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Using Big Data to Fight Range Anxiety in Electric Vehicles

New software uses predictive data to make better guesses about when the juice will run out

2 min read
Using Big Data to Fight Range Anxiety in Electric Vehicles
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One of the biggest obstacles preventing more widespread adoption of electric vehicles is “range anxiety,” the fear of losing power and seeing your car shut down in the middle of a long-distance drive. Current technologies that estimate how much longer a battery will last still provide inaccurate measurements, because they use computer models that rely heavily on the driver’s recent behavior and don’t account for other factors.

Mo-Yuen Chow and Habiballah Rahimi-Eichi from North Carolina State University’s Advanced Diagnosis, Automation, and Control Lab think they have developed a better model. In a new paper that will be presented at the 40th Annual Conference of the IEEE Industrial Electronics Society, the researchers describe new software that uses a “big data” approach to gather information from multiple sources in order to estimate electric vehicle range. The driver needs only to provide a destination address or GPS coordinates, and the software combines historical data along with “predictive” data—variables such as traffic data, highway and surface-road characteristics, and even weather—to determine how much longer a driver can go before the batteries are tapped out.

“We’re not simply feeding data acquired from the last 5 or 10 minutes of driving, the way most estimation software has,” says Rahimi-Eichi. “We’re looking at what the next 5 minutes, 10 minutes, and more, look like for the car, and predicting what the car will do.”

The software acquires data from five sources: Google Maps (for route, terrain, and traffic data), Wunderground.com (for weather), driver history (through driving behavior measurements), vehicle manufacturers (for vehicle modeling data), and battery manufacturers (for battery modeling data). 

Rahimi-Eichi points out that the software makes heavy use of data already available online. But its algorithms gather and analyze the information more effectively to improve range estimations. The system still needs to be tested in actual electric vehicles, but the researchers say that in simulations their code predicted a car’s range with 95 percent accuracy.

Past research has been aimed at alleviating range anxiety through other means, like speed management and optimized braking systems. Rahimi-Eichi says his team is now working on more extensive simulations and tests, and hopes the system could be commercialized within two years.

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We Need More Than Just Electric Vehicles

To decarbonize road transport we need to complement EVs with bikes, rail, city planning, and alternative energy

11 min read
A worker works on the frame of a car on an assembly line.

China has more EVs than any other country—but it also gets most of its electricity from coal.

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Green

EVs have finally come of age. The total cost of purchasing and driving one—the cost of ownership—has fallen nearly to parity with a typical gasoline-fueled car. Scientists and engineers have extended the range of EVs by cramming ever more energy into their batteries, and vehicle-charging networks have expanded in many countries. In the United States, for example, there are more than 49,000 public charging stations, and it is now possible to drive an EV from New York to California using public charging networks.

With all this, consumers and policymakers alike are hopeful that society will soon greatly reduce its carbon emissions by replacing today’s cars with electric vehicles. Indeed, adopting electric vehicles will go a long way in helping to improve environmental outcomes. But EVs come with important weaknesses, and so people shouldn’t count on them alone to do the job, even for the transportation sector.

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