The Volvo 240R is an independent concept for a high-performance sedan and wagon.
It blends the boxy shape of the classic Volvo 200 Series with modern design elements.
The project was created by independent designer Jordan Rubinstein-Towler.
Polestar has come a long way from its origins as Volvo’s performance division, evolving into a standalone electric vehicle manufacturer. But with this shift in direction, it raises the inevitable question: Will future Volvo models get high-performance variants like BMW’s M division or Mercedes’ AMG lineup? Well, independent designer Jordan Rubinstein-Towler has ventured into this very idea with his creation, the 240R, which is a high-performance, retro-futuristic take on his own Volvo 240 concept.
A Nod to the Past with a Twist
In keeping with the spirit of the original design study, the Volvo 240R blends the unmistakable boxy shape of the old 200 Series with modern styling. The R version introduces a sporty bodykit—think deeper bumpers, side skirts, a subtle diffuser, and a ducktail spoiler. It’s like the 240 got a solid workout, but still managed to keep its classic personality.
The renders speak for themselves. Rubinstein-Towler’s design work strikes a perfect balance with black and body-colored accents on the revamped parts, delivering that classic yet contemporary look. To really emphasize the performance angle, the car rolls on new black five-spoke alloy wheels, an homage to Volvo’s sporty heritage.
In one of the renderings, the sedan poses next to a Volvo 240 racer that enjoyed great success in touring car championships back in the ’80s. Likewise, the wagon is joined by the Volvo 850 R from the mid-90s.
Modern Interior, Retro Vibes
But the 240R isn’t just about looking fast on the outside—Rubinstein-Towler has also done some work on the interior. Highlights include the blue upholstery that brings a splash of color to the retro-inspired bucket seats, along with a fresh new steering wheel design. The digital cockpit, while updated, keeps things minimalist with a few smaller displays, paired with physical controls to preserve that tactile feel on the center console.
Illustrations Jordan Rubinstein-Towler
Power to Match the Looks?
Now, if this car ever made it to production, what’s going to power this modern interpretation of the classic ‘brick’? Since the modern reimagining of the Volvo 240 is an electric vehicle, the (fictional) 240R would also be expected to run on a zero-emission powertrain.
A quick peek into the Geely parts bin—Volvo’s parent company—shows that a high-performance electric car is well within reach. Take the Twin Motor Performance version of the new Volvo ES90, for instance; it produces a combined 671 hp (680 PS / 500 kW). That’s plenty of power for both a high-performance sedan and wagon.
Speaking of the ES90, the electric liftback sedan recently debuted as a direct rival to the BMW i5 and Mercedes EQE. From Rubinstein-Towler’s renderings, it looks like his 240R proposal could slot in just below that model, potentially filling the gap left by the soon-to-be-discontinued S60/V60.
Such an offering would compete against the upcoming BMW Neue Klasse i3 and the electric Mercedes C-Class. BMW M has already teased a high-performance version of the electric sedan, while Mercedes is expected to release an AMG version sooner or later.
Hopefully, Volvo’s design team is paying attention to the innovative concepts emerging from independent designers. Perhaps they’ll find some inspiration for future models – assuming they’re not too busy figuring out how to make their SUVs even more ubiquitous.
Tesla granted a new interview to Sandy Munro, revealing more about future product plans.
Executives doubled down on Elon Musk’s promised timelines and plans during the chat.
This comes at a crucial time when Tesla can certainly use all the good news it can get.
Franz Von Holzhausen and Lars Moravy are among Tesla’s top brass—Von Holzhausen is the senior design exec, and Moravy serves as the VP of engineering. In other words, these two are practically walking encyclopedias of Tesla’s product plans. And now, they’ve decided to spill a bit more of the proverbial tea in a fresh interview with teardown expert Sandy Munro.
In this conversation, the pair delves into the challenges of developing and producing ambitious vehicles like the CyberCab and Robovan. Speaking of the small people mover, expect the CyberCab to continue its march toward a 2026 release date. Both Von Holzhausen and Moravy agreed that Tesla would manage to begin testing for Level 5 autonomy later this year too. Sure, those rideshare cars will use a real human backup working remotely but let’s see how it goes before we critique it.
CyberCab’s Surprising Range and Production Details
Moravy shared that the CyberCab will likely be powered by a battery pack smaller than 50 kWh, and still manage to deliver around 300 miles of “real-world” range. That would be impressive as most cars with that type of range currently have much larger battery packs. For instance, the Model 3 Long Range RWD uses a 79.7 kWh battery and has 363 miles of range.
The production side of things isn’t being left behind either. Moravy mentioned that Tesla will continue to lean heavily on its signature casting process too. The CyberCab will feature a large casting at both the front and rear, as well as door shell castings to help tie everything together. That aids in Tesla’s goal to cut costs and reduce complexity. Another move toward that goal is that the team isn’t going to paint the castings. They say they have corrosion under control so there’s no need.
Robovan Is Not Quite Ready for Prime Time
Switching gears to the Robovan, Von Holzhausen gave Sandy Munro a glimpse into the vehicle’s interior, though it seems Tesla’s still working through the details. The team has tried different configurations, but the one shown in the video features a 14-seat layout, which is a bit… ambitious. It’s easier to pick up on additional details in the light of day too. For instance, the seats look very wide compared to an everyday car.
While it’s safe to say the final design could shift quite a bit before the Robovan hits the streets (whenever that may be), this video provides an interesting peek into Tesla’s ongoing projects. And while Elon Musk is off doing, well, whatever it is he does remotely, Von Holzhausen and Moravy are still hard at work shaping the future of the brand.
VW’s design boss said that future models will have physical buttons for crucial functions.
Andreas Mindt said they won’t make the same mistake with touch-based controls.
An extra row of buttons can be found on the VW ID.2all and ID.Every1 concepts.
Volkswagen has finally realized what pretty much every driver already knew: stuffing every function into a touchscreen is a nightmare. The company’s design boss, Andreas Mindt, has revealed that future VWs, starting with the ID.2, will feature physical controls for essential functions. This might sound like common sense, but, hey, better late than never.
In late 2022, VW made the bold (and highly unpopular) decision to ditch traditional physical buttons and switch to touch-sensitive controls on the steering wheel. That, predictably, led to a flood of complaints. So, after the unveiling of the ID.2all concept in 2023, VW admitted its mistake and promised to roll back the touch-centric design. Now, Mindt is spilling the details on the company’s course correction.
The Return of Buttons
Mindt told Autocar that next-gen VW models will come with physical buttons for the five most important functions: volume, seat heating, fan controls, and hazard lights—right below the infotainment screen. No more endlessly swiping through menus just to turn on the heat. He also made it clear that this shift would apply “in every car that we make from now on. We understood this.”
In a refreshingly candid moment, VW’s design boss openly admitted that the strategy followed by his predecessor wasn’t the right one: “We will never, ever make this mistake any more. On the steering wheel, we will have physical buttons. No guessing any more. There’s feedback, it’s real, and people love this. Honestly, it’s a car. It’s not a phone: it’s a car.”
The first car to embrace this much-welcome change with an extra row of physical buttons under the infotainment display will be the ID.2all electric supermini, which is set to launch in 2026. The production version of VW’s ID.Every1 concept, expected in 2027, will use a similar layout. Say goodbye to those awkward, touch-sensitive sliders that control everything from volume to AC temperature. Honestly, if you’ve ever tried to adjust the heat on a touchscreen while driving, you probably understand why this was inevitable.
This means that VW will finally ditch the haptic sliders currently being used to control the media volume, navigation zoom, and A/C temperature. These can be found in several VW Group models including the facelifted Golf.
Despite all the button talk, Mindt made it clear that the touchscreen isn’t going anywhere. “There are a lot of functions you have to deliver in certain areas, so the screen will be big and you will find a lot of HMI contents in the depths of the system,” he said. “But the five main things will always be on the first physical layer. That’s very important.”
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.”
“So many things I was lucky to be exposed to at MIT,” Doug Field says, were “all building blocks, pieces of the puzzle, that helped me navigate through difficult situations later on.”
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.
In a new dataset that includes more than 8,000 car designs, MIT engineers simulated the aerodynamics for a given car shape, which they represent in various modalities, including “surface fields.”
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.
A new study by MIT researchers quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue.
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