For decades, motorsport’s promise of “race to road” technology transfer has largely centered on hardware. Better engines, improved aerodynamics, more capable brakes, and increasingly sophisticated batteries have all made the journey from racing circuits to production vehicles. But as Formula E prepares to enter its GEN4 era in 2026, the most important technology transfer may not be a physical component at all. Instead, it could be artificial intelligence.
“AI is the biggest thing to happen to our industry since the birth of the Internet,” says Dan Cherowbrier, CTO, Formula E. “We’re going through the same kind of journey. There’s an awful lot of hype about it, but if even 50% of that hype becomes real, it will be game-changing for how we work.” Behind Formula E’s dramatic leap in performance with its GEN4 car lies a growing reliance on AI, predictive analytics, simulation, and software-driven decision making.
The championship’s engineers increasingly believe that future electric vehicles will not simply carry better batteries and motors. They will actively think about energy and decision making in much the same way that a Formula E race team manages a race. Those same technologies are beginning to reshape the wider automotive industry. The future electric vehicle may not owe its efficiency gains to a larger battery pack or more powerful motor. Instead, it may achieve longer range and better performance because it understands how to use energy more intelligently. Formula E is becoming one of the world’s most important proving grounds for that future.
Formula E Software Becomes The Performance Advantage
One of the most significant shifts occurring within Formula E is that hardware is no longer the primary differentiator. According to Ian James, Team Principal of Jaguar TCS Racing, manufacturers have become remarkably effective at extracting efficiency from modern electric motors and batteries. “We’re seeing efficiencies throughout the powertrain of 91.5% now,” he says.
That figure would have seemed almost unimaginable in traditional motorsport. Internal combustion engineers spent decades celebrating incremental improvements. In Formula E, the industry’s leading manufacturers have pushed electric efficiency so far that the performance gap between competing hardware solutions has narrowed dramatically.
As a result, the focus has shifted elsewhere. “This means that there’s a lot more focus now on the software as the performance differentiator,” James explains. That observation mirrors a broader transformation happening throughout the automotive industry. Increasingly, consumers are choosing vehicles based on software capabilities rather than mechanical specifications. Companies such as Tesla, Rivian, XPENG, NIO, and Volvo are competing as much on digital experiences as they are on horsepower or battery size. Formula E sits at the leading edge of that transition.
The championship’s long-standing partnership with ABB is an example of how Formula E’s role has evolved. ABB has sponsored the championship for over 8 years, since season 4, but its involvement extends far beyond branding. The company serves as Formula E’s charging partner and contributes critical electrification technologies that help power both current racing programs and future development projects, summed up in its slogan “Engineered to outrun”.
One example is how ABB’s technology has been integrated into Formula E’s GenBeta development vehicle. Dan Cherowbrier, Chief Technology Officer at Formula E, explains how an unassuming, compact blue-and-silver unit developed by ABB plays a crucial role inside the car. “This is a DC-to-DC converter that ABB provided as part of their BrightLoop technology product line,” he says.
The converter manages the flow of electricity between the battery and the vehicle’s various electrical systems. While that may sound mundane, the challenge lies in delivering precisely the right amount of power at exactly the right moment. “It’s no different to any other converter in what it does,” says Cherowbrier. “The difference is it must do it at such a precise pace to get it right. That’s where ABB comes in.”
The relationship works both ways. Formula E gains access to advanced electrification technologies, while ABB gains something equally valuable: a highly demanding real-world testing environment. “We provide them with a test bed, a living laboratory that gives them the opportunity to try new things,” says Cherowbrier. Unlike industrial installations, race cars operate at the limits of performance. New ideas can be tested, refined, and validated under extreme conditions before influencing future commercial products.
As charging partner, ABB also provides all the facilities to replenish the Formula E cars between sessions. This could involve sending power to up to 20 cars at once. This doesn’t need to be at the 600kW level of the in-race Pit Boost system but still requires careful logistical management at a location that may not have a grid connection enough on its own to provide this. Buffer batteries and load balancing are involved, which is a similar solution to any busy public charging location for personal EVs. Everything learned can be transferred to ABB’s commercial charger installations.
How Formula E Delivers AI’s Biggest Opportunity Since The Internet
While ABB’s hardware contributions are important, increasingly the value comes from software. Cherowbrier’s comparison between AI and the Internet regarding its impact on racing reflects how rapidly AI has moved from theoretical possibility to practical tool. Formula E has already launched projects with partners including Google and ABB, experimenting with applications ranging from broadcasting to operational efficiency.
One example involves race coverage itself. “There’s a pop up that comes on screen with insights, leveraging data,” explains Cherowbrier. “It’s predicting what’s going to happen based on data, rather than analyzing what needs to happen.” This seemingly small change highlights a fundamental shift. While traditional analytics explain the past, AI can increasingly predict the future.
That distinction matters enormously for both motorsport and road cars. A modern EV already knows how much charge remains in its battery. Future AI-driven vehicles could continuously predict how weather, traffic, topography, driver behavior, and charging infrastructure will affect energy consumption long before the driver notices. Instead of reacting to circumstances, the vehicle will anticipate them.
Thomas Chevaucher, FIA Technical Engineering Director, believes that understanding how energy is used may ultimately prove more valuable than the hardware itself. “There is a technology aspect, including the battery, efficiency of the motors and so on,” he says. “But a very important part is the way you are driving it.”
Formula E has always been an efficiency championship. Drivers constantly balance speed against energy consumption, seeking the optimal compromise between performance and conservation. As engineers gain deeper insight into those decisions, they are discovering that software can help guide drivers toward more efficient behavior.
“The goal is to find a way to teach customers to drive their cars in an efficient way,” says Chevaucher. That concept is already visible in production EVs through regenerative braking systems and one-pedal driving modes. But AI promises to make those systems far more sophisticated. Imagine a vehicle that understands your daily commute, predicts traffic patterns, monitors weather forecasts, and adapts its energy management strategy accordingly. Instead of merely displaying range, it actively helps maximize efficiency throughout every journey. In effect, the car becomes its own race engineer.
Formula E’s Digital Twins And Virtual Drivers
Perhaps nowhere is the convergence between Formula E and road-car development more obvious than simulation. Jaguar TCS Racing has worked closely with Tata Consultancy Services to develop what it calls its Virtual Vehicle Validation Model, or V3M. “This is a fundamental element of our simulation work,” says James. The system allows engineers to model vehicle behavior before a race weekend begins and continue refining their understanding throughout the event. Every lap generates new information, although this is only downloaded at the end of each race rather than live during the event.
Simulation produces new insights, too. The automotive industry increasingly uses similar digital twin technologies to design vehicles, validate software, and optimize battery systems long before physical prototypes exist. However, the pace of innovation is different from racing. Formula E compresses development cycles into weeks or months. What might take an automaker several years to learn through customer fleets can often be discovered over the course of a single racing season.
As AI becomes more powerful, a new challenge is emerging. Engineers now have access to more information than ever before. Frédéric Bertrand, CEO and Team Principal of Mahindra Racing, sees both opportunities and risks from this. “AI now for sure is coming massively,” he says. Mahindra already uses data-driven tools to improve strategy, analyze energy consumption, and understand vehicle performance.
But Bertrand believes AI’s most valuable contribution may be filtering information rather than generating it. “AI helps a lot with this, because you can have an initial management of the data, which just eliminates all the noise around the real topic,” he says. That capability is becoming increasingly relevant to production vehicles. Modern EVs generate enormous quantities of data through cameras, radar systems, battery management systems, power electronics, and vehicle sensors. The challenge is no longer collecting information, but determining which information matters. AI excels at identifying patterns that humans might overlook. As vehicles become increasingly connected and autonomous, that capability will only become more important.
From Formula E Race Engineers To Digital Assistants
Former Formula E champion Lucas di Grassi has spent years exploring the intersection between motorsport and artificial intelligence. Long before today’s generative AI boom, he was involved with Roborace, an autonomous racing initiative that explored how machine intelligence could be applied to high-performance vehicles. Some of the teams from Roborace have subsequently joined Abu Dhabi’s A2RL autonomous racing competition.
Now di Grassi sees AI creating opportunities across Formula E’s entire ecosystem. “There is the AI implementation into the sport,” he says. “There are so many areas that you could work in.” He believes AI could eventually help teams optimize energy management, accelerate development, improve strategy, and process information more efficiently. But he also recognizes that motorsport remains fundamentally human. “You could augment a lot how you teach humans, with an AI coach, or using AI to optimize your technique, or to process the data and give the right feedback.”
That observation may provide the clearest glimpse of the automotive future. The goal isn’t necessarily to replace drivers, so much as support them. Future EVs may function as intelligent assistants, constantly analyzing conditions and offering guidance in the background. Drivers remain in control, but software helps them achieve outcomes that would otherwise be impossible.
Formula E’s early years focused on proving that electric racing could work. The GEN4 era will showcase how far electric performance has progressed. But beneath the headlines about acceleration and power lies a deeper story. The championship is becoming a laboratory for energy intelligence. Supported by partners such as ABB, Formula E teams are learning how to predict energy usage, optimize efficiency, manage vast quantities of data, and make better decisions through AI.
Those lessons extend far beyond motorsport. The next generation of road cars won’t simply contain more advanced batteries or faster charging systems. They will increasingly understand how to use those technologies intelligently. The most important Formula E innovation may not be a motor, battery, or inverter. It may be the software that decides how to use them, and that software is already teaching tomorrow’s road cars how to think about energy, with the help of AI.
