the potential to increase the efficiency of automotive heat pumps and consequently increase the range and reduce energy consumption of electric vehicles.

Planning of Charging Facilities Considering Terrain Characteristics

The existing studies on the planning of charging facilities generally neglect the influence of terrain characteristics. First, this study quantifies the transportation characteristics based on the relative height to calculate the travel energy consumption of electric vehicles (EVs) users. Then, the charging demand of EVs is estimated and assigned according to urban traffic flow. Finally, a land purchasing coefficient is proposed based on a functional agglomeration phenomenon. The coefficient is integrated into formulating a planning model with the objective of minimising the total annual social costs. Numerical experiments are conducted to validate the novel planning method. The experimental results verify that the terrain characteristics increase users’ travel energy consumption and limit their driving range.

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

SummaryThe transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91% (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to US$27.6 billion by 2027.

Energy Consumption, Pollutant Emissions and Cost of Electric Vehicles and Fuel Vehicles

With the depletion of the earth’s energy and the deterioration of the environment, since the United Nations issued the Sustainable Development Goals, all countries have begun research and policies aimed at energy conservation and emission reduction. In the fields of transportation and automobiles, electric vehicles have great potential as an envisioned alternative to conventional fuel vehicles. This article first describes the relevant knowledge about the GREET model and the WTW evaluation system and then discusses and evaluates the differences in energy consumption, pollution emissions and personal use costs between electric vehicles and conventional fuel vehicles. The research results show that the energy consumption of electric vehicles is 11 % lower than that of conventional fuel vehicles. Pollutant emissions of electric vehicles are significantly lower than that of fuel vehicles and their emissions are mainly concentrated in the power generation stage and the overall price of electric vehicles is lower, and the proportion of fuel consumption is smaller than that of conventional fuel vehicles. This article provides a data basis for judging of electric vehicles advantages and development prospects and contribute to reducing social energy consumption and improving environmental degradation.

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient ( ${\text C}_{d}$ ) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead.

Influence Factors and Correction coefficient for Energy Consumption of Electric Vehicles

With the application of electric vehicles, the problem of energy consumption has been widely concerned. Fixed operating conditions energy consumption in the laboratory environment was used for vehicle identification, which made big difference from the actual situation. The paper was based on the actual operation data of the vehicles. The influence factors of energy consumption have been put forward through theoretical analysis. The correction factor was calculated by big data analysis and statistical fitting. The actual energy consumption of electric vehicles can be more accurately estimated through the research results.

Integration of a Pilot PV Parking Lot and an Electric Vehicle in a University Campus Located in Curitiba: a Study Case

HIGHLIGHTS Increasing electric vehicles against utility grid energy supplying capacity. Solar Carport system as a solution to provide energy for EV recharging needs. Performance analysis of a real solar carport energy production. Comparison between energy production and electric vehicle consumption for a day use.

Pricing strategy and coordination of automobile manufacturers based on government intervention and carbon emission reduction

The game model of the duopoly automobile manufacturers established in this paper takes the carbon emission reduction policy constraint as the research background, and discusses how the electric vehicle and the fuel vehicle compete in the performance of the product in the delay pricing decision under the strategic consumer, which is seldom considered in other related studies. The government gives electric vehicle consumers preferential policies on consumption subsidies and exemption from consumption tax. Additionally, he levies consumption tax and sales carbon tax from fuel vehicle consumers and manufacturers, respectively. The government also supports the development of electric vehicles by establishing charging piles. This paper cast light on how different market structures and different adjusting speed of price work on the vehicle manufacturers' operation and the system's stability. At first, we consider a single period game to derive the optimal solutions, finding that government intervention policies can maximize the social welfare. Additionally, the impacts of tax and subsidy on social welfare are different when fuel vehicle manufacturer is leader. Then, we consider a repeated game where players make decision based on bounded rationality. We compare the optimal solutions and give a numerical simulation for the dynamic process. For fuel vehicle manufacturer, too much energy consumption triggers high emission tax and also lowers consumers' surplus. We also find that fuel vehicle manufacturer acting as the leader enables the system to be more stable than the scenario in the current vehicle industry condition, where the electric vehicle manufacturer acts as the leader.

Exploring the factors influencing electric vehicle adoption: an empirical investigation in the emerging economy context of India

Purpose The Indian automotive industry was witnessing a transition from conventional vehicles to greener battery-operated electric vehicles (EVs). However, the acceptance of these EVs was still muted and brought significant challenges for the industry. Literature regarding the adoption of EVs was scarce in the Indian context. It was thus imperative to explore and comprehend the distinct perceptions of industry managers and consumers regarding the adoption of EVs in India. The purpose of this study is to comprehensively analyze the entire Indian EVs ecosystem to address this research gap. Design/methodology/approach The authors carried out an empirical investigation starting with a structured literature review to identify the researchable gaps. Subsequently, the authors conducted semi-structured open-ended interviews with 38 experts including automotive industry experts and EV consumers. The authors further performed a thematic content analysis of the expert interview responses to document critical insights regarding the adoption of EVs. Findings The authors identified 11 key factors influencing the adoption of EVs in this study. The vital considerations regarding the availability of charging technologies, its associated selection dilemma, emerging business models and public policy support were presented and discussed. Market penetration of EVs was found to be influenced mostly by the choice of charging technology. Further, the switching intention of consumers was deliberated upon to highlight the specific technological and psychological preferences of consumers. The accessibility of charging stations emerged as the most influential factor. The research findings indicated that harmony among stakeholders was missing in the Indian EVs ecosystem. Instead, there were discrete efforts by organizations. The EVs ecosystem required collaboration for improved adoption of the EVs. Further, the necessity to rectify the chaotic charging infrastructure in the country was highlighted as a major pain point for customers to adopt EV. Research limitations/implications This study theoretically contributed to push–pull–mooring (PPM) framework for understanding the adoption of EVs in India. This enabled the authors to extensively analyze consumers’ psychological and technological considerations regarding their switching intention toward EVs. Practical implications The findings of this study would help managers in decision-making toward the establishment of charging infrastructure involving multiple considerations such as the accessibility of charging, multi-dimensional competence at charging stations and servicing capabilities. Managers could also use the insights from this study to secure supportive recommendations for improving the overall EV infrastructure. The results of this study would benefit policymakers to set strategic directions through an integrated view of the entire EVs ecosystem involving management of bus and taxi fleets, two-wheelers and three-wheelers and such others. Originality/value Generally, in extant research, either firm managers’ or customers’ perspectives are considered separately. This study deliberated upon the PPM framework and switching intention accommodating both the industry and consumers’ perspectives. To the best of the authors’ knowledge, this was, thus, one of the first research articles which integrated insights from both the industry and consumers. This established the PPM framework for understanding the adoption of EVs. Further, it helped in comprehending the specific technological and psychological preferences of consumers regarding switching intention toward EVs.

Predictive model for energy consumption of battery electric vehicle with consideration of self-uncertainty route factors

Greenhouse gas emissions caused by urban road mobility have reached unsustainable levels, being partly responsible for both, the current global warming alarming situation and the pollutant emissions risk to the health of citizens. Transport electrification represents an encouraging solution to this problem. The development and improvement of electric vehicle technology combined with transportation management strategies have become relevant research topics, not only in the area of mobility of people, but also to large companies whose supply chain may involve hundreds of commercial vehicles. The costs from running these vehicles represent a significant impact on their finance and controlling fuel usage is often a key contributor to keep running costs under control across their fleet and supply chain. In this context, traffic regulation elements evaluation, considering its uncertainty, could represent a significant influence on the vehicle consumption and, consequently, on its indirect greenhouse gas emissions. For this reason, traffic regulation system analysis may represent a step forward for developing solutions that collaborate with both, pollution and global warming, abatement. The present study proposes a predictive methodology for determining battery electric vehicle consumption variations when modifying traffic regulation elements. It uses stochastic speed profiles for neutralizing human intervention in consumption and multiple linear regressions to predict the energy consumed by the electric vehicle as a function of a set of factors that represent the traffic regulations. The research does not aim to provide particular energy consumption data, but to expose the variations in consumption and emissions caused by urban planning modifications. Model accuracy and achieved conclusions are illustrated through the development of a case study. The methodology could be a help for urban planners, fleet managers, logistics and supply chain decision makers environmentally concerned.

Research on the Development and Application of Charging Piles Based on the Development of New Energy Vehicles

In this paper, based on the cloud computing platform, the reasonable design of the electric vehicle charging pile can not only effectively solve various problems in the process of electric vehicle charging, but also enable the electric vehicle users to participate in the power management. It makes the charging and power consumption of electric vehicles more scientific and reasonable, realizes the communication and interaction between users and power grid, meets the charging requirements of electric vehicles, improves the overall service quality, and contributes to the comprehensive promotion of the technology.

A Novel Methodology for Evaluating the Impact of Energy Efficiency Measures on the Cabin Thermal Comfort of Electric Vehicles

Climate control systems have a largely negative effect on the energy consumption of electric vehicles and consequently on their real driving range. Improving the efficiency of climate control systems requires advanced simulation tools for an accurate evaluation of both the energy savings and thermal comfort of innovative heating and cooling solutions. In this study, the advancements beyond the state of the art consists primarily of the methodology tackling the reduction of computational costs of intensive computational fluid dynamics (CFD) simulations and/or time-consuming experimental investigations and the simultaneous assessment of vehicle cabin thermal comfort and energy flows. The approach was validated against climatized chassis dyno measurements from the EU Horizon 2020 research project QUIET. Indeed, all the considered locations within the cabin were properly validated, both in steady state and transient conditions with the largest deviations at steady state below 3 °C. Additionally, the validation results show a perfect agreement for the average cabin predicted mean vote (PMV) value and a largest deviation in terms of the PMV for the other locations below 0.3. Furthermore, the applicability of the methodology is proved with the help of its application on a parametric study for which various cabin temperature setpoints and heating, ventilation and air conditioning (HVAC) modes were simulated in winter operation.

Forecasted datasets of electric vehicle consumption on the electricity grid of Spain

The information included in this study were calculated on the basis of data provided by the Spanish electricity grid, for thirteen years between 2007 and 2019. This data includes: the average consumption demand on the Spanish electricity grid at national level, and its availability. Subsequently, the report looks at the number of electric vehicles that could be supported in the years 2020–2023, depending on the consumption demand and availably of the electricity grid for those future years. The data presented in the article refers to the research study: ‘Electric vehicles in Spain: An overview of charging systems’[1] and ‘Analysis of charging stations for electric vehicles in Spain’ [2].

Coordinating Electric Vehicle Charging Station Installation between the Government and Automakers

Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

Charge coordination and battery lifecycle analysis of electric vehicles with V2G implementation

With the growing concerns on the energy depletion and the reduction of CO2 emission, electric vehicles (EVs) have gained popularity in transport sector due to clean and reliable energy source. However, the charging of EVs has imposed significant load in the electrical distribution system. The stability of power network is disturbed with uncoordinated charging. This work aims to investigate the optimal EV coordination with vehicle to grid (V2G) technology for the cost-benefit analysis. However, the cost of EV battery replacement due to degradation is of paramount concern for the EV users, which constitute the most important ingredient to ensure active participation in V2G energy exchanges. Therefore, battery degradation cost is formulated for real time analysis taking depth of discharge at each time interval. The firefly algorithm has been used to optimize the system cost. The performance of the proposed system is tested on modified 33 bus distribution system in the presence of renewable energy sources (RES). The impact of system cost and energy losses are analysed for different RES penetration, EV capacities and travelling distances. The simulation results show significant reduction in operating cost when RESs are integrated into distribution network. Moreover, V2G technology is beneficial for EV consumers with high penetration of RES.

Energy efficiency trade-offs in small to large electric vehicles

BackgroundAs electric kick scooters, three-wheelers, and passenger cars enter the streets, efficiency trade-offs across vehicle types gain practical relevance for consumers and policy makers. Here, we compile a comprehensive dataset of 428 electric vehicles, including seven vehicle types and information on certified and real-world energy consumption. Regression analysis is applied to quantify trade-offs between energy consumption and other vehicle attributes.ResultsCertified and real-world energy consumption of electric vehicles increase by 60% and 40%, respectively, with each doubling of vehicle mass, but only by 5% with each doubling of rated motor power. These findings hold roughly also for passenger cars whose energy consumption tends to increase 0.6 ± 0.1 kWh/100 km with each 100 kg of vehicle mass. Battery capacity and vehicle mass are closely related. A 10 kWh increase in battery capacity increases the mass of electric cars by 15 kg, their drive range by 40–50 km, and their energy consumption by 0.7–1.0 kWh/100 km. Mass-produced state-of-the-art electric passenger cars are 2.1 ± 0.8 kWh/100 km more efficient than first-generation vehicles, produced at small scale.ConclusionEfficiency trade-offs in electric vehicles differ from those in conventional cars—the latter showing a strong dependency of fuel consumption on rated engine power. Mass-related efficiency trade-offs in electric vehicles are large and could be tapped by stimulating mode shift from passenger cars to light electric road vehicles. Electric passenger cars still offer potentials for further efficiency improvements. These could be exploited through a dedicated energy label with battery capacity as utility parameter.

Self-Sustainable off grid Electric Vehicle EV Charging Station with Integration of Renewable Sources

Electric Vehicles (EV) are the world’s future transport systems. With the rise in pollutions and its effects on the environment, there has been a large scale movetowards electrical vehicles. But the plug point availability for charging is the serious problem faced by the mostof Electric Vehicle consumers. Therefore, there is a definite need to move from the GRID based/connected charging stations to standalone off-grid stations for charging the Electric Vehicles. The objective of this paper is to arrive at the best configuration or mix of the renewable resources and energy storage systems along with conventional Diesel Generator set which together works in offgrid for Electric Vehicle charging. As aconclusion, by utilizing self-sustainable off-grid power generation technology, the availability of EV charging stations in remote localities at affordable price can be made and mainly it reduces burden on the existing electrical infrastructure.

Pricing for private charging pile sharing considering EV consumers based on non-cooperative game model

The supply of public charging infrastructure is insufficient to meet the charging demand of a large number of electric vehicles (EVs). Private charging pile sharing is an emerging solution to alleviate this imbalance. However, a reasonable price for charging pile sharing has not yet been determined. This study employs a non-cooperative game model to determine a charging pile sharing price considering EV consumers’ charging behaviors. First, a multi-logit model is constructed to measure the probability of EV consumers’ charging behavior choices. Then, a two-matrix game model is established between the private charging pile sharing and public charging mode. Using Beijing as a sample, the sharing rate and price strategies of private and public charging piles are calculated based on the proposed game model. The results show that the optimal sharing rate is 20.01% private charging pile sharing with 1.14 yuan/kWh and 79.99% public charging with 1.7946 yuan/kWh. Sensitivity analysis shows that economics is the most sensitive factor affecting the charging price of private charging pile sharing. Finally, policy recommendations are outlined to improve the private charging pile sharing rate and service efficiency, to broaden the charging options for EV consumers, reduce the construction of public charging piles, and save the government subsidy.

Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: The case of South Korea

With the world seeking ways to cope with climate change, the interest in and demand for electric vehicles are increasing as part of the efforts to resolve the issue of fine dust, especially in South Korea. The Korean government has consistently announced plans to promote electric vehicles as a means of transportation by providing benefits such as subsidies for electric vehicle purchase and expansion of charging infrastructure. Meanwhile, as electric vehicles continue to grow in number, the energy industry has become attentive to its role as a resource for power generation through vehicle to grid technology. This study analyzes electric vehicle consumer preferences using the discrete choice experiment (DCE) and found that there exists a clear nested structure in Korean consumers’ choice of vehicle. The study also estimates the amount of vehicle to grid electricity supply in the power market and calculates not only national but also individual economic benefit of electric vehicle owners participating in vehicle to grid services based on the estimated amount of electricity supplied. The results of scenario analysis indicate that the estimated electric vehicle supply in Korea will be about 560,000 units cumulatively and that the vehicle to grid electric vehicle power supply scale will reach 1.81 GW by 2030. The estimation shows that the economic benefit of vehicle to grid at the national power market level is 50.9 billion KRW per year, while the economic benefit at an individual level (per vehicle) is 104,151 KRW.