Preferences For Electric Vehicles Research Articles

Can Decoy Effects Nudge Ride-hailing Drivers’ Preferences for Electric Vehicles?

Can Decoy Effects Nudge Ride-hailing Drivers’ Preferences for Electric Vehicles?

Consumers' preferences for electric vehicles: The role of status and reputation

Consumers' preferences for electric vehicles: The role of status and reputation

The impact of learning and short-term experience on preferences for electric vehicles

The transport sector is a key contributor of global greenhouse gas emissions and electric vehicles have become a focus in striving to achieve decarbonisation and efficiency in the sector. This study uses a stated preference methodology, specifically choice experiments, to investigate the attitudes and preferences of potential buyers for a number of technical, environmental and policy attributes of electric vehicles in Ireland. We specifically focus on whether learning through provision of information and a brief vehicle experience affects preferences and welfare measures. Previous studies have examined the role of lengthy electric vehicle demonstration trials, for example 3 month trials, on preferences. This paper addresses a gap in the literature by considering the role of much shorter scale experience (minutes rather than months) on attitudes which more closely represents the experience that a potential purchaser will have at the point of investment. Using random parameter models, our results show that people are willing to pay more for certain technical and environmental features of electric vehicles, however, policy measures such as preferential parking rates are seen to have a non-significant effect on utility of participants. The learning process increases the significance of the environmental component, and produces significantly higher willingness to pay for increased battery range and vehicle size.

BiS4EV: A fast routing algorithm considering charging stations and preferences for electric vehicles

Electric Vehicles (EVs) have grown in recent years as they have become a promising alternative to traditional fossil fuel-driven vehicles. As a result, new routing algorithms that consider both the locations of charging stations and the charging preferences of users are necessary to maintain urban traffic efficiency. This paper proposes a Constrained Route Planning (CRP) approach, called BiS4EV, to provide efficient route planning for EVs in large urban road networks. CRP is an NP-hard problem. BiS4EV introduces a two-layered structure to solve the CRP problem for EVs. In the first layer, BiS4EV integrates the locations of charging stations and the battery consumption on each road arc into a road network graph and proposes a fast routing algorithm that considers the constraints of battery capacity and charging preferences to find the shortest feasible path for EVs. The second layer incorporates charging policies to the obtained path, which forms a complete route plan for EVs. Such a two-layered method breaks down the complexity of the problem and avoids directly solving the entire NP-hard problem. We have proved the correctness of the proposed approach in theory, and the presented experimental results verified the efficiency and the effectiveness of BiS4EV from the practical aspect.

Correction to: Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time

Correction to: Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time

Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time

Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.

Analysing online behaviour to determine Chinese consumers’ preferences for electric vehicles

While prior studies of consumer preferences on electric vehicles (EVs) utilized stated preference (SP) or revealed preference (RP) data, this study examined consumer preferences for EVs through their online behaviour. Using big data and text mining technologies to explore large quantities of Chinese consumers’ online behaviour pertaining to EV selection, comparison, purchase inquiry, and comments, we found that EV prices, car classification and powertrain types were the most important factors influencing consumer response. In addition to general interests, we also noted application specific interests. For instance, we observed a preference for small battery electric vehicle (BEV) models with fast-charging batteries, which are well-suited to work commutes and daily life. By contrast, when examining plug-in hybrid electric vehicles (PHEVs), we noticed that consumers expressed a preference for sport utility vehicle (SUV) models. Finally, we found that in addition to price and technical specifications, EV aesthetics play a significant role in consumer choice. These conclusions are valuable for government agencies and EV manufacturers looking to promote EVs and address environmental pollution by appealing to consume interests.

Electric, plug-in hybrid, hybrid, or conventional? Polish consumers’ preferences for electric vehicles

Poland aims at stimulating the market to reach a target of 50,000 plug-in and battery electric vehicles by 2020. However, as in other Eastern European countries, the market penetration stays very low. In Poland, there were only 475 battery electric vehicles and 514 plug-in electric vehicles registered in 2017. To identify effective support measures, this paper examines the preferences of Polish consumers for three types of electric vehicles: battery, hybrid, and plug-in hybrid vehicles. We use a discrete choice experiment to estimate the willingness to pay of a representative sample of consumers intending to buy a car in Poland. We find that electric vehicles are significantly less preferred than conventional cars, even under a public programme that would enable slow-mode charging in places where respondents usually park. We quantify the marginal willingness to pay for increasing the driving range, reductions in charging time, the availability of fast-mode charging stations, and the provision of policy incentives. The novelty of the paper lies in presenting a scenario with the slow-mode and availability of several levels of fast-mode charging stations and examination of the extent to which the heterogeneity of consumer preferences is driven by place of residence (urban, suburban, rural), intention to buy a new versus a used car, and the annual mileage. This is also the first discrete choice experiment on electric vehicles conducted in Eastern Europe. To stimulate the electric vehicle market, we recommend a pricing policy that affects the operating costs and other incentives along with an effective up-front price incentive scheme.

The Influence of Environmental Concerns on Driverss Preferences for Electric Cars

We examine the influence of drivers’ environmental concerns on their preferences for different types of plug-in electric vehicles (PEVs). Our empirical approach is built around the results of a large-scale survey among Dutch drivers, where preferences for electric vehicles are elicited through a choice experiment and environmental concerns are reflected in individual responses to Likert-type questions. On this basis, we develop advanced latent class models to study preference heterogeneity and its link to drivers’ socio-demographic background and environmental concerns. We find that environmental concerns are an important predictor of class membership and that highly concerned drivers tend to cluster in classes with a positive stand towards PEVs. High environmental concerns are positively associated with driver’s age and education, while negatively related to driver’s household income.