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      in Energy

      Computers that power self-driving cars could be a huge driver of global carbon emissions

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      In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

      That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

      The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

      The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

      “If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

      Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

      Modeling emissions

      The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a back-up human driver.

      The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

      “On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

      For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

      In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn’t exist yet.

      To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

      When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

      For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

      “After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

      Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

      Keeping emissions in check

      To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

      One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

      In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

      Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

      While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

      “We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

      This research was funded, in part, by the National Science Foundation and the MIT-Accenture Fellowship. More

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      in Environment

      New maps show airplane contrails over the U.S. dropped steeply in 2020

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      As Covid-19’s initial wave crested around the world, travel restrictions and a drop in passengers led to a record number of grounded flights in 2020. The air travel reduction cleared the skies of not just jets but also the fluffy white contrails they produce high in the atmosphere.

      MIT engineers have mapped the contrails that were generated over the United States in 2020, and compared the results to prepandemic years. They found that on any given day in 2018, and again in 2019, contrails covered a total area equal to Massachusetts and Connecticut combined. In 2020, this contrail coverage shrank by about 20 percent, mirroring a similar drop in U.S. flights.  

      While 2020’s contrail dip may not be surprising, the findings are proof that the team’s mapping technique works. Their study marks the first time researchers have captured the fine and ephemeral details of contrails over a large continental scale.

      Now, the researchers are applying the technique to predict where in the atmosphere contrails are likely to form. The cloud-like formations are known to play a significant role in aviation-related global warming. The team is working with major airlines to forecast regions in the atmosphere where contrails may form, and to reroute planes around these regions to minimize contrail production.

      “This kind of technology can help divert planes to prevent contrails, in real time,” says Steven Barrett, professor and associate head of MIT’s Department of Aeronautics and Astronautics. “There’s an unusual opportunity to halve aviation’s climate impact by eliminating most of the contrails produced today.”

      Barrett and his colleagues have published their results today in the journal Environmental Research Letters. His co-authors at MIT include graduate student Vincent Meijer, former graduate student Luke Kulik, research scientists Sebastian Eastham, Florian Allroggen, and Raymond Speth, and LIDS Director and professor Sertac Karaman.

      Trail training

      About half of the aviation industry’s contribution to global warming comes directly from planes’ carbon dioxide emissions. The other half is thought to be a consequence of their contrails. The signature white tails are produced when a plane’s hot, humid exhaust mixes with cool humid air high in the atmosphere. Emitted in thin lines, contrails quickly spread out and can act as blankets that trap the Earth’s outgoing heat.

      While a single contrail may not have much of a warming effect, taken together contrails have a significant impact. But the estimates of this effect are uncertain and based on computer modeling as well as limited satellite data. What’s more, traditional computer vision algorithms that analyze contrail data have a hard time discerning the wispy tails from natural clouds.

      To precisely pick out and track contrails over a large scale, the MIT team looked to images taken by NASA’s GOES-16, a geostationary satellite that hovers over the same swath of the Earth, including the United States, taking continuous, high-resolution images.

      The team first obtained about 100 images taken by the satellite, and trained a set of people to interpret remote sensing data and label each image’s pixel as either part of a contrail or not. They used this labeled dataset to train a computer-vision algorithm to discern a contrail from a cloud or other image feature.

      The researchers then ran the algorithm on about 100,000 satellite images, amounting to nearly 6 trillion pixels, each pixel representing an area of about 2 square kilometers. The images covered the contiguous U.S., along with parts of Canada and Mexico, and were taken about every 15 minutes, between Jan. 1, 2018, and Dec. 31, 2020.

      The algorithm automatically classified each pixel as either a contrail or not a contrail, and generated daily maps of contrails over the United States. These maps mirrored the major flight paths of most U.S. airlines, with some notable differences. For instance, contrail “holes” appeared around major airports, which reflects the fact that planes landing and taking off around airports are generally not high enough in the atmosphere for contrails to form.

      “The algorithm knows nothing about where planes fly, and yet when processing the satellite imagery, it resulted in recognizable flight routes,” Barrett says. “That’s one piece of evidence that says this method really does capture contrails over a large scale.”

      Cloudy patterns

      Based on the algorithm’s maps, the researchers calculated the total area covered each day by contrails in the US. On an average day in 2018 and in 2019, U.S. contrails took up about 43,000 square kilometers. This coverage dropped by 20 percent in March of 2020 as the pandemic set in. From then on, contrails slowly reappeared as air travel resumed through the year.

      The team also observed daily and seasonal patterns. In general, contrails appeared to peak in the morning and decline in the afternoon. This may be a training artifact: As natural cirrus clouds are more likely to form in the afternoon, the algorithm may have trouble discerning contrails amid the clouds later in the day. But it might also be an important indication about when contrails form most. Contrails also peaked in late winter and early spring, when more of the air is naturally colder and more conducive for contrail formation.

      The team has now adapted the technique to predict where contrails are likely to form in real time. Avoiding these regions, Barrett says, could take a significant, almost immediate chunk out of aviation’s global warming contribution.  

      “Most measures to make aviation sustainable take a long time,” Barrett says. “(Contrail avoidance) could be accomplished in a few years, because it requires small changes to how aircraft are flown, with existing airplanes and observational technology. It’s a near-term way of reducing aviation’s warming by about half.”

      The team is now working towards this objective of large-scale contrail avoidance using realtime satellite observations.

      This research was supported in part by NASA and the MIT Environmental Solutions Initiative. More

    • 113 Shares149 Views

      in Energy

      Preparing global online learners for the clean energy transition

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      After a career devoted to making the electric power system more efficient and resilient, Marija Ilic came to MIT in 2018 eager not just to extend her research in new directions, but to prepare a new generation for the challenges of the clean-energy transition.

      To that end, Ilic, a senior research scientist in MIT’s Laboratory for Information and Decisions Systems (LIDS) and a senior staff member at Lincoln Laboratory in the Energy Systems Group, designed an edX course that captures her methods and vision: Principles of Modeling, Simulation, and Control for Electric Energy Systems.

      EdX is a provider of massive open online courses produced in partnership with MIT, Harvard University, and other leading universities. Ilic’s class made its online debut in June 2021, running for 12 weeks, and it is one of an expanding set of online courses funded by the MIT Energy Initiative (MITEI) to provide global learners with a view of the shifting energy landscape.

      Ilic first taught a version of the class while a professor at Carnegie Mellon University, rolled out a second iteration at MIT just as the pandemic struck, and then revamped the class for its current online presentation. But no matter the course location, Ilic focuses on a central theme: “With the need for decarbonization, which will mean accommodating new energy sources such as solar and wind, we must rethink how we operate power systems,” she says. “This class is about how to pose and solve the kinds of problems we will face during this transformation.”

      Hot global topic

      The edX class has been designed to welcome a broad mix of students. In summer 2021, more than 2,000 signed up from 109 countries, ranging from high school students to retirees. In surveys, some said they were drawn to the class by the opportunity to advance their knowledge of modeling. Many others hoped to learn about the move to decarbonize energy systems.

      “The energy transition is a hot topic everywhere in the world, not just in the U.S.,” says teaching assistant Miroslav Kosanic. “In the class, there were veterans of the oil industry and others working in investment and finance jobs related to energy who wanted to understand the potential impacts of changes in energy systems, as well as students from different fields and professors seeking to update their curricula — all gathered into a community.”

      Kosanic, who is currently a PhD student at MIT in electrical engineering and computer science, had taken this class remotely in the spring semester of 2021, while he was still in college in Serbia. “I knew I was interested in power systems, but this course was eye-opening for me, showing how to apply control theory and to model different components of these systems,” he says. “I finished the course and thought, this is just the beginning, and I’d like to learn a lot more.” Kosanic performed so well online that Ilic recruited him to MIT, as a LIDS researcher and edX course teaching assistant, where he grades homework assignments and moderates a lively learner community forum.

      A platform for problem-solving

      The course starts with fundamental concepts in electric power systems operations and management, and it steadily adds layers of complexity, posing real-world problems along the way. Ilic explains how voltage travels from point to point across transmission lines and how grid managers modulate systems to ensure that enough, but not too much, electricity flows. “To deliver power from one location to the next one, operators must constantly make adjustments to ensure that the receiving end can handle the voltage transmitted, optimizing voltage to avoid overheating the wires,” she says.

      In her early lectures, Ilic notes the fundamental constraints of current grid operations, organized around a hierarchy of regional managers dealing with a handful of very large oil, gas, coal, and nuclear power plants, and occupied primarily with the steady delivery of megawatt-hours to far-flung customers. But historically, this top-down structure doesn’t do a good job of preventing loss of energy due to sub-optimal transmission conditions or due to outages related to extreme weather events.

      These issues promise to grow for grid operators as distributed resources such as solar and wind enter the picture, Ilic tells students. In the United States, under new rules dictated by the Federal Energy Regulatory Commission, utilities must begin to integrate the distributed, intermittent electricity produced by wind farms, solar complexes, and even by homes and cars, which flows at voltages much lower than electricity produced by large power plants.

      Finding ways to optimize existing energy systems and to accommodate low- and zero-carbon energy sources requires powerful new modes of analysis and problem-solving. This is where Ilic’s toolbox comes in: a mathematical modeling strategy and companion software that simplifies the input and output of electrical systems, no matter how large or how small. “In the last part of the course, we take up modeling different solutions to electric service in a way that is technology-agnostic, where it only matters how much a black-box energy source produces, and the rates of production and consumption,” says Ilic.

      This black-box modeling approach, which Ilic pioneered in her research, enables students to see, for instance, “what is happening with their own household consumption, and how it affects the larger system,” says Rupamathi Jaddivada PhD ’20, a co-instructor of the edX class and a postdoc in electrical engineering and computer science. “Without getting lost in details of current or voltage, or how different components work, we think about electric energy systems as dynamical components interacting with each other, at different spatial scales.” This means that with just a basic knowledge of physical laws, high school and undergraduate students can take advantage of the course “and get excited about cleaner and more reliable energy,” adds Ilic.

      What Jaddivada and Ilic describe as “zoom in, zoom out” systems thinking leverages the ubiquity of digital communications and the so-called “internet of things.” Energy devices of all scales can link directly to other devices in a network instead of just to a central operations hub, allowing for real-time adjustments in voltage, for instance, vastly improving the potential for optimizing energy flows.

      “In the course, we discuss how information exchange will be key to integrating new end-to-end energy resources and, because of this interactivity, how we can model better ways of controlling entire energy networks,” says Ilic. “It’s a big lesson of the course to show the value of information and software in enabling us to decarbonize the system and build resilience, rather than just building hardware.”

      By the end of the course, students are invited to pursue independent research projects. Some might model the impact of a new energy source on a local grid or investigate different options for reducing energy loss in transmission lines.

      “It would be nice if they see that we don’t have to rely on hardware or large-scale solutions to bring about improved electric service and a clean and resilient grid, but instead on information technologies such as smart components exchanging data in real time, or microgrids in neighborhoods that sustain themselves even when they lose power,” says Ilic. “I hope students walk away convinced that it does make sense to rethink how we operate our basic power systems and that with systematic, physics-based modeling and IT methods we can enable better, more flexible operation in the future.”

      This article appears in the Autumn 2021 issue of Energy Futures, the magazine of the MIT Energy Initiative More