Although the results presented in this paper are good indicators for driving behavior of a large population, they cannot be applied to individual people or households. Besides that, driving velocity, use of climate control and other range influencing factors were not captured by the survey and these were shown to greatly influence range.
Further research will focus on expanding the car usage patterns, by specifying parking spots (home, workplace) which are likely to adapt charging stations early on in the implementation process. This will increase accuracy of electricity load prediction.
In terms of range analysis, an average driving velocity may help in estimating consumption. Trips of longer distance are likely to take place on highways, therefore effectively increasing energy consumption per mile (as shown in section 5). Average velocity can be derived from the NHTS database as distance and duration are recorded for each trip. Also, additional analyses may be done on households with more than one car, as simple decisions may be made on: ‘who takes the electric car today?’, to assure that trips with a range beyond the EV’s driving range are made by the gasoline car in the family.
Another useful expansion on Section 5 of this paper would be to build a personal ‘EV range-assessment tool’. A Smartphone or GPS-device may be used to collect people’s car coordinates during trips, of which the data can be used to estimate velocity and acceleration for each timestamp and consequently the EV battery consumption of the whole trip. Additional data that can be used in this model would be:
This tool may help future car-buyers to assess whether an EV would fit their needs, and what range the vehicle needs to have. Also, it could estimate the payback time for the EV or PHEV and for the people who are interested, the reduction in their carbon footprint.