A Vehicle Use Profile was generated based on relevant literature.
Significant growth in the electric vehicle market is predicted over the next few decades as EV range and charging infrastructure improve, although the size and configuration of EV market share is difficult to predict and will be based upon a range of technical, social, economic and environmental factors (Shahidinejad et al, 2012).
One of the issues we faced in this project was the lack of driving and car use data for plug in electric vehicles, and little published empirical analysis of driver range requirements (Pearre et al, 2011). It was therefore necessary to gain an understanding of when and how conventional vehicles are used in order to create a typical car use profile for our vehicle to home scenarios. We have attempted to use the available information in the literature to generate a representative profile of car use (departure and arrival time, distance driven and SOC). Using daily distance travelled distributions from the work of Ashtari (et al, 2012) and Pearre (et al, 2011), we created a profile based on the following assumptions on daily driving range and no. of days a year this range occurs).
Charging each EV battery creates an additional load on the electrical network, but there is significant potential for these batteries to act as a responsive load through intelligent charging, assisting in the demand side management of the larger electricity network (Huang and Infield, 2009). Huang and Infield (2009) extracted valuable information on car use from the Time of Use UK Survey 2000 (2003). Analysis indicated that privately owned cars are only utilized 5.2% of the time. This leaves 94.8% of the time when the EV could potentially be available for energy storage, load control (controlled charging) and brief discharge purposes during peak demand periods. The average ownership of a vehicle is one per household, based on the group sampled. Weekdays and weekends are treated separately. Through the allocation of driving codes for each 10 minute interval, the probability of a car being driven at a particular time could be calculated. The main findings were that peak driving time on weekdays was over the period 7:30am to 9:20am and 16:45pm to 18:40pm, associated with the commute to workplace. The pattern over the weekend was that driving was more widely spread over the whole day. Stats also show that over half of the vehicles are parked at home and available early evening and during the night. This provides the opportunity for the batteries to discharge into network, offsetting other loads on the system.
Pearre (et al, 2011) collected vehicle use data using GPS data logging, from a sample of 484 conventional vehicles over the course of 1 to 3 years. Their main findings were 1) that the vast majority of daily range needs are between 0 to 50 miles, 2) that even during a weekday rush hour, around 85% of vehicles are parked, 3) the number of vehicles parked increases steadily to 98.5% by midnight, 4) during the hours of 12.10am and 5.50am, 99% of vehicles are parked, on both weekdays and weekends.
One of main objectives of the project was to ensure the vehicle was available for use as a mode of transport at all times. This meant the driver had to be confident there would be enough charge to complete the next journey. It is necessary to leave a small surplus driving range available to accommodate error or unplanned side trips, preventing “range anxiety”. It has been expressed in the literature that 20 miles provides a suitable range buffer (Pearre et al, 2011). A third study conducted in Winnipeg, Canada recorded vehicle use data using GPS logging on a per second basis for a period of 1 year. The sample size was 76 vehicles (Ashtari et al, 2012)
Shahidinejad (et al, 2012), assumed a charging efficiency of 90% in their vehicle study, including ac/dc converter and charger circuitry, and losses from wall plug to battery pack. We have assumed the same efficiency in our own battery model
Like other studies in the literature, we have assumed that driver lifestyle and driving behavior are not affected by the electrification of vehicles (Shahidinejad et al, 2012, Ashtari et al, 2012). Our project has focused on vehicle to home scenarios, although charging occurs primarily at off peak times via the grid. Pearre (et al, 2011) made a similar assumption in their assessment of daily driving distances, conservatively assuming a single charging event at night, and a fully charged battery in the morning. This is probably the most likely scenario for early adopters of EV’s due to limited public charging infrastructure, lengthy charge times (due to battery capability), and to ensure a full battery each morning. It is also important to know duration of use and distance covered, how much energy is consumed moving vehicle over travel distance, the impact this has on on SOC at plug in, and amount of energy required to charge battery at a particular time of day (at home, at work, on the road).
Although we do not consider the impact of this type of cycling on battery life, we do recognize there will be a reduction in lifetime. However, there is still very little in the way of literature to indicate what the likely impact will be. However, using the battery in this way provides a triple purpose which may make this expensive energy storage option more appealing.
Real world, high resolution data and fact based models of electric vehicle use are becoming a necessity in order for utility companies to be able to simulate and accurately predict the impacts of EV’s on the network, and to optimize required investments (Ashtari et al, 2012). Driving habits and road conditions also greatly affect the range of an electric vehicle. It is possible that future EV’s will have in-car feedback instructions for drivers to optimize their driving range.
Ashtari, A., Bibeau, E., Shahidinejad, S., Molinski, T. 2012. PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data. IEEE Transactions on Smart Grid. Vol. 3, No. 1, pp. 341-350.
Huang, S., Infield, D. The potential of domestic electric vehicles to contribute to Power System Operation through vehicle to grid technology. 2009. Proceedings of the 44th International Universities Power Engineering Conference (UPEC), pp. 1-5.
Pearre, N.S., Kempton, W., Guensler, R.L., Elango, V.V. 2011. Electric vehicles: How much range is required for a day’s driving? Transport Research Part C. 19, pp. 1171-1184.
Shahidinejad, S., Filizadeh, S., Bibeau, E. 2012. Profile of Charging Load on the Grid Due to Plug-in Vehicles. IEEE Transactions on Smart Grid, Vol. 3, No. 1, pp. 135-141.
The United Kingdom 2000 Time of Use Survey Technical Report. 2003. HMSO. Available online. [http://www.ons.gov.uk/ons/search/index.html?pageSize=50&newquery=UK+TUS+2000+Technical+Report] Accessed 3 May 2012
Total annual mileage: 10240 miles.