Identifying and Recognizing Usage Pattern of Electric Vehicles Using GPS and On-Board Diagnostics Data (original) (raw)
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Travel Behavior and Transportation Systems Analysis of Electric Vehicles
Journal of Advanced Transportation, 2018
Electrification of personal transportation is widely regarded as an effective solution to relieve some increasingly serious crises facing our society today and in the near future, such as energy security, climate change and air quality. Depending on the type of power sources, electric vehicles (EVs) may be categorized into hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). While HEVs and PHEVs may better satisfy people’s travel need nowadays, especially for long-distance trips, it is generally believed that BEVs will most probably dominate the EV market, after charging infrastructures are sufficiently expanded and charging time is reduced to a satisfactorily short level. Partially for this reason, an increasing number of EV-related scientific studies have focused on BEVs in recent years. The last decade observed a fast climb of the market penetration level of EVs in many economies, especially in China, the United States and the European Union. The increasing global market share leads to a series of new engineering, economic, environmental and institutional problems and concerns we have not dealt with in our past transportation systems development and management experience, such as electricity-charging infrastructure planning for EVs, EV-based travel and charging demand analysis, EVs’ energy consumption and cost analysis, EVs’ market penetration forecasting, air quality and environment improvements due to EV adoption, and so on. These new problems and concerns intrigued numerous attention from the research community and general public. Moreover, with the continuous advances in EV technologies and increasing willingness of consumers purchasing and utilizing EVs, the mechanism, means and magnitude of these impacts on transportation systems as well as their evolution have developed to a highly complex and unprecedented level that we might have underestimated before, if we still made judgments by fully relying on our existing knowledge. In additional to electric cars, electrification of transportation is also reflected by the increasing penetration of electric bicycles, which now are widely adopted in many large cities with high population density, especially in Asian and European countries. Their influences on urban travel behaviors and transportation planning are not fully investigated yet and worth being further discovered.
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.
Analyzing the Travel and Charging Behavior of Electric Vehicles - A Data-driven Approach
2021
The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.
World Electric Vehicle Journal
To counteract climate change, electric vehicles are replacing vehicles with internal combustion engine on the automotive market. Therefore, electric vehicles must be accepted and used like conventional vehicles. This study aims to investigate to which extent electric vehicles are already being used like conventional vehicles. To do this, we present a supervised method where we combine usage data from conventional vehicles (from car use model based on survey data) and electric vehicles (from sensor data) in Germany and California. Based on conventional vehicles, eight car usage profiles were defined by hierarchical clustering in a previous study. Using a softmax regression, we estimate for each electric vehicle a probability of assignment for every car usage profile. Comparison of conventional and electric vehicles with a high probability reveals that electric vehicles are used similar for long-distance travel (>100 km) and different for short-distance travel (<10 km) to conven...
Usage Profile Rating of Suitability to E-Vehicles Utilizing a Physical Consumption Model
Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, 2018
The project "Wohnungswirtschaftlich integrierte netzneutrale Elektromobilitat in Quartier und Region" (WINNER) aims to integrate shared electric vehicles, smart local grids and renewable energy in tenant households. This paper focuses on how to find the model of an electric vehicle (consumption, recharging, usage) which perfectly matches the requirements of particular carsharing stations. This approach utilizes usage profiles of conventional combustion vehicles. Each profile describes booking time and distance. Applying that information to a rating model which simulates the driving task and charges the vehicle between usages should be able to tell how much bookings might be handled by an electric vehicle. Within this paper, we give an introduction to our simulation system. This covers the data model, transforming bookings into driving tasks, and the consumption and charging model itself. Further, we validate the model by using high detailed data captured on regular routes as well as booking sets with electric vehicles. This validation shows an average relative error of 10 % for high detailed data from and an average relative error for booking information with known consumptions of 5 %. Finally, we present the application of our simulation system to make a decision based on historical booking information. This application example shows that 90 % usages at some station might be handled with electric vehicles, while others should not be replaced.
Battery electric vehicle Range anxiety GPS-based travel survey Genetic algorithm a b s t r a c t This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.
10 Electric Vehicles and Driving Patterns
2015
The magnitude of personal travel in the industrialised society has increased tremendously. In Sweden, the average travelled distance per person is currently around 40 km per day (see Figure 10.1). Travelling distances are related to speed. Studies show that the time spent on travel tends to be relatively constant at around one hour a day on average. The historical development of new transport technology such as trains and cars has increased the speed and comfort of travelling, and hence also the distances travelled. Airplanes make it convenient to travel even up to tens of thousands of kilometres for business meetings, visits or vacation. Globalised industries and charter tourism rely to a large extent on the speed of airplanes. And increased globalisation spurs further demand for air traffic.
Analysis of Electric Vehicle Usage of a Hyundai Santa Fe Fleet in Hawaii
Journal of Asian Electric Vehicles, 2005
Here we report the analysis of vehicle usage of a fleet of 15 Hyundai Santa Fe electric-sport-utility-vehicles (e-SUVs), which was tested in Honolulu, Hawaii, from July 2001 to June 2003. The 15 vehicles were dispatched to the Hickam Air Force Base (HAFB), City and County (C&C) of Honolulu, Hawaiian Electric Co. (HECO), and the Hawaii Electric Vehicle Demonstration Project (HEVDP) office for field evaluation. More than 25,000 trips were recorded using on-board data acquisition systems in all vehicles during the two-year period, representing a total driving distance of more than 255,000 km. We used a systematic approach to conduct driving cycle analysis (DCA) from the second-by-second trip data. Detailed breakdown of the driving cycles in terms of driving patterns was generated and summarized as functions of vehicle operating time and mileage for each vehicle over the evaluation period. In this paper, we illustrate how to analyze the vehicle usage from such a DCA and the real-life data in the database. The vehicle usage analysis (VUA) includes frequency and extent of vehicle operation in addition to the DCA. We intend to correlate vehicle performance via DCA and VUA for comparison among different operating organizations to allow us develop a more effective fleet operation in the future.
Passenger Car Energy Demand Assessment: a New Approach Based on Road Traffic Data
E3S Web of Conferences
Nowadays the automotive market is oriented to the production of hybrid or electric propulsion vehicle equipped with Energy Management System that aims to minimize the consumption of fossil fuel. The EMS, generally, performs a local and not global optimization of energy management due to the impossibility of predicting the user’s energy demand and driving conditions. The aim of this research is to define a driving cycle (speed time) knowing only the starting and the arrival point defined by the driver, considering satellite data and previous experiences. To achieve this goal, the data relating to the energy expenditure of a car (e.g. speed, acceleration, road inclination) will be acquired, using on-board acquisition system, during road sections in the city of Messina. At the same time, the traffic level counterplot and others information provided, for these specific sections, from GPS acquisition software will be collected. On-board and GPS data will be compared and, after considerin...
Determining Electric Vehicle Charging Point Locations Considering Drivers’ Daily Activities
Procedia Computer Science, 2014
In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) microsimulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day. Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity. From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours. Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed .