A statistical approach to estimating acceptance of electric vehicles and electrification of personal transportation

Tamor, M. A.; Gearhart, Chris; Soto, Ciro A.

doi:10.1016/j.trc.2012.07.007

Cited by 122 publications

(56 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is more visible for the EV vans, indicating they perform more stops longer than trip threshold value, but lower than the time to recharge, compared to other EV vehicles. The numbers for the average daily distance are low, compared to results from the Flemish Survey on Trip behaviour [5], which shows an average daily distance of 79 kilometres, of which 31 kilometres are travelled by car. As other means of transport have not been registered, these figures are hard to compare and further research should clarify whether this difference is due to the vehicle properties (limited range) or the user properties (use by employers from the municipality administration, implying shorter trips).…”

Section: Measures Of Distancecontrasting

confidence: 61%

See 1 more Smart Citation

Identification of EV use patterns, based on large scale EV monitoring data

Cauwer

Gillis

Coosemans

et al. 2013

WEVJ

View full text Add to dashboard Cite

This paper presents a methodology to analyse electric vehicle (EV) monitoring data to identify use patterns.The methodology is applied to the monitored data of a fleet of electric vehicles and plug-in hybrid vehicles (PHEV). The methodology introduces a link of the travel behaviour and characterisation with the range of the electric vehicle. To identify use patterns a number of indicators are defined: reset points, extra range potential, daily distance driven, average distance between reset points and maximum distance between two reset points. These indicators are calculated on monitored data of EVs, and the first results and observations are presented.

show abstract

Section: Measures Of Distancecontrasting

confidence: 61%

“…In the data analysis below, this threshold time was set to 15 minutes. 5 For this reason, a range closer to the reality has been used in the analysis of the data. The AER in used in the analysis is 70% of the AER range specified by the manufacturer.…”

Section: The Maximum Distance Between Two Reset Pointsmentioning

confidence: 99%

Identification of EV use patterns, based on large scale EV monitoring data

Cauwer

Gillis

Coosemans

et al. 2013

WEVJ

View full text Add to dashboard Cite

show abstract

“…(4) indicates that the passenger demand is assigned to the specific car at one time. Only one passenger can be served by the car at a time (5); if the maximum distance between the demand point and any EV's coordinates is less than the mileage of the EV (6), then we should make full use of power (7).…”

Section: The Modelmentioning

confidence: 99%

“…However, there are a number of factors that hinder the development of the EV industry, including mileage (battery), the charging time, and charging convenience as well as the purchasing price of EVs, the performance of the vehicle, and battery recycling, among others [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Finding Key Factors Affecting the Locations of Electric Vehicle Charging Stations: a Simulation and ANOVA Approach

Tang¹,

Gong²,

Liu³

et al. 2017

Int. j. simul. model.

View full text Add to dashboard Cite

In this study, we aim to find the key factors affecting the location of electric vehicle charging stations. We first developed a Non-deterministic Polynomial (NP) model that aims to minimize the total travel distance of cars. Second, we applied an agent-based simulation algorithm to determine the optimized location for charging stations. Finally, we conducted multi-simulation and statistical analysis of passenger priority, car mileage, electric vehicle distribution and passenger distribution using a one-way analysis of variance (ANOVA). The results of this study show that priority is not a factor affecting the location of electric vehicle (EV) charging stations and that mileage, the EV distribution and the passenger distribution are factors affecting the location of EV charging stations, with exogenous variables such as the type of circuit and the voltage drawn as constants. The proposed model can help provide a reference for the location of charging stations in urban areas.

show abstract

“…With these vehicles, the range limitations will not be a problem, but there will still be a limit to the number of miles that are electrified. 49 The assumed vehicle parameters and estimated EIs for the four drivetrains assessed, as well as the baseline vehicle assumptions, are given in Table 1 . Figure 4 shows the breakdown of EI for these vehicles.…”

Section: Battery Electric Vehiclesmentioning

confidence: 99%