Are electric vehicles getting too big and heavy? Modelling future vehicle journeying demand on a decarbonized US electricity grid

Galvin, Ray

doi:10.1016/j.enpol.2021.112746

Cited by 31 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As illustrated in Table 2, this is the most effective way to improve fuel efficiency. Galvin [48] presented a counterpoint, illustrating that EVs are catching up with ICE vehicles, narrowing the gap in size and power, with implications for emissions. ICEs will still be the major power source for decades to come, regardless of the penetration of AFVs (alternative fuel vehicles), because the latter must start from very low bases [2].…”

Section: Resultsmentioning

confidence: 99%

“…The same could occur for EVs. Galvin [48] explained that EVs are still less potent than ICEs in the US, with 254 HP against 284 HP on average, but this gap may be closed by the emergence of super-powerful EVs with more than 600 HP. He estimated that a 5% weight increase in smaller EVs resulted in a 4.7% increase in the electricity demand, whereas for larger EVs, this number was 10.5%.…”

Section: Resultsmentioning

confidence: 99%

“…The title of this section is taken from [48]. The 1.0 L engines were initially synonymous with entry-level vehicles.…”

Section: Trends In Performance In the Brazilian Ldv Fleetmentioning

confidence: 99%

See 2 more Smart Citations

Toward a Direct CO2 Tax for the Brazilian LDV Fleet

Mosquim,

Collaço,

Mady

2024

Energies

View full text Add to dashboard Cite

Policies for reducing the environmental impact of light-duty vehicles are restarting in Brazil. While electrification attracts more engagement than other forms of carbon footprint reduction, the use of ethanol could achieve faster gains with the existing infrastructure. The prominence of entry-level cars has reduced, which may be due to several factors, making these vehicles uneconomical for manufacturers to produce. This situation is different from those in other countries. It is our premise that other modes, such as walking, cycling, and public transportation, are the main methods of decarbonization. However, most need improvements in terms of security and attractiveness for consumers. Hence, the internal combustion engine will dominate the Brazilian fleet for quite some time. This article discusses the policies used to incentivize the implementation of entry-level vehicles in contrast with electric vehicles based on regression models of the Brazilian fleet. By providing a history of the 1.0 L engine in Brazil, this article debates replacing the indirect displacement taxation with direct CO2 taxation, which can make the Brazilian entry-level car practical again, with the possibility of adopting some incentives. This logic will cause a fleet renovation and more rational energy use. The distinguishing feature of this article is the proposition of possible public policies based on a regression model of the national fleet. The key findings show that the fleet is getting older and heavier, consuming more energy, and emitting greater amounts of CO2 per unit of displacement, for example, 1.0 L. Furthermore, the carbon footprint of the electricity mix is similar to that of the produced ethanol (from well to wheel, the carbon footprint is the same). This highlights the necessity for national policy adjustments and effective public policies to reduce carbon emissions from the energy sector for small vehicles.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Toward a Direct CO2 Tax for the Brazilian LDV Fleet

Mosquim,

Collaço,

Mady

2024

Energies

View full text Add to dashboard Cite

show abstract

“…We model the distribution of drivers' total annual energy use by assuming a high mean efficiency in future EVs of 5 miles per kWh (ref. 72 ) with negligible losses to charging efficiency and define seven bins aligned with the annual mileage d is tr ib ut ions: ( 0, 600), (600, 1,000), (1,000, 1,600), (1,600, 2,000), (2,000, 3,000), (3,000, 4,000), (4,000, +) kWh. We assume that the distribution of EVs over counties will match the current distribution of passenger vehicles at the high levels of EV adoption studied in this paper.…”

Section: Nature Energymentioning

confidence: 99%

Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

et al. 2022

View full text Add to dashboard Cite

Electric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic dispatch model of 2035 generation. We find that peak net electricity demand increases by up to 25% with forecast adoption and by 50% in a stress test with full electrification. Locally optimized controls and high home charging can strain the grid. Shifting instead to uncontrolled, daytime charging can reduce storage requirements, excess non-fossil fuel generation, ramping and emissions. Our results urge policymakers to reflect generation-level impacts in utility rates and deploy charging infrastructure that promotes a shift from home to daytime charging.

show abstract

“…In order to meet the FCS's demand for electricity while reducing the cost of production and energy loss, energy management and optimization are consequently crucial for the mixed energy system 33 . A mixed-integer linear programming-based energy management technique was created as part of a model reference adaptive control to address this issue and enhance the charging station's performance.…”

Section: Introductionmentioning

confidence: 99%

Power consumption prediction for electric vehicle charging stations and forecasting income

Akshay,

Grace,

Gunasekaran

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

show abstract

Are electric vehicles getting too big and heavy? Modelling future vehicle journeying demand on a decarbonized US electricity grid

Cited by 31 publications

References 19 publications

Toward a Direct CO2 Tax for the Brazilian LDV Fleet

Toward a Direct CO2 Tax for the Brazilian LDV Fleet

Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

Power consumption prediction for electric vehicle charging stations and forecasting income

Contact Info

Product

Resources

About