Heterogeneous preferences of green vehicles by vehicle size: Analysis of Seoul case

Although there are several policies for promoting green vehicles, green vehicles have yet to penetrate the market to the extent desired. To attain the goal, a complete understanding of consumers’ preferences of green vehicles is essential. This paper proposes and specifies the fuel-type choice models among conventional gasoline vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, and electric vehicles by vehicle size using the stated preferences data collected in Seoul, Korea. The results highlight the need to accommodate the correlation between similar alternatives and the unobserved heterogeneity within the context of choosing a green vehicle. The choice probabilities of green vehicles are affected by the relative impacts of the vehicles’ attributes and socio-demographic variables, and both of these variables are affected by the sizes of the vehicles. For compact size and mid-size vehicles, the effects of operating costs were less than those of purchase prices; however, for the subcompact vehicles, the effects of operating cost were greater than those of purchase price. The parameter of operating costs was not statistically significant in the full-size model. With respect to electric vehicles, the availability of fuel stations would be more important in Seoul than in the U.S. These results can also be useful for policy makers in that they provide information about the impact of green vehicles’ attributes on the choice probabilities of green vehicles.     

Aftermarket vehicle hybridization: Potential market penetration and environmental benefits of a hybrid-solar kit

The possibility of upgrading conventional vehicles to hybrid electric vehicles has recently gained considerable interest. Among the diverse options for hybridization, researchers of the Energy and Propulsion Laboratory at the University of Salerno (Italy) have patented a system based on the electrification of the rear wheels in front-wheel-drive vehicles by adopting in-wheel motors and adding a lithium-ion battery, which is rechargeable via plug-in and solar mode. The technology integrates various components, in-vehicle Intelligent Transportation Systems and advanced on-line management algorithms. With respect to the proposed technological context, this paper uses a real case study to investigate the potential market penetration, the achievable environmental benefits and relevant policy issues. To this aim, the paper first analyses the main behavioral determinants that may affect users' perception of this new technology, then specifies and calibrates an installation choice model, and finally develops bottom-up methodology to estimate the environmental benefits to be obtained from different commercialization scenarios. The proposed methodology was applied to the city of Salerno and the achievable benefits were compared to those obtainable through the implementation of traditional transport policies.     

Demand for electric vehicles in hybrid households: an exploratory analysis

Previous studies of the potential market for battery electric vehicles (BEVs) from different research streams have failed to converge on a single, robust estimate. What these previous research streams share are untested or implausible assumptions about consumer response to new transportation technology. We frame the BEV purchase decision in terms of a household's entire stock of vehicles, car purchase behavior and travel behavior. Within this framework, households which own both electric vehicles and gasoline vehicles are called ‘hybrid households’. Because nearly all consumers are unfamiliar with the characteristics of BEVs, we designed an interactive interview based on week-long travel diaries, which we call Purchase Intentions and Range Estimation Games (PIREG) to explore hypothetical hybrid household vehicle use. Our primary finding is that consumers' perceived driving range needs are substantially lower than previous hypothetical stated preference studies conclude. We find evidence of a viable market for BEVs with 60 to 100 miles driving range.     

Can public slow charging accelerate plug-in electric vehicle sales? A simulation of charging infrastructure usage and its impact on plug-in electric vehicle sales for Germany

Alternative fuel vehicles face the lack of refueling infrastructure as one obstacle to market diffusion and potential operators of refueling stations await significant market shares before constructing a dense refueling network. The resulting lock-in effect or chicken-egg-problem has scarcely been analyzed for plug-in electric vehicles (PEVs) up to now. The research question of this article is How much public charging infrastructure for PEVs is needed and is there mutual interaction in the diffusion of public charging infrastructure and electric vehicles?

Here, we present an agent-based market diffusion model for PEVs and their charging infrastructure that is based on a large number of individual driving profiles for private and commercial car holders in Germany. Our results demonstrate the possibility of a market diffusion in Germany without any slow public charging infrastructure until 2030. Although a charging point at home is obligatory for early adopters, the second-best option for an infrastructure set-up is at work where the majority of vehicles is parked for a long time during the day, the installation is not costly and users profit more than from public facilities. Public slow charging facilities do not increase PEV market shares and they need to be subsidized for a long time.     

Where are the electric vehicles? A spatial model for vehicle-choice count data

Electric vehicles (EVs) are predicted to increase in market share as auto manufacturers introduce more fuel efficient vehicles to meet stricter fuel economy mandates and fossil fuel costs remain unpredictable. Reflecting spatial autocorrelation while controlling for a variety of demographic and locational (e.g., built environment) attributes, the zone-level spatial count model in this paper offers valuable information for power providers and charging station location decisions. By anticipating over 745,000 personal-vehicle registrations across a sample of 1000 census block groups in the Philadelphia region, a trivariate Poisson-lognormal conditional autoregressive (CAR) model anticipates Prius hybrid EV, other EV, and conventional vehicle ownership levels. Initial results signal higher EV ownership rates in more central zones with higher household incomes, along with significant residual spatial autocorrelation, suggesting that spatially-correlated latent variables and/or peer (neighbor) effects on purchase decisions are present. Such data sets will become more comprehensive and informative as EV market shares rise. This work’s multivariate Poisson-lognormal CAR modeling approach offers a rigorous, behaviorally-defensible framework for spatial patterns in choice behavior.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.     

Electric and hybrid car use in a free-floating carsharing system

A free-floating carsharing system is a flexible way to provide cars to members in a limited area. This paper discusses the user behavior and choice between electric and hybrid cars in a free-floating system. We use Auto-mobile's transactional and Global Positioning System (GPS) datasets to analyze car use. We develop a binomial logit model to find the probability of choosing an electric car when there is a choice. The analysis reveals that travel distance has a major influence on this probability: electric vehicles are less popular for distances of more than 24 km. Cold temperatures and being a female also decrease the probability, while higher energy level increases odds. The spatial analysis shows a difference in behavior between both car technologies. Trips made by electric cars are less dispersed spatially than those made by hybrid cars.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

Location optimisation method for fast-charging stations along national roads

Limited range of electric vehicles is still a huge barrier compared to conventional vehicles. A well-established charging station network, which is derived from users' charging demand, facilitates the spread of electric vehicles and lessens the range anxiety. Several methods have been developed for locating fast-charging stations along national roads in Europe according to the given objective function. In this paper, an arc-based location optimisation method realized by using a geographic information system and greedy algorithm is presented. An ‘oil stain’ deployment strategy is used to achieve even coverage with the minimum number of fast-charging stations along the roads. Several demographic, neighbourhood, and transport-related attributes, as well as the available services that influence the utilization of a fast-charging station, have been identified and their effects have been revealed in a systematic approach. The developed multi-criteria decision-making method has been applied to evaluate the rest areas along motorways and main roads and to propose deployment locations for fast-charging stations. The method was applied for Hungary as a case study and validated using real origin-destination (O-D) data. By the application of the locating method, the user can specify a network character by geographic parameters. The method can be especially beneficial if the O-D flows are unknown. Furthermore, the even distribution of the stations contributes to the high utilization of the fast-charging stations.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Anticipating long-distance travel shifts due to self-driving vehicles

Increasing population and travel demand has prompted new efforts to model travel demand across the United States. One such model is rJourney that estimates travel demand among thousands of regions and models mode and destination choice. rJourney includes records representing 1.17 billion long-distance trips throughout the year 2010. Although inter-regional impacts caused by an increase of automated vehicles (AVs) has been investigated, there is little research on inter-regional travel and how longer distance destination and mode choices will change. Because of conveniences offered by AVs, the value of travel time of drivers is expected to fall, thus reducing the generalized cost of AV travel. To initially analyze the impacts of AVs in the United States, a new AV mode was added to a subset of the rJourney mode and destination choice models. With an initial scenario assuming an operating cost of AVs that is 118% of traditional cars, two outcomes are observed that are solely based on model results. First, the attractiveness of AVs severely digs into the airline travel market, reducing airline revenues to 53%. Second, the introduction of AVs results in a shift of destination choice, increasing travel in further distances for personal vehicles, but favoring closer distances across all modes, for an overall 6.7% decline in US passenger-miles traveled on existing long-distance trips. While this preliminary research has revealed an initial perspective on how an existing model can support AVs, the increasing availability of data as AVs emerge will refine nationwide long-distance modeling.     

Battery electric vehicles in cities: Measurement of some impacts on traffic and government revenue recovery

We are told that electric vehicles, cars in particular, will be good for the environment. But what exactly might this mean? It is true that end use emissions will be significantly reduced when we move from fossil fuels to green energy sources? Assuming that the demand for such cars, including battery electric vehicles (BEVs) in particular will grow, we can expect a significant number of such vehicles manufactured in future years. Given the potentially relatively lower cost (fewer moving parts) compared to internal combustion engine vehicles as well as the significantly lower usage costs per kilometre, we would expect a level of uptake that could impact on the performance of the road network (perhaps increased congestion and crash risk) but also a concomitant reduced use of public transport and fuel excise loss. In this paper, we apply the MetroScan modelling system in the Greater Sydney Metropolitan Area (GSMA) over the period 2021–2056 to identify the likely impact that the growth in BEV ownership and use will have on vehicle kilometres, modal shares, government revenues, levels of CO₂ emissions and other impacts. Moreover, we investigate the introduction of a BEV usage charge proposed in Australia to see what it might do to these key performance indicators and whether it can offset the adverse effects during BEV uptake such as government fuel excise revenue loss and increased congestion.     

Are batteries ready for plug-in hybrid buyers?

The notion persists that battery technology and cost remain as barriers to commercialization of electric-drive passenger vehicles. Within the context of starting a market for plug-in hybrid electric vehicles (PHEVs), we explore two aspects of the purported problem: (1) PHEV performance goals and (2) the abilities of present and near-term battery chemistries to meet the resulting technological requirements. We summarize evidence stating that battery technologies do not meet the requirements that flow from three sets of influential PHEV goals due to inherent trade-offs among power, energy, longevity, cost, and safety. However, we also show that part of this battery problem is that those influential goals are overly ambitious compared to goals derived from consumers’ PHEV designs. We elicited PHEV designs from potential early buyers among U.S. new car buyers; most of those who are interested in a PHEV are interested in less technologically advanced PHEVs than assumed by experts. Using respondents’ PHEV designs, we derive peak power density and energy density requirements and show that current battery chemistries can meet them. By assuming too aggressive PHEV goals, existing policy initiatives, battery research, and vehicle development programs mischaracterize the batteries needed to start commercializing PHEVs. To answer the question whether batteries are ready for PHEVs, we must first answer the question, “whose PHEVs?”     

Examining individuals preferences for hybrid electric and alternatively fuelled vehicles

This paper examines individuals motivations when purchasing vehicles, focusing upon what factors would encourage individuals to purchase hybrid electrical vehicle (HEV) or alternatively fuelled vehicle (AFV). AFVs in this paper refer to any cars run on alternatives to petrol and diesel. This research attempts to ascertain whether reductions in fuel costs, vehicle registration tax (VRT), or green house gas emissions would encourage individuals to purchase a HEV or an AFV instead of a conventional vehicle. VRT is an Irish tax that is levied on the purchase of new vehicles. One of the motivations to conduct this research was to examine a new car tax and VRT scheme introduced by the Irish government in 2008. This new policy rewards the purchase of environmentally friendly cars, with lower VRT and car tax rates. To understand individuals’ perceptions of these new taxes a survey was sent to recent customers of a car company in Ireland. The survey asked respondents about their recently purchased vehicle and how important they considered vehicle attributes such as environmental performance, fuel cost, and safety, before making their car purchase. The survey also contained a number of stated preference experiments that were designed to ascertain what factors influence individuals’ decisions when purchasing their new car. The results showed that respondents did not rate green house gas emissions or VRT as crucial attributes when purchasing a new vehicle. The vehicle attributes that respondents rated most highly were reliability, automobile safety, fuel costs, and the cost price. The majority of respondents agreed that HEVs and AFVs are better for the environment, cheaper to run than conventional vehicles and would be the vehicle of choice in ten years time.     

Conditions for electric vehicle taxi: A case study in the Greater Stockholm region

This study investigates the usability of electric vehicles (EVs) in a taxi company in Greater Stockholm, Sweden. By investigating cost and revenue data of both electric and conventional taxi vehicles, as well as by interviewing taxi drivers and carriers, an assessment has been made of the financial and operational implications of using EVs in a company’s taxi fleet. Both the drivers’ and the carriers’ perspectives have been examined. The main findings are that the investigated e-taxis have a similar or lower Total Cost of Ownership and slightly higher profitability than the investigated conventional taxis. For taxi drivers, using e-taxis implies more advanced planning and revenue service time being sacrificed for charging. However, certain customers’ preferences for EVs, as well as benefits such as corporate clients favoring e-taxis and a zero emission priority queuing system at Stockholm’s main international airport (partly) compensate for time devoted to charging. In order to facilitate increased use of e-taxis, more fast charging facilities should become available at strategic locations. Besides that, there are signs that carriers’ lack of information about the opportunities and consequences of shifting towards e-taxis hamper a wider deployment of e-taxis.     

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

A review of spatial localization methodologies for the electric vehicle charging infrastructure

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem’s spatial dimension, and derives perspectives for further research.     

Urban public charging station locating method for electric vehicles based on land use approach

Since the use of electric vehicles decreases the local pollution and noise emission electromobility gains a huge potential in sustainable transport. The currently insufficient charging network, namely the low number of stations and ill-chosen locations are a significant barrier of the widespread of electric vehicles in many countries. Accordingly, localization of new charging stations is of utmost importance. In this study, we develop a two-level charging station locating method. Weighted multicriteria methods were introduced to evaluate territory segments and allocate charging stations within a segment applying a hexagon-based approach and using a greedy algorithm. The novelty of the method in comparison with previous studies is that it assesses the potential of electric vehicle use on macro-level and the possible locations of charging stations on micro-level with a focus on the land-use. We apply the method to Hungary (macro evaluation) and to a district of the capital city, Budapest (micro evaluation). It is found that charging stations at P + R facilities, close to concentrated services and high-density areas are better suited to serve urban public charging demand than gas stations.     

Rural vehicles in China: appropriate policy for appropriate technology

Over 3 million Chinese Rural Vehicles (CRVs) were produced in China in 2002, three times that of conventional passenger cars. Yet these smaller, simpler, indigenous vehicles are virtually unknown outside China. The CRV industry is unusual in that it evolved largely outside the control of government regulation and policy, using local technology and resources. CRVs now consume one fourth of the diesel fuel in China and play an important role in rural development. This paper is the first comprehensive assessment (in English or Chinese) of these vehicles and this remarkable industry. This study documents and analyzes vehicle technology, government policy, environmental impacts, market demand, and industry dynamics. We find that increasing government regulation (mostly for emissions and safety) is having profound effects on the industry, with uncertain implications for the sales and globalization of rural vehicle technology.