PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data
- 12 September 2011
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Smart Grid
- Vol. 3 (1), 341-350
- https://doi.org/10.1109/tsg.2011.2162009
Abstract
Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to address new electric vehicle loads for addressing energy drivers.Keywords
This publication has 14 references indexed in Scilit:
- Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving CycleTransportation Science, 2014
- Electric vehicles: Holy grail or fool's goldPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Assessment of the Impact of Plug-in Electric Vehicles on Distribution NetworksIEEE Transactions on Power Systems, 2010
- Scenario analysis on alternative fuel/vehicle for China’s future road transport: Life-cycle energy demand and GHG emissionsEnergy Policy, 2010
- Grid impacts of plug-in electric vehicles on Hydro Quebec's distribution systemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution GridIEEE Transactions on Power Systems, 2009
- Analyzing the impacts of plug-in electric vehicles on distribution networks in British ColumbiaPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid LimitationsIEEE Transactions on Industrial Electronics, 2009
- Grid of the futureIEEE Power and Energy Magazine, 2009
- Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid ServicesEnvironmental Science & Technology, 2009