Modeling Demand for Electric Cars - A Methodical Approach

Link, Christoph; Raich, Ulrike; Sammer, Gerd; Stark, Juliane

doi:10.1016/j.sbspro.2012.06.1169

Cited by 16 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They reach the optimistic conclusion that the BEV could gain a market share between 48% and 72%. On the contrary, Link et al (2012) predict that the BEVs could increase in Austria to a modest 2.5% in 2020 and to 5.7% in 2025, whereas HFCVs have a considerable higher potential (11.5%). Glerum and Themans (2011) use a logit model to analyse the market potential of the Renault cars compared to the other brands.…”

Section: Literature Reviewmentioning

confidence: 94%

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

Valeri

Danielis

2015

Transport Policy

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 94%

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

Valeri

Danielis

2015

Transport Policy

View full text Add to dashboard Cite

“…Factors Reviewed [16] financial incentives, urban density, education level, environmentalism, income, fuel price, EV price, presence of production facilities, per capita vehicles, EV model availability, EV introduction date, recharging infrastructure, electricity price [34] income, EVSE at home, house ownership, mileage traveled, level of hybrid ownership, tendency to buy more new cars, ownership rate of solar panels [35,36] purchase price, range, CO 2 emission performance, engine power, time for charging/refueling, and running costs [37] price, running cost, driving range, acceleration, accessibility to recharging, [22] price, driving range, charging time, acceleration, fuel cost, pollution emitted, age, education, gasoline price change expectation, environmental sensitivity, preference to HEV, preference to small and mid-sized car, charger at home, innovation acceptance tendency [25] price, range, home refueling time, home refueling cost, service station refueling time, service station refueling cost, service station availability, acceleration, top speed, tailpipe emission, vehicle size, body type, luggage space [27] price, fuel cost, repair and maintenance cost, commuting time, acceleration, range, charging time [26] price, fuel cost, range, acceleration, fuel availability, emission reduction…”

Section: Studymentioning

confidence: 99%

Does Driving Range of Electric Vehicles Influence Electric Vehicle Adoption?

Kim

Lee²,

Lee

2017

Sustainability

View full text Add to dashboard Cite

Abstract:This study aims to determine the influential factors on the market share of electric vehicles through panel data analysis based on time series data from 2011 to 2015 in 31 countries. We selected five significant independent variables that are expected to affect electric vehicle adoption based on literature review. The econometric model in this study suggests that the relative price of electric vehicle compared to internal combustion engine vehicle, driving range, and number of models available in markets are correlated to the market share of electric vehicles. On the other hand, relationship between recharging infrastructure-an important factor for electric vehicle adoption in many studies-and market share of electric vehicles turned out to be insignificant in this study. From a political point of view, we argue that policy makers need to allocate more resources to research and development in order to extend driving range at the early stage of electric vehicle deployment in the markets.

show abstract

“…This sector also accounts a big contribution of total greenhouse gas emission. For the most part, this is due to road transport [1,2]. Nowadays, the concept of an alternative propulsion is becoming increasingly important as a consequence of climate change and the increasing global resource crisis.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Bus Body Frame Structure for Weight Minimizing with Constraint of Natural Frequency using Adaptive Single-Objective Method

Haryanto¹,

Raharjo²,

Kurdi³

et al. 2018

IJSTT

View full text Add to dashboard Cite

The objective of this work is to analyze and optimize a bus frame structure using Finite Element Method in dynamic conditions. The bus body geometry was obtained directly from the three-dimensional Computer-Aided Design files. The optimization was conducted to determine the minimum weight of the bus frame structure without violating the specified natural frequency constraints. The design variable is the thickness of the bus body frame. In present study, Adaptive Single Objective method was chosen as an optimizer method. The results show that the structural weight of the bus frame can be reduced about 8% without changing its dynamic characteristic.

show abstract

Modeling Demand for Electric Cars - A Methodical Approach

Cited by 16 publications

References 8 publications

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

Does Driving Range of Electric Vehicles Influence Electric Vehicle Adoption?

Optimization of Bus Body Frame Structure for Weight Minimizing with Constraint of Natural Frequency using Adaptive Single-Objective Method

Contact Info

Product

Resources

About