GPS-like eco-routing device may extend range of electric vehicles
July 18, 2012 – Researchers at the University of California, Riverside, believe they can extend the range of electric vehicles (EVs) by at least 10% by taking into account real-time traffic information, road type and grade, and passenger and cargo weight—a.k.a. eco-routing.
The researchers, who work at the Center for Environmental Research and Technology (CE-CERT)—part of the Bourns College of Engineering—have received a nearly $95,000, one-year grant from the California Energy Commission to develop an eco-routing algorithm that finds the route requiring the least amount of energy for a trip.
“This is particularly useful given the limited range of electric vehicles,” said Guoyuan Wu, an assistant researcher at CE-CERT and the principal investigator on the project. “It should really help cut down on what has become known as range anxiety.”
Wu will be assisted by co-principal investigators Matthew Barth, director of CE-CERT and the Yeager Families Professor of Engineering, and Kanok Boriboonsomsin, a research faculty member at CE-CERT.
The work on EVs builds upon research by Barth and Boriboonsomsin. They found eco-routing navigation systems can potentially reduce fuel consumption and greenhouse gas emissions in fossil fuel-powered vehicles by 5-15%.
Most EVs have a manufacturer-estimated range of 100 miles or greater, say the researchers; however, range can vary drastically. For example, the range of the Nissan LEAF may vary between 47 and 138 miles depending on driving conditions, such as air temperature, traffic congestion and road grade, according to EPA testing.
In the last decade, there has been a proliferation of GPS-guided navigation systems that assist drivers on which routes to take to their destination. Most attempt to minimize distance travelled but, in many cases, that route doesn’t minimize energy consumption or emissions. Newer generation navigation systems use predicted travel time. But, even the shortest-time route doesn’t ensure the minimum energy consumption or emissions.
A number of factors affect vehicle energy consumption, including:
• Traffic conditions: Stop-and-go movement in congested traffic wastes fuel. So, the vehicle energy consumption increases significantly under this traffic condition.
• Road type: Driving patterns on different road types vary. For example, driving on highways often involves cruising at higher speeds. Driving on surface streets often involves more frequent stops due traffic signals, stop signs and increased idling. These differences have significant impacts on vehicle energy consumption.
• Road grade: Climbing a steep grade requires more power from the engine to overcome the added gravitational force. This increases vehicle energy consumption.
• Weight: A vehicle carrying more weight requires more energy to run, thereby impacting its energy consumption rate.
• Weather conditions: Weather conditions have direct and indirect impact on energy consumption. For instance, headwind increases vehicle energy consumption as the vehicle needs additional power to combat drag. Using the heater or air-conditioner also increases energy consumption.
With the grant, energy consumption data will be collected when an EV is driven under a variety of real-world driving conditions, including different vehicle speeds, traffic congestion levels, road types, and road grades, with varying number of passengers.
Tables created from the data will be used to develop real-time energy consumption estimate models for a test EV. The models will then be integrated into an eco-routing algorithm. The algorithm will then be incorporated into a prototype eco-routing navigation system, which will resemble a small computer screen and be placed on the dashboard. Once in place, testing using an electric vehicle will begin.