By driving smarter, autonomous cars have the potential to move people around and between cities with far greater efficiency. Estimates of their energy dividends, however, have largely ignored autonomous driving’s energy inputs, such as the electricity consumed by brawny on-board computers.
First-of-a-kind modeling published today by University of Michigan and Ford Motor researchers shows that autonomy's energy pricetag is substantial — high enough to turn some autonomous cars into net energy losers.
"We knew there was going to be a tradeoff in terms of the energy and greenhouse gas emissions associated with the equipment and the benefits gained from operational efficiency. I was surprised that it was so significant,” says to Greg Keoleian, senior author on the paper published today in the journal Environmental Science & Technology and director of the University of Michigan Center for Sustainable Systems.
Keoleian’s team modeled both conventional and battery-electric versions of Ford's Focus sedan carrying sensing and computing packages that enable them to operate without human oversight under select conditions. Three subsystems were studied: small and medium-sized equipment packages akin to those carried by Tesla's Model S and Ford's autonomous vehicle test platform, respectively, and the far larger package on Waymo's Pacifica minivan test bed [photo above].
For the small and medium-sized equipment packages, going autonomous required 2.8 to 4.0 percent more onboard power. This went primarily to power the computers and sensors, and secondarily to the extra 17-22 kilograms of mass the equipment contributed.
However, autonomy’s energy bill ate up only part of the overall energy reduction expected from the autonomous vehicles’ ability to drive smarter driving — such as platooning of vehicles through intersections and on highways to cut congestion in cities and aerodynamic drag on the highway. As a result the modeled Ford sedans still delivered a 6-9 percent net energy reduction over their life cycle with autonomy added, and promised a comparable reduction in greenhouse gas emissions.
EV and gas models offered comparable results. Adding equipment was less burdensome for the EVs, which provided extra power for the processors and sensors more efficiently than a gas vehicle. But autonomy delivered a slightly larger net energy reduction in the gas vehicles, whose relatively inefficient drivetrains should benefit more from smart driving.
In contrast adding the large Waymo equipment package yielded a comparatively dark picture for the modeled EVs and gasoline-fueled sedans. The larger equipment increased net energy consumption on the Ford sedans by 5 percent, thanks mostly to the aerodynamic drag induced by its rooftop sensors.
Keoleian says this modeling result likely overstates real impacts from future autonomous vehicles, which he expects will manage to streamline even substantial sensors arrays. What concerns him more is the likelihood that all of the modeled packages understate power consumption by future autonomous driving subsystems.
For instance, Keoleian says future autonomous vehicles may employ street maps of far higher resolution than those used today to ensure the safety of pedestrians, cyclists and other drivers. In fact, real-time updating of high-definition maps by autonomous cars is one of the applications pushing the development of next-generation 5G wireless data networks.
Higher-bandwidth data transmission via today's 4G network could boost power consumption by onboard computers by one third or more according to Keoleian and his coauthors. It is premature, they write in today's study, to judge the power consumption associated with 5G.
Another concern for Keoleian are the indirect effects of introducing autonomous vehicles. By making driving more convenient, for example, smart cars could encourage longer commutes. "There could be a rebound effect. They could induce travel, adding to congestion and fuel use,” says Keoleian.
Such indirect effects of smart cars could either slash energy consumption from driving by 60 percent, or increase it by 200 percent, according to a 2016 study by the U.S. National Renewable Energy Laboratory. Guiding the technology’s development to avoid an energy demand explosion, says Keoleian, will require a lot more study.