Carbon fiber, aluminum, maybe the odd bit of titanium here or there: These are the materials we usually expect race cars to be made of. Now you can start adding thermoplastics like Ultem to the list. Additive manufacturing has become a real asset in the racer’s toolbox, although the technology has actually been used at the track longer than you might think.
“Some people think that 3D printing was invented last year,” said Fadi Abro, senior global director of automotive and mobility at Stratasys. The company recently became NASCAR’s official 3D printing partner, but it has a relationship with one of the teams—Joe Gibbs Racing—that stretches back two decades.
“Now the teams only have certain things that they can touch in the vehicle, but what that does is it makes it so that every microscopic advantage you can get out of that one tiny detail that you have control over is so meaningful to your team,” Abro said.
Currently, JGR has five printers, which it uses in a variety of applications. Some are common to other industries—additive manufacturing is a good way to quickly develop new prototypes, as well as tooling and fixtures. But the team also prints parts that go straight onto the race car, like housings, ducts, and brackets.
“These are elements that are really integral for the vehicle to be on the track. If there are changes they want to make, they throw it to the printer, it prints overnight, and you have a part that can go on a track that’s specific to that track. So that gives them a competitive advantage,” Abro said.
Enlarge/ The Cadillac V-Series.R is one of General Motors’ factory-backed racing programs.
James Moy Photography/Getty Images
It is hard to escape the feeling that a few too many businesses are jumping on the AI hype train because it’s hype-y, rather than because AI offers an underlying benefit to their operation. So I will admit to a little inherent skepticism, and perhaps a touch of morbid curiosity, when General Motors got in touch wanting to show off some of the new AI/machine learning tools it has been using to win more races in NASCAR, sportscar racing, and IndyCar. As it turns out, that skepticism was misplaced.
GM has fingers in a lot of motorsport pies, but there are four top-level programs it really, really cares about. Number one for an American automaker is NASCAR—still the king of motorsport here—where Chevrolet supplies engines to six Cup teams. IndyCar, which could once boast of being America’s favorite racing, is home to another six Chevy-powered teams. And then there’s sportscar racing; right now, Cadillac is competing in IMSA’s GTP class and the World Endurance Championship’s Hypercar class, plus a factory Corvette Racing effort in IMSA.
“In all the series we race we either have key partners or specific teams that run our cars. And part of the technical support that they get from us are the capabilities of my team,” said Jonathan Bolenbaugh, motorsports analytics leader at GM, based at GM’s Charlotte Technical Center in North Carolina.
Unlike generative AI that’s being developed to displace humans from creative activities, GM sees the role of AI and ML as supporting human subject-matter experts so they can make the cars go faster. And it’s using these tools in a variety of applications.
Enlarge/ One of GM’s command centers at its Charlotte Technical Center in North Carolina.
General Motors
Each team in each of those various series (obviously) has people on the ground at each race, and invariably more engineers and strategists helping them from Indianapolis, Charlotte, or wherever it is that the particular race team has its home base. But they’ll also be tied in with a team from GM Motorsport, working from one of a number of command centers at its Charlotte Technical Center.
What did they say?
Connecting all three are streams and streams of data from the cars themselves (in series that allow car-to-pit telemetry) but also voice comms, text-based messaging, timing and scoring data from officials, trackside photographs, and more. And one thing Bolenbaugh’s team and their suite of tools can do is help make sense of that data quickly enough for it to be actionable.
“In a series like F1, a lot of teams will have students who are potentially newer members of the team literally listening to the radio and typing out what is happening, then saying, ‘hey, this is about pitting. This is about track conditions,'” Bolenbaugh said.
Instead of giving that to the internship kids, GM built a real time audio transcription tool to do that job. After trying out a commercial off-the-shelf solution, it decided to build its own, “a combination of open source and some of our proprietary code,” Bolenbaugh said. As anyone who has ever been to a race track can attest, it’s a loud environment, so GM had to train models with all the background noise present.
“We’ve been able to really improve our accuracy and usability of the tool to the point where some of the manual support for that capability is now dwindling,” he said, with the benefit that it frees up the humans, who would otherwise be transcribing, to apply their brains in more useful ways.
Take a look at this
Another tool developed by Bolenbaugh and his team was built to quickly analyze images taken by trackside photographers working for the teams and OEMs. While some of the footage they shoot might be for marketing or PR, a lot of it is for the engineers.
Two years ago, getting those photos from the photographer’s camera to the team was the work of two to three minutes. Now, “from shutter click at the racetrack in a NASCAR event to AI-tagged into an application for us to get information out of those photos is seven seconds,” Bolenbaugh said.
Enlarge/ Sometimes you don’t need a ML tool to analyze a photo to tell you the car is damaged.
Jeffrey Vest/Icon Sportswire via Getty Images
“Time is everything, and the shortest lap time that we run—the Coliseum would be an outlier, but maybe like 18 seconds is probably a short lap time. So we need to be faster than from when they pass that pit lane entry to when they come back again,” he said.
At the rollout of this particular tool at a NASCAR race last year, one of GM’s partner teams was able to avoid a cautionary pitstop after its driver scraped the wall, when the young engineer who developed the tool was able to show them a seconds-old photo of the right side of the car that showed it had escaped any damage.
“They didn’t have to wait for a spotter to look, they didn’t have to wait for the driver’s opinion. They knew that didn’t have damage. That team made the playoffs in that series by four points, so in the event that they would have pitted, there’s a likelihood where they didn’t make it,” he said. In cases where a car is damaged, the image analysis tool can automatically flag that and make that known quickly through an alert.
Not all of the images are used for snap decisions like that—engineers can glean a lot about their rivals from photos, too.
“We would be very interested in things related to the geometry of the car for the setup settings—wicker settings, wing angles… ride heights of the car, how close the car is to the ground—those are all things that would be great to know from an engineering standpoint, and those would be objectives that we would have in doing image analysis,” said Patrick Canupp, director of motorsports competition engineering at GM.
Enlarge/ Many of the photographers you see working trackside will be shooting on behalf of teams or manufacturers.
Steve Russell/Toronto Star via Getty Images
“It’s not straightforward to take a set of still images and determine a lot of engineering information from those. And so we’re working on that actively to help with all the photos that come in to us on a race weekend—there’s thousands of them. And so it’s a lot of information that we have at our access, that we want to try to maximize the engineering information that we glean from all of that data. It’s kind of a big data problem that AI is really geared for,” Canupp said.
The computer says we should pit now
Remember that transcribed audio feed from earlier? “If a bunch of drivers are starting to talk about something similar in the race like the track condition, we can start inferring, based on… the occurrence of certain words, that the track is changing,” said Bolenbaugh. “It might not just be your car… if drivers are talking about something on track, the likelihood of a caution, which is a part of our strategy model, might be going up.”
That feeds into a strategy tool that also takes lap times from timing and scoring, as well as fuel efficiency data in racing series that provide it for all cars, or a predictive model to do the same in series like NASCAR and IndyCar where teams don’t get to see that kind of data from their competitors, as well as models of tire wear.
“One of the biggest things that we need to manage is tires, fuel, and lap time. Everything is a trade-off between trying to execute the race the fastest,” Bolenbaugh said.
Obviously races are dynamic situations, and so “multiple times a lap as the scenario changes, we’re updating our recommendation. So, with tire fall off [as the tire wears and loses grip], you’re following up in real time, predicting where it’s going to be. We are constantly evolving during the race and doing transfer learning so we go into the weekend, as the race unfolds, continuing to train models in real time,” Bolenbaugh said.
Enlarge/ After developing the Next Gen stock car and then the Garage 56 car, NASCAR’s tech team has now created a battery-electric race car prototype.
NASCAR
This past weekend was a busy one on the racing calendar. Over in the UK, the British Grand Prix at Silverstone was yet more evidence that Red Bull no longer has the fastest car in F1. In Ohio, IndyCar had a mostly successful introduction of its new supercapacitor-based hybrid system. And a couple of Great Lakes over, NASCAR held its second street race in Chicago, choosing that event to also show off its prototype of a fully electric stock car.
In doing so, it has partnered with the technology company ABB, which, among other things, makes charging equipment and is also Formula E’s title sponsor. “The objective of the collaboration between NASCAR, ABB in the United States, and the NASCAR industry is to push the boundaries of electrification technology, from EV racing to long-haul transportation to facility operations,” said ABB Executive Vice President Ralph Donati.
The NASCAR EV prototype starts with a modified Next Gen chassis, which was introduced to the sport in 2022. This is something of a no-brainer: in addition to the other stuff you want in a race car chassis, like a good ratio of stiffness to weight, it’s also designed to be able to safely protect the driver from the consequences of the very high-speed crashes that occur in the series. So, there shouldn’t be any concerns about the 78 kWh liquid-cooled lithium-ion battery pack.
Enlarge/ The EV prototype was developed together with Ford, General Motors, and Toyota. And yes, it’s a crossover.
NASCAR
That pack supplies three electric motors: one for the front axle and one for each rear wheel. And it’s far more powerful than any V8-powered stock car, with 1,341 hp (1,000 kW) available at peak power. The motors are supplied by STARD, an Austrian motorsport company that also helped Ford develop the wacky Supervan 4, Supervan 4.2, and most recently, its SuperTruck EV demonstrators.
Like those machines, this electric demonstrator also looks a little out of the ordinary for a race car. It’s most noticeable in profile, where you see the EV prototype is a few inches taller than a Next Gen car, aiming for a crossover-shaped body.
Flax composites
Those body panels might just be the first thing we see translate from the prototype over to competition cars. NASCAR recently moved away from sheet metal for its bodywork, but for this car, it opted to make the body out of flax-based composites from a company called Bcomp.
People have been working on sustainable alternatives to carbon fiber for a while now—we encountered hemp body panels on the Eco Racing Radical in the late 2000s. Plant-based composites are heavier than synthetic carbon composites, but as sustainability becomes an increasingly important aspect of modern racing series, that becomes a trade-off worth making, as Bcomp says its composites have an 85-percent smaller carbon footprint when compared to a traditional composite of similar stiffness.
“Integrating sustainable innovations into the design process helps set the standard for sustainability across our industry and supports forward progress towards the company’s sustainability goals and targets,” said Bcomp’s vice president of vehicle design, Brandon Thomas.
Enlarge/ Bcomp bodywork helps reduce the EV prototype’s carbon footprint.
Bcomp
Just don’t expect to see an all-electric NASCAR race anytime soon. While a battery EV like the prototype you see here would work well on road and street courses as well as short ovals, since all three offer chances to regenerate energy under braking, no one is entirely sure how to make EVs work on superspeedways.
Rather, the car is a way for the sport to gauge fan interest and to promote NASCAR IMPACT, the sport’s new sustainability push. The plan is for ABB to help NASCAR decarbonize its operations, which are responsible for far more of its carbon footprint than the cars running on track. It wants to reduce its footprint to zero by 2035, but a more immediate goal is to use only renewable electricity at its racetracks and facilities by 2028, as well as building out on-site charging stations.
