There’s a lot of untapped potential in our homes and vehicles that could be harnessed to reinforce local power grids and make them more resilient to unforeseen outages, a new study shows.

In response to a cyber attack or natural disaster, a backup network of decentralized devices — such as residential solar panels, batteries, electric vehicles, heat pumps, and water heaters — could restore electricity or relieve stress on the grid, MIT engineers say.

Such devices are “grid-edge” resources found close to the consumer rather than near central power plants, substations, or transmission lines. Grid-edge devices can independently generate, store, or tune their consumption of power. In their study, the research team shows how such devices could one day be called upon to either pump power into the grid, or rebalance it by dialing down or delaying their power use.

In a paper appearing this week in the Proceedings of the National Academy of Sciences, the engineers present a blueprint for how grid-edge devices could reinforce the power grid through a “local electricity market.” Owners of grid-edge devices could subscribe to a regional market and essentially loan out their device to be part of a microgrid or a local network of on-call energy resources.

In the event that the main power grid is compromised, an algorithm developed by the researchers would kick in for each local electricity market, to quickly determine which devices in the network are trustworthy. The algorithm would then identify the combination of trustworthy devices that would most effectively mitigate the power failure, by either pumping power into the grid or reducing the power they draw from it, by an amount that the algorithm would calculate and communicate to the relevant subscribers. The subscribers could then be compensated through the market, depending on their participation.

The team illustrated this new framework through a number of grid attack scenarios, in which they considered failures at different levels of a power grid, from various sources such as a cyber attack or a natural disaster. Applying their algorithm, they showed that various networks of grid-edge devices were able to dissolve the various attacks.

The results demonstrate that grid-edge devices such as rooftop solar panels, EV chargers, batteries, and smart thermostats (for HVAC devices or heat pumps) could be tapped to stabilize the power grid in the event of an attack.

“All these small devices can do their little bit in terms of adjusting their consumption,” says study co-author Anu Annaswamy, a research scientist in MIT’s Department of Mechanical Engineering. “If we can harness our smart dishwashers, rooftop panels, and EVs, and put our combined shoulders to the wheel, we can really have a resilient grid.”

The study’s MIT co-authors include lead author Vineet Nair and John Williams, along with collaborators from multiple institutions including the Indian Institute of Technology, the National Renewable Energy Laboratory, and elsewhere.

Power boost

The team’s study is an extension of their broader work in adaptive control theory and designing systems to automatically adapt to changing conditions. Annaswamy, who leads the Active-Adaptive Control Laboratory at MIT, explores ways to boost the reliability of renewable energy sources such as solar power.

“These renewables come with a strong temporal signature, in that we know for sure the sun will set every day, so the solar power will go away,” Annaswamy says. “How do you make up for the shortfall?”

The researchers found the answer could lie in the many grid-edge devices that consumers are increasingly installing in their own homes.

“There are lots of distributed energy resources that are coming up now, closer to the customer rather than near large power plants, and it’s mainly because of individual efforts to decarbonize,” Nair says. “So you have all this capability at the grid edge. Surely we should be able to put them to good use.”

While considering ways to deal with drops in energy from the normal operation of renewable sources, the team also began to look into other causes of power dips, such as from cyber attacks. They wondered, in these malicious instances, whether and how the same grid-edge devices could step in to stabilize the grid following an unforeseen, targeted attack.

Attack mode

In their new work, Annaswamy, Nair, and their colleagues developed a framework for incorporating grid-edge devices, and in particular, internet-of-things (IoT) devices, in a way that would support the larger grid in the event of an attack or disruption. IoT devices are physical objects that contain sensors and software that connect to the internet.

For their new framework, named EUREICA (Efficient, Ultra-REsilient, IoT-Coordinated Assets), the researchers start with the assumption that one day, most grid-edge devices will also be IoT devices, enabling rooftop panels, EV chargers, and smart thermostats to wirelessly connect to a larger network of similarly independent and distributed devices. 

The team envisions that for a given region, such as a community of 1,000 homes, there exists a certain number of IoT devices that could potentially be enlisted in the region’s local network, or microgrid. Such a network would be managed by an operator, who would be able to communicate with operators of other nearby microgrids.

If the main power grid is compromised or attacked, operators would run the researchers’ decision-making algorithm to determine trustworthy devices within the network that can pitch in to help mitigate the attack.

The team tested the algorithm on a number of scenarios, such as a cyber attack in which all smart thermostats made by a certain manufacturer are hacked to raise their setpoints simultaneously to a degree that dramatically alters a region’s energy load and destabilizes the grid. The researchers also considered attacks and weather events that would shut off the transmission of energy at various levels and nodes throughout a power grid.

“In our attacks we consider between 5 and 40 percent of the power being lost. We assume some nodes are attacked, and some are still available and have some IoT resources, whether a battery with energy available or an EV or HVAC device that’s controllable,” Nair explains. “So, our algorithm decides which of those houses can step in to either provide extra power generation to inject into the grid or reduce their demand to meet the shortfall.”

In every scenario that they tested, the team found that the algorithm was able to successfully restabilize the grid and mitigate the attack or power failure. They acknowledge that to put in place such a network of grid-edge devices will require buy-in from customers, policymakers, and local officials, as well as innovations such as advanced power inverters that enable EVs to inject power back into the grid.

“This is just the first of many steps that have to happen in quick succession for this idea of local electricity markets to be implemented and expanded upon,” Annaswamy says. “But we believe it’s a good start.”

This work was supported, in part, by the U.S. Department of Energy and the MIT Energy Initiative.

Across a career’s worth of pioneering product designs, Doug Field’s work has shaped the experience of anyone who’s ever used a MacBook Air, ridden a Segway, or driven a Tesla Model 3.

But his newest project is his most ambitious yet: reinventing the Ford automobile, one of the past century’s most iconic pieces of technology.

As Ford’s chief electric vehicle (EV), digital, and design officer, Field is tasked with leading the development of the company’s electric vehicles, while making new software platforms central to all Ford models.

To bring Ford Motor Co. into that digital and electric future, Field effectively has to lead a fast-moving startup inside the legacy carmaker. “It is incredibly hard, figuring out how to do ‘startups’ within large organizations,” he concedes.

If anyone can pull it off, it’s likely to be Field. Ever since his time in MIT’s Leaders for Global Operations (then known as “Leaders in Manufacturing”) program studying organizational behavior and strategy, Field has been fixated on creating the conditions that foster innovation.

“The natural state of an organization is to make it harder and harder to do those things: to innovate, to have small teams, to go against the grain,” he says. To overcome those forces, Field has become a master practitioner of the art of curating diverse, talented teams and helping them flourish inside of big, complex companies.

“It’s one thing to make a creative environment where you can come up with big ideas,” he says. “It’s another to create an execution-focused environment to crank things out. I became intrigued with, and have been for the rest of my career, this question of how can you have both work together?”

Three decades after his first stint as a development engineer at Ford Motor Co., Field now has a chance to marry the manufacturing muscle of Ford with the bold approach that helped him rethink Apple’s laptops and craft Tesla’s Model 3 sedan. His task is nothing less than rethinking how cars are made and operated, from the bottom up.

“If it’s only creative or execution, you’re not going to change the world,” he says. “If you want to have a huge impact, you need people to change the course you’re on, and you need people to build it.”

A passion for design

From a young age, Field had a fascination with automobiles. “I was definitely into cars and transportation more generally,” he says. “I thought of cars as the place where technology and art and human design came together — cars were where all my interests intersected.”

With a mother who was an artist and musician and an engineer father, Field credits his parents’ influence for his lifelong interest in both the aesthetic and technical elements of product design. “I think that’s why I’m drawn to autos — there’s very much an aesthetic aspect to the product,” he says. 

After earning a degree in mechanical engineering from Purdue University, Field took a job at Ford in 1987. The big Detroit automakers of that era excelled at mass-producing cars, but weren’t necessarily set up to encourage or reward innovative thinking. Field chafed at the “overstructured and bureaucratic” operational culture he encountered.

The experience was frustrating at times, but also valuable and clarifying. He realized that he “wanted to work with fast-moving, technology-based businesses.”

“My interest in advancing technical problem-solving didn’t have a place in the auto industry” at the time, he says. “I knew I wanted to work with passionate people and create something that didn’t exist, in an environment where talent and innovation were prized, where irreverence was an asset and not a liability. When I read about Silicon Valley, I loved the way they talked about things.”

During that time, Field took two years off to enroll in MIT’s LGO program, where he deepened his technical skills and encountered ideas about manufacturing processes and team-driven innovation that would serve him well in the years ahead.

“Some of core skill sets that I developed there were really, really important,” he says, “in the context of production lines and production processes.” He studied systems engineering and the use of Monte Carlo simulations to model complex manufacturing environments. During his internship with aerospace manufacturer Pratt & Whitney, he worked on automated design in computer-aided design (CAD) systems, long before those techniques became standard practice.

Another powerful tool he picked up was the science of probability and statistics, under the tutelage of MIT Professor Alvin Drake in his legendary course 6.041/6.431 (Probabilistic Systems Analysis). Field would go on to apply those insights not only to production processes, but also to characterizing variability in people’s aptitudes, working styles, and talents, in the service of building better, more innovative teams. And studying organizational strategy catalyzed his career-long interest in “ways to look at innovation as an outcome, rather than a random spark of genius.”

“So many things I was lucky to be exposed to at MIT,” Field says, were “all building blocks, pieces of the puzzle, that helped me navigate through difficult situations later on.”

Learning while leading

After leaving Ford in 1993, Field worked at Johnson and Johnson Medical for three years in process development. There, he met Segway inventor Dean Kamen, who was working on a project called the iBOT, a gyroscopic powered wheelchair that could climb stairs.

When Kamen spun off Segway to develop a new personal mobility device using the same technology, Field became his first hire. He spent nearly a decade as the firm’s chief technology officer.

At Segway, Field’s interests in vehicles, technology, innovation, process, and human-centered design all came together.

“When I think about working now on electric cars, it was a real gift,” he says. The problems they tackled prefigured the ones he would grapple with later at Tesla and Ford. “Segway was very much a precursor to a modern EV. Completely software controlled, with higher-voltage batteries, redundant systems, traction control, brushless DC motors — it was basically a miniature Tesla in the year 2000.”

At Segway, Field assembled an “amazing” team of engineers and designers who were as passionate as he was about pushing the envelope. “Segway was the first place I was able to hand-pick every single person I worked with, define the culture, and define the mission.”

As he grew into this leadership role, he became equally engrossed with cracking another puzzle: “How do you prize people who don’t fit in?”

“Such a fundamental part of the fabric of Silicon Valley is the love of embracing talent over a traditional organization’s ways of measuring people,” he says. “If you want to innovate, you need to learn how to manage neurodivergence and a very different set of personalities than the people you find in large corporations.”

Field still keeps the base housing of a Segway in his office, as a reminder of what those kinds of teams — along with obsessive attention to detail — can achieve.

Before joining Apple in 2008, he showed that component, with its clean lines and every minuscule part in its place in one unified package, to his prospective new colleagues. “They were like, “OK, you’re one of us,’” he recalls.

He soon became vice president of hardware development for all Mac computers, leading the teams behind the MacBook Air and MacBook Pro and eventually overseeing more than 2,000 employees. “Making things really simple and really elegant, thinking about the product as an integrated whole, that really took me into Apple.”

The challenge of giving the MacBook Air its signature sleek and light profile is an example.

“The MacBook Air was the first high-volume consumer electronic product built out of a CNC-machined enclosure,” says Field. He worked with industrial design and technology teams to devise a way to make the laptop from one solid piece of aluminum and jettison two-thirds of the parts found in the iMac. “We had material cut away so that every single screw and piece of electronics sat down into it an integrated way. That’s how we got the product so small and slim.”

“When I interviewed with Jony Ive” — Apple’s legendary chief design officer — “he said your ability to zoom out and zoom in was the number one most important ability as a leader at Apple.” That meant zooming out to think about “the entire ethos of this product, and the way it will affect the world” and zooming all the way back in to obsess over, say, the physical shape of the laptop itself and what it feels like in a user’s hands.

“That thread of attention to detail, passion for product, design plus technology rolled directly into what I was doing at Tesla,” he says. When Field joined Tesla in 2013, he was drawn to the way the brash startup upended the approach to making cars. “Tesla was integrating digital technology into cars in a way nobody else was. They said, ‘We’re not a car company in Silicon Valley, we’re a Silicon Valley company and we happen to make cars.’”

Field assembled and led the team that produced the Model 3 sedan, Tesla’s most affordable vehicle, designed to have mass-market appeal.

That experience only reinforced the importance, and power, of zooming in and out as a designer — in a way that encompasses the bigger human resources picture.

“You have to have a broad sense of what you’re trying to accomplish and help people in the organization understand what it means to them,” he says. “You have to go across and understand operations enough to glue all of those (things) together — while still being great at and focused on something very, very deeply. That’s T-shaped leadership.”

He credits his time at LGO with providing the foundation for the “T-shaped leadership” he practices.

“An education like the one I got at MIT allowed me to keep moving that ‘T’, to focus really deep, learn a ton, teach as much as I can, and after something gets more mature, pull out and bed down into other areas where the organization needs to grow or where there’s a crisis.”

The power of marrying scale to a “startup mentality”

In 2018, Field returned to Apple as a vice president for special projects. “I left Tesla after Model 3 and Y started to ramp, as there were people better than me to run high-volume manufacturing,” he says. “I went back to Apple hoping what Tesla had learned would motivate Apple to get into a different market.”

That market was his early love: cars. Field quietly led a project to develop an electric vehicle at Apple for three years.

Then Ford CEO Jim Farley came calling. He persuaded Field to return to Ford in late 2021, partly by demonstrating how much things had changed since his first stint as the carmaker.

“Two things came through loud and clear,” Field says. “One was humility. ‘Our success is not assured.’” That attitude was strikingly different from Field’s early experience in Detroit, encountering managers who were resistant to change. “The other thing was urgency. Jim and Bill Ford said the exact same thing to me: ‘We have four or five years to completely remake this company.’”

“I said, ‘OK, if the top of company really believes that, then the auto industry may be ready for what I hope to offer.’”

So far, Field is energized and encouraged by the appetite for reinvention he’s encountered this time around at Ford.

“If you can combine what Ford does really well with what a Tesla or Rivian can do well, this is something to be reckoned with,” says Field. “Skunk works have become one of the fundamental tools of my career,” he says, using an industry term that describes a project pursued by a small, autonomous group of people within a larger organization.

Ford has been developing a new, lower-cost, software-enabled EV platform — running all of the car’s sensors and components from a central digital operating system — with a “skunk works” team for the past two years. The company plans to build new sedans, SUVs, and small pickups based on this new platform.

With other legacy carmakers like Volvo racing into the electric future and fierce competition from EV leaders Tesla and Rivian, Field and his colleagues have their work cut out for them.

If he succeeds, leveraging his decades of learning and leading from LGO to Silicon Valley, then his latest chapter could transform the way we all drive — and secure a spot for Ford at the front of the electric vehicle pack in the process.

“I’ve been lucky to feel over and over that what I’m doing right now — they are going to write a book about it,” say Field. “This is a big deal, for Ford and the U.S. auto industry, and for American industry, actually.”

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

As a major contributor to global carbon dioxide (CO₂) emissions, the transportation sector has immense potential to advance decarbonization. However, a zero-emissions global supply chain requires re-imagining reliance on a heavy-duty trucking industry that emits 810,000 tons of CO₂, or 6 percent of the United States’ greenhouse gas emissions, and consumes 29 billion gallons of diesel annually in the U.S. alone.

A new study by MIT researchers, presented at the recent American Society of Mechanical Engineers 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue. The multivariable model outlined in the paper allows fleet owners and operators to better understand the design choices that impact the economic feasibility of battery-electric and hydrogen fuel cell heavy-duty trucks for commercial application, equipping stakeholders to make informed fleet transition decisions.

“The whole issue [of decarbonizing trucking] is like a very big, messy pie. One of the things we can do, from an academic standpoint, is quantify some of those pieces of pie with modeling, based on information and experience we’ve learned from industry stakeholders,” says ZhiYi Liang, PhD student on the renewable hydrogen team at the MIT K. Lisa Yang Global Engineering and Research Center (GEAR) and lead author of the study. Co-authored by Bryony DuPont, visiting scholar at GEAR, and Amos Winter, the Germeshausen Professor in the MIT Department of Mechanical Engineering, the paper elucidates operational and socioeconomic factors that need to be considered in efforts to decarbonize heavy-duty vehicles (HDVs).

Operational and infrastructure challenges

The team’s model shows that a technical challenge lies in the amount of energy that needs to be stored on the truck to meet the range and towing performance needs of commercial trucking applications. Due to the high energy density and low cost of diesel, existing diesel drivetrains remain more competitive than alternative lithium battery-electric vehicle (Li-BEV) and hydrogen fuel-cell-electric vehicle (H2 FCEV) drivetrains. Although Li-BEV drivetrains have the highest energy efficiency of all three, they are limited to short-to-medium range routes (under 500 miles) with low freight capacity, due to the weight and volume of the onboard energy storage needed. In addition, the authors note that existing electric grid infrastructure will need significant upgrades to support large-scale deployment of Li-BEV HDVs.

While the hydrogen-powered drivetrain has a significant weight advantage that enables higher cargo capacity and routes over 750 miles, the current state of hydrogen fuel networks limits economic viability, especially once operational cost and projected revenue are taken into account. Deployment will most likely require government intervention in the form of incentives and subsidies to reduce the price of hydrogen by more than half, as well as continued investment by corporations to ensure a stable supply. Also, as H2-FCEVs are still a relatively new technology, the ongoing design of conformal onboard hydrogen storage systems — one of which is the subject of Liang’s PhD — is crucial to successful adoption into the HDV market.

The current efficiency of diesel systems is a result of technological developments and manufacturing processes established over many decades, a precedent that suggests similar strides can be made with alternative drivetrains. However, interactions with fleet owners, automotive manufacturers, and refueling network providers reveal another major hurdle in the way that each “slice of the pie” is interrelated — issues must be addressed simultaneously because of how they affect each other, from renewable fuel infrastructure to technological readiness and capital cost of new fleets, among other considerations. And first steps into an uncertain future, where no one sector is fully in control of potential outcomes, is inherently risky. 

“Besides infrastructure limitations, we only have prototypes [of alternative HDVs] for fleet operator use, so the cost of procuring them is high, which means there isn’t demand for automakers to build manufacturing lines up to a scale that would make them economical to produce,” says Liang, describing just one step of a vicious cycle that is difficult to disrupt, especially for industry stakeholders trying to be competitive in a free market. 

Quantifying a path to feasibility

“Folks in the industry know that some kind of energy transition needs to happen, but they may not necessarily know for certain what the most viable path forward is,” says Liang. Although there is no singular avenue to zero emissions, the new model provides a way to further quantify and assess at least one slice of pie to aid decision-making.

Other MIT-led efforts aimed at helping industry stakeholders navigate decarbonization include an interactive mapping tool developed by Danika MacDonell, Impact Fellow at the MIT Climate and Sustainability Consortium (MCSC); alongside Florian Allroggen, executive director of MITs Zero Impact Aviation Alliance; and undergraduate researchers Micah Borrero, Helena De Figueiredo Valente, and Brooke Bao. The MCSC’s Geospatial Decision Support Tool supports strategic decision-making for fleet operators by allowing them to visualize regional freight flow densities, costs, emissions, planned and available infrastructure, and relevant regulations and incentives by region.

While current limitations reveal the need for joint problem-solving across sectors, the authors believe that stakeholders are motivated and ready to tackle climate problems together. Once-competing businesses already appear to be embracing a culture shift toward collaboration, with the recent agreement between General Motors and Hyundai to explore “future collaboration across key strategic areas,” including clean energy. 

Liang believes that transitioning the transportation sector to zero emissions is just one part of an “energy revolution” that will require all sectors to work together, because “everything is connected. In order for the whole thing to make sense, we need to consider ourselves part of that pie, and the entire system needs to change,” says Liang. “You can’t make a revolution succeed by yourself.” 

The authors acknowledge the MIT Climate and Sustainability Consortium for connecting them with industry members in the HDV ecosystem; and the MIT K. Lisa Yang Global Engineering and Research Center and MIT Morningside Academy for Design for financial support.

Climate anxiety affects nearly half of young people aged 16-25. Students like second-year Rachel Mohammed find hope and inspiration through her involvement in innovative climate solutions, working alongside peers who share her determination. “I’ve met so many people at MIT who are dedicated to finding climate solutions in ways that I had never imagined, dreamed of, or heard of. That is what keeps me going, and I’m doing my part,” she says.

Hydrogen-fueled engines

Hydrogen offers the potential for zero or near-zero emissions, with the ability to reduce greenhouse gases and pollution by 29 percent. However, the hydrogen industry faces many challenges related to storage solutions and costs.

Mohammed leads the hydrogen team on MIT’s Electric Vehicle Team (EVT), which is dedicated to harnessing hydrogen power to build a cleaner, more sustainable future. EVT is one of several student-led build teams at the Edgerton Center focused on innovative climate solutions. Since its founding in 1992, the Edgerton Center has been a hub for MIT students to bring their ideas to life.

Hydrogen is mostly used in large vehicles like trucks and planes because it requires a lot of storage space. EVT is building their second iteration of a motorcycle based on what Mohammed calls a “goofy hypothesis” that you can use hydrogen to power a small vehicle. The team employs a hydrogen fuel cell system, which generates electricity by combining hydrogen with oxygen. However, the technology faces challenges, particularly in storage, which EVT is tackling with innovative designs for smaller vehicles.

Presenting at the 2024 World Hydrogen Summit reaffirmed Mohammed’s confidence in this project. “I often encounter skepticism, with people saying it’s not practical. Seeing others actively working on similar initiatives made me realize that we can do it too,” Mohammed says.

The team’s first successful track test last October allowed them to evaluate the real-world performance of their hydrogen-powered motorcycle, marking a crucial step in proving the feasibility and efficiency of their design.

MIT’s Sustainable Engine Team (SET), founded by junior Charles Yong, uses the combustion method to generate energy with hydrogen. This is a promising technology route for high-power-density applications, like aviation, but Yong believes it hasn’t received enough attention. Yong explains, “In the hydrogen power industry, startups choose fuel cell routes instead of combustion because gas turbine industry giants are 50 years ahead. However, these giants are moving very slowly toward hydrogen due to its not-yet-fully-developed infrastructure. Working under the Edgerton Center allows us to take risks and explore advanced tech directions to demonstrate that hydrogen combustion can be readily available.”

Both EVT and SET are publishing their research and providing detailed instructions for anyone interested in replicating their results.

Charging stations for electric vehicles are essential for cleaning up the transportation sector. A new study by MIT researchers suggests they’re good for business, too.

The study found that, in California, opening a charging station boosted annual spending at each nearby business by an average of about $1,500 in 2019 and about $400 between January 2021 and June 2023. The spending bump amounts to thousands of extra dollars annually for nearby businesses, with the increase particularly pronounced for businesses in underresourced areas.

The study’s authors hope the research paints a more holistic picture of the benefits of EV charging stations, beyond environmental factors.

“These increases are equal to a significant chunk of the cost of installing an EV charger, and I hope this study sheds light on these economic benefits,” says lead author Yunhan Zheng MCP ’21, SM ’21, PhD ’24, a postdoc at the Singapore-MIT Alliance for Research and Technology (SMART). “The findings could also diversify the income stream for charger providers and site hosts, and lead to more informed business models for EV charging stations.”

Zheng’s co-authors on the paper, which was published today in Nature Communications, are David Keith, a senior lecturer at the MIT Sloan School of Management; Jinhua Zhao, an MIT professor of cities and transportation; and alumni Shenhao Wang MCP ’17, SM ’17, PhD ’20 and Mi Diao MCP ’06, PhD ’10.

Understanding the EV effect

Increasing the number of electric vehicle charging stations is seen as a key prerequisite for the transition to a cleaner, electrified transportation sector. As such, the 2021 U.S. Infrastructure Investment and Jobs Act committed $7.5 billion to build a national network of public electric vehicle chargers across the U.S.

But a large amount of private investment will also be needed to make charging stations ubiquitous.

“The U.S. is investing a lot in EV chargers and really encouraging EV adoption, but many EV charging providers can’t make enough money at this stage, and getting to profitability is a major challenge,” Zheng says.

EV advocates have long argued that the presence of charging stations brings economic benefits to surrounding communities, but Zheng says previous studies on their impact relied on surveys or were small-scale. Her team of collaborators wanted to make advocates’ claims more empirical.

For their study, the researchers collected data from over 4,000 charging stations in California and 140,000 businesses, relying on anonymized credit and debit card transactions to measure changes in consumer spending. The researchers used data from 2019 through June of 2023, skipping the year 2020 to minimize the impact of the pandemic.

To judge whether charging stations caused customer spending increases, the researchers compared data from businesses within 500 meters of new charging stations before and after their installation. They also analyzed transactions from similar businesses in the same time frame that weren’t near charging stations.

Supercharging nearby businesses

The researchers found that installing a charging station boosted annual spending at nearby establishments by an average of 1.4 percent in 2019 and 0.8 percent from January 2021 to June 2023.

While that might sound like a small amount per business, it amounts to thousands of dollars in overall consumer spending increases. Specifically, those percentages translate to almost $23,000 in cumulative spending increases in 2019 and about $3,400 per year from 2021 through June 2023.

Zheng says the decline in spending increases over the two time periods might be due to a saturation of EV chargers, leading to lower utilization, as well as an overall decrease in spending per business after the Covid-19 pandemic and a reduced number of businesses served by each EV charging station in the second period. Despite this decline, the annual impact of a charging station on all its surrounding businesses would still cover approximately 11.2 percent of the average infrastructure and installation cost of a standard charging station.

Through both time frames, the spending increases were highest for businesses within about a football field’s distance from the new stations. They were also significant for businesses in disadvantaged and low-income areas, as designated by California and the Justice40 Initiative.

“The positive impacts of EV charging stations on businesses are not constrained solely to some high-income neighborhoods,” Wang says. “It highlights the importance for policymakers to develop EV charging stations in marginalized areas, because they not only foster a cleaner environment, but also serve as a catalyst for enhancing economic vitality.”

Zheng believes the findings hold a lesson for charging station developers seeking to improve the profitability of their projects.

“The joint gas station and convenience store business model could also be adopted to EV charging stations,” Zheng says. “Traditionally, many gas stations are affiliated with retail store chains, which enables owners to both sell fuel and attract customers to diversify their revenue stream. EV charging providers could consider a similar approach to internalize the positive impact of EV charging stations.”

Zheng also says the findings could support the creation of new funding models for charging stations, such as multiple businesses sharing the costs of construction so they can all benefit from the added spending.

Those changes could accelerate the creation of charging networks, but Zheng cautions that further research is needed to understand how much the study’s findings can be extrapolated to other areas. She encourages other researchers to study the economic effects of charging stations and hopes future research includes states beyond California and even other countries.

“A huge number of studies have focused on retail sales effects from traditional transportation infrastructure, such as rail and subway stations, bus stops, and street configurations,” Zhao says. “This research provides evidence for an important, emerging piece of transportation infrastructure and shows a consistently positive effect on local businesses, paving the way for future research in this area.”

The research was supported, in part, by the Singapore-MIT Alliance for Research and Technology (SMART) and the Singapore National Research Foundation. Diao was partially supported by the Natural Science Foundation of Shanghai and the Fundamental Research Funds for the Central Universities of China.

While siblings Kevin Chan ’17 and rising senior Monica Chan may be seven years apart in age, as Monica Chan puts it, “we’re eight grades apart, so, like, eight life-years apart.”

Despite this age gap — Kevin left for college when Monica was in fifth grade — the siblings share remarkably similar experiences and interests. Both led subteams on the MIT Motorsports team, albeit eight years apart. Kevin was the electrical systems lead from 2015 to 2017, and Monica is the current software lead.

Founded in 2001 by Rich James ’04, SM '06 and Nick Gidwani ’04, and supported by the Edgerton Center, MIT Motorsports designs and builds a high-caliber Formula SAE car to race in yearly competitions. Over the past 23 years, MIT Motorsports has built 19 cars, won 10 trophies, and has had hundreds of team members. Alumni are die-hard fans and established an endowed fund for their 20th anniversary to ensure the team’s longevity. In 2017, Kevin’s team won Second Place Overall at the Formula SAE Electric competition in Lincoln, Nebraska.

Kevin was one of two electrical engineering students on the team, and today Monica oversees a subteam of 10 students. The subteam expansion has facilitated the development of a custom telemetry system. “You can view live data coming off of the car that’s transmitted through radio, and we have a custom dashboard that we created with a custom PCB that transmits all that data now,” Monica says. 

“It’s so funny to hear Monica talking about this, because when I was on the team, our UI [user interface] for the driver and everything was so simple. It was just a little, single-line display that showed the max cell temperature and minimum cell voltage,” Kevin chuckles. “And then we literally had a sticky note on the dashboard that was like, do not go above this temperature. Do not go below this voltage.”

While at MIT, Kevin kept up with his sister weekly, updating her on everything happening at Formula Society of Automotive Engineers (FSAE). “A big piece of advice Kevin gave me when I was a junior in high school was that you’re never too young to do something amazing,” Monica says. “He told me back then that ‘you're not going to be much smarter two years from now than you are now.’ That piece of advice helped me get through high school and pushed me to do my best to do the hard and difficult things because indeed, it’s more about the personal qualities you have that push you to do the hard projects. Knowledge can always be acquired, but the drive is the harder part.”

Traditions are part of the fabric of the team culture. Their team stretch at the end of every meeting is an enduring tradition. “Everyone just extends their arms out while standing up and then does a squat. Then, they clap. This is just a thing that has been done on the team since before I was on the team. They said that the origin of it was the stretch that Japanese autoworkers do at the beginning of the day to stretch out their jumpsuits in the factory and make the knees a little bit more flexible. And it’s just fascinating, because this stretch is now almost 20 years old on the team,” Kevin says.

“Hitting Roman,” the day the car first rolls, is an important milestone. “When I was on the team, we were convinced that saying that the car was going to run was bad luck,” Kevin says. “We were trying to come up with a new term to replace the term ‘running car’ because we thought that saying the words ‘running car’ would make it so that the car never ran. So instead of calling it a running car, we called it ‘Roman Chariot.’” The name stuck, and Monica’s team hit Roman in April.

For Kevin, the spirit of Motorsports remains ever-present, as he shares his home with four Motorsports alums and collaborates with three Motorsports alums at Tesla, where he serves as a staff energy systems design/architecture engineer.

“FSAE and the Edgerton Center played a huge role in jump starting my career and my internships. I think there’s not many places where you can get both the breadth and the depth of the design process,” Kevin says.

For Monica, “Race car puts many things in perspective where you implement a lot of the things that you learn in class into a physical project. Sometimes I learn things through race car before I learn them in class. And then when I go back to class, it gives me a better physical intuition for how something works because I have experience implementing it.”

The team recently returned from the Formula Hybrid competition in Loudon, New Hampshire, where they finished first in design, first in scrutineering [mandatory technical, safety, and administrative checks], second in acceleration, third in the racing challenge, fourth in project management, and fifth overall. Edgerton Center Technical Instructor Pat McAtamney reports, “I’ve never seen a team complete a brakes test in one try.”