Electric Factor—A Comparison of Car Usage Profiles of Electric and Conventional Vehicles by a Probabilistic Approach (original) (raw)
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Rapid estimation of electric vehicle acceptance using a general description of driving patterns
Transportation Research Part C: Emerging Technologies, 2015
A reliable estimate of the potential for electrification of personal automobiles in a given region is dependent on detailed understanding of vehicle usage in that region. While broad measures of driving behavior, such as annual miles traveled or the ensemble distribution of daily travel distances are widely available, they cannot be predictors of the range needs or fuel-saving potential that influence an individual purchase decision. Studies that record details of individual vehicle usage over a sufficient time period are available for only a few regions in the US. In this paper we compare statistical characterization of four such studies (three in the US, one in Germany) and find remarkable similarities between them, and that they can be described quite accurately by properly chosen set of distributions. This commonality gives high confidence that ensemble data can be used to predict the spectrum of usage and acceptance of alternative vehicles in general. This generalized representation of vehicle usage may also be a powerful tool in estimating real-world fuel consumption and emissions.
Are cars used differently in Germany than in California? Findings from annual car-use profiles
The personal car is the most important mode of transport in most countries. Many policies are in place in different countries and regions to tackle unsustainable trends associated with car travel. A reason for the varying success of the same measure from one country to another might be different car-usage patterns. Using Germany and California as case studies to investigate differences and similarities in car use, we adapted the CUMILE model both for Germany and California in order to generate detailed profiles of car use over one year. Hierarchical cluster analysis subdivided the sample into clusters with similar car-usage characteristics. Then, we compared clusters of cars with similar usage between Germany and California in terms of cluster size, car properties and sociodemographic characteristics of their owners. The same eight car-usage clusters emerged in both study areas–with varying cluster sizes. We descriptively labeled the clusters: standing cars, moderate-range cars, day-today cars, workday cars, weekend cruisers, long-distance cars, short-haul cars and all-rounders. A better understanding of car-use patterns throughout a year and the size and characteristics of car-use clusters is beneficial for the identification of policies to make transport systems more sustainable.
Journal of Transportation Technologies, 2013
Plug-in electric vehicle (PHEV) technology is seen as promising technology for reducing oil use, improving local air quality, and/or possibly reducing GHG emissions to support a sustainable transportation system. This paper examines the usage of household vehicles to support assessment of the market potential of plug-in hybrid electric vehicles (PHEVs), the higher purchase price of which requires high usage rates to pay off the investment in the technology. According to the 2009 National Household Travel Survey (NHTS), about 40% of household vehicles were not used on the survey travel day [1]. This study analyzed household vehicle use and non-use by vehicle type, age, area type (metropolitan statistical area [MSA] and non-MSA), and population density. Vehicles used on survey day with or without a reported travel time and distance in the survey are considered "vehicles used". All others are referred to as "vehicles not used". We divided the "vehicles not used" into three categories: 1) left at home while other household vehicles were used; 2) not used because travelers used other modes; and 3) no household trips. The "vehicle used" consists of two categories: 1) those with distance and time data and 2) those with no travel data. Within these five categories, vehicles were subdivided according to four vehicle types: car, van, SUV, and pickup. Each vehicle type was further subdivided in two age groups: 10 years or less (≤10) and more than 10 years (>10). In addition, vehicle usage was compared in both MSAs and non-MSAs and during weekdays and weekends. Results indicate that most vehicles-especially pickups-are not used because the households own and use other vehicles. Moreover, SUVs-especially newer SUVs (≤10 years)-are the most utilized vehicle type and should be strongly considered as a primary vehicle type for PHEVs, in addition to cars.
Technological Forecasting and Social Change, 2014
This study is aimed at building a database of load profiles for electric-drive vehicles (EDVs) based on car use profiles in six European countries (Germany, Spain, France, Italy, Poland, and the United Kingdom). Driving profiles were collected by means of sample travel surveys carried out in the six countries. Here, we present the resulting load profiles obtained by associating assumptions on technical features of EDVs and on behavioural elements. The document provides details on the methodology and the assumptions used for driving profiles estimations, discusses the results of a common scenario for six countries and presents an alternative scenario to assess how load profiles might change under alternative parameters and assumptions. The report draws conclusions on this subject, and puts forward suggestions for follow-up studies. As the Commission's in-house science service, the Joint Research Centre's mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multidisciplinary approach.
Journal of Modern Power Systems and Clean Energy, 2015
Electic vehicles (EVs) show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid. Furthermore, EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts on power systems. This paper presents a methodology to transform driving behavior of person into one of the cars in order to analyze the driving pattern of EVs based on the National Travel Surveys. In the proposed methodology, a statistical process is used to obtain the driving behavior of cars by grouping the survey respondents according to the driving license number and car number, and mapping the households with similar characteristics. The proposed methodology was used to carry out the driving pattern analysis in the Nordic region. The detailed driving requirements and charging/discharging availability of vehicles along the day were obtained. Two types of EV availabilities were studied in this paper considering different charging/discharging conditions of EVs for the power system integration, i.e. EV availability all day and EV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.
Energies
This study describes, applies, and compares three different approaches to integrate electric vehicles (EVs) in a cost-minimising electricity system investment model and a dispatch model. The approaches include both an aggregated vehicle representation and individual driving profiles of passenger EVs. The driving patterns of 426 randomly selected vehicles in Sweden were recorded between 30 and 73 days each and used as input to the electricity system model for the individual driving profiles. The main conclusion is that an aggregated vehicle representation gives similar results as when including individual driving profiles for most scenarios modelled. However, this study also concludes that it is important to represent the heterogeneity of individual driving profiles in electricity system optimisation models when: (i) charging infrastructure is limited to only the home location in regions with a high share of solar and wind power in the electricity system, and (ii) when addressing spe...
Statistical analysis of the predictors of annual electric vehicle mileage
Bulletin of Electrical Engineering and Informatics, 2023
This study evaluates the impact of technical and economic factors related to electric vehicles and the impact of socio-demographic factors related to electric vehicle owners on annual electric vehicle mileage from a statistical perspective. The data set was analyzed using regression and correlation analyses using Ms Excel and several Python libraries. The influence of the socio-demographic characteristics of the respondents was estimated as minimal and requiring reassessment. It was shown that among the sociodemographic factors considered, only the age of vehicle owners correlates with the annual mileage of electric vehicles. It is shown that technical and economic parameters are much more closely related to the annual mileage of electric cars than socio-demographic parameters. Significant factors among the technical and economic ones were battery capacity, power consumption of the electric car, and the size of the respondent's locality.
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