Researchers are developing an eco-routing navigation system that can extend the range of electric vehicles by at least ten percent. The system will consider real-time traffic information, road type and grade, and passenger and cargo weight.
Guoyuan Wu, from the University of California, Riverside, heads the research. His co-investigators are Matthew Barth and Kanok Boriboonsomsin. They are conducting their investigation at the Center for Environmental Research and Technology.
The California Energy Commission provided the funding for the research. The commission gave a $95,000 one-year grant for the development of an eco-routing algorithm that finds the route requiring the least amount of energy for a trip.
The current project builds upon the previous research of Barth and Boriboonsomsin. They previously reported that an eco-routing navigation system could reduce the fuel consumption and greenhouse gas emission of a fossil fuel-powered vehicle by as much as 5 to 15 percent.
Wu says the present research will target electric vehicles, where it will have the greatest impact given the limited range of these vehicles.
Manufacturers usually estimate the range of electric vehicles to be 100 miles or more. However, the range can vary drastically according to EPA testing. It depends on several driving conditions such as air temperature, traffic congestion and road grade. For instance, the Nissan LEAF vehicle has a range that varies from 47 to 138 miles.
The last decade had seen the proliferation of GPS-guide navigation systems to find routes that minimize distance to be travelled. In most cases, these routes do not minimize energy consumption and exhaust emission as well.
This is because distance is not the only factor that determines energy consumption. Other factors are equally as important.
Traffic condition is one such factor. The stop-and-go movements in congested traffic waste fuel.
Another factor is road type. Driving patterns depend on the type of road. Driving in highways involve cruising at high speeds. In contrast, driving in surface streets involve more idling and frequent stops due to traffic signs and stop signs.
Road grade is another consideration. Climbing a steep road requires more power to overcome gravitational forces. This increases fuel consumption.
The weight load is likewise important. The greater the weight, the greater the power and energy required.
Weather conditions also have a direct and indirect impact. Headwind increases energy consumption since the vehicle needs more power to fight the wind drag. In a similar manner, the use of heater on cold weather or air condition in hot weather consumes fuel.
The researchers need to gather energy consumption data when an electric vehicle is driven under real-world driving conditions that will take into account all these factors. Then, they have to create tables from these data to develop real-time energy consumption estimate models for the test electric vehicle. Next, they have to integrate these models into an eco-routing algorithm that will be incorporated into a prototype eco-routing navigation system. Finally, they have to test the prototype using the test electric vehicle.