GCP – Pushing the limits of electric mobility: Formula E’s Mountain Recharge

When’s the last time you watched a race for the braking?

It’s the heart-pounding acceleration and death-defying maneuvers that keep most motorsport fans on the edge of their seats. Especially when it comes to Formula E — and really all EVs — the explosive, near-instantaneous acceleration of an electric motor is part of the appeal.

A less considered, yet no less important feature, is how EVs can regeneratively brake, turning friction into fuel. Part of Formula E’s mission is to make EVs a compelling automotive choice for consumers, not just world-class racers; highlighting this powerful aspect of the vehicles has become a priority. The question remained: How do you get others to feel the same exhilaration from deceleration?

The answer came from the mountains above Monaco, as well as some prompts in Gemini 2.5.

In the lead up to the Monaco E-Prix, Formula E and Google undertook a project dubbed Mountain Recharge. The challenge: Whether a Formula E GENBETA race car, starting with only 1% battery, could regenerate enough energy from braking during a descent through France’s coastal Alps to then complete a full lap of the iconic Monaco circuit.

More than just a stunt, this experiment is testing the boundaries of technology — and not just in EVs, but on the cloud, too. Without the live analytics and plenty of AI-powered planning, the Mountain Recharge might not have come to pass. In fact, AI even helped determine which mountain pass would be best suited for this effort. (Read on to find out which one, and see if we made it to the bottom.) 

Mountain Recharge is exciting not only for thrills on the course but also the potential it shows for AI across industries. In addition to its role in helping to execute tasks, AI proved valuable to the brainstorming, experimentation, and rapidfire simulations that helped get Mountain Recharge to the finish line.

Planning the charge up the mountain

Before even setting foot or wheel to the course, the team at Formula E and Google Cloud turned to Gemini to try and figure out if such an endeavor was possible.

To answer the fundamental question of feasibility, the team entered a straightforward prompt into Google’s AI Studio: “Starting with just 1% battery, could the GENBETA car potentially generate enough recharge by descending a high mountain pass to do a lap of the Circuit of Monaco?” 

The AI Studio validator, running Gemini 2.5 Pro with its deep reasoning functionality, analyzed first-party data that had been uploaded by Formula E on the GENBETA’s capabilities; we then grounded the model with Google Search to further improve accuracy and reliability by connecting to the universe of information available online.

AI Studio shared its “thinking” in a detailed eight-step process, which included identifying the key information needed; consulting the provided documents; gathering external information through a simulated search; performing calculations and analysis; and finally synthesizing the answer based on the core question. 

The final output: “theoretically feasible.” In other words, the perfect challenge.

Still working in AI Studio, we then used a new feature, the ability to build custom apps such as the Maps Explorer, to determine the best route, which turned out to be the Col de Braus. AI Studio then mapped out a route for the challenge. This rigorous, data-backed validation, facilitated by AI Studio and Gemini’s ability to incorporate technical specifications and estimations, transformed the project from a speculative what-if into something Formula E felt confident attempting.

AI played an important role away from the course, as well. To aid in coordination and planning, teams at Formula E and Google Cloud used NotebookLM to digest the technical regulations and battery specifications and locate relevant information within them, which, given the complexity of the challenge and the number of parties involved, helped ensure cross-functional teams were kept up to date and grounded with sourced data to help make informed decisions.

Smart cars, smart drivers, and a smartphone

During the mountain descent, real-time monitoring of the car’s progress and energy regeneration would be crucial. Firebase and BigQuery were instrumental in visualizing this real-time telemetry. Data from both multiple sensors and Google Maps was streamed to BigQuery, Google Cloud’s data warehouse, from a high-performance mobile phone connected to the car (a Pixel 9 was well suited to the task). 

This data stream proved to be yet another challenge to overcome, because of the patchy mobile signal in the mountainous terrain of the Maritime Alps. When data couldn’t be sent, it was cached locally on the phone until the signal was available again.

BigQuery’s capacity for real-time data ingestion and in-platform AI model creation enabled speedy analysis and the calculation of essential metrics. A web-based dashboard was developed using Firebase that connected to BigQuery to display both data and insights. AI Studio greatly facilitated the development of the application by translating a picture of a dashboard mockup into fully functional code.

“From figuring out if our crazy Mountain Recharge idea was even possible, to giving us live insights during the descent, AI was our guide,” said Alex Aidan, Formula E’s VP of Marketing. “It’s what turned an ambitious ‘what if’ into a reality we could track moment by moment.” 

After completing its descent, the car stored up enough energy that it is expected to complete its lap of the Monaco circuit on Saturday, as part of the E-Prix’s pre-race festivities.

Benefits beyond the finish line

Both the success and the development of the Mountain Recharge campaign offer valuable lessons to others pursuing ambitious projects. It shows that AI doesn’t have to be central to a project — it can be just as powerful at facilitating and optimizing something we’ve been doing for years, like racing cars. Our results in the Mountain Recharge only underscores the potential benefits of AI for a wide range of industries:

• Enhanced planning and exploration: Just as Gemini helped Formula E explore unconventional ideas and identify the optimal route, businesses can leverage large language models for innovative problem-solving, market analysis, and strategic planning, uncovering unexpected angles and accelerating the journey from “what if” to “we can do that”.

• Streamlined project management: NotebookLM’s ability to centralize and organize vast amounts of information demonstrates how AI can significantly improve efficiency in complex projects, from logistics and resource allocation to research and compliance. This reduces the risk of errors and ensures smoother coordination across teams.

• Data-driven decision making: The real-time data analysis capabilities showcased in the Mountain Recharge underscore the power of cloud-based data platforms like BigQuery. Organizations can leverage these tools to gain immediate insights from their data, enabling them to make agile adjustments and optimize performance on the fly. This is invaluable in dynamic environments where rapid responses are critical.

• Deeper understanding of complex systems: By applying AI to analyze intricate data streams, teams can gain a more profound understanding of the factors influencing performance. 

Such capabilities certainly impressed James Rossiter, a former Formula E Team Principal, current test driver, and broadcaster for the series. “I was really surprised at the detail of the advice and things to consider,” Rossiter said. “We always talk about these things as a team, but as this is so different to racing, I had to totally rethink the drive.”

The Formula E Mountain Recharge campaign is more than just an exciting piece of content; it’s a testament to the power of human ingenuity amplified by intelligent technology. It’s also the latest collaboration between Formula E and Google Cloud and our shared commitment to use AI to push the boundaries of what’s possible in the sport in the sport and in the world. 

We’ve already developed an AI-powered digital driving coach to help level the field for EV racing. Now, with the Mountain Recharge, we can inspire everyday drivers well beyond the track with the capabilities of electric vehicles. 

It’s thinking big, even if it all starts with a simple prompt on a screen. You just have to ask the right questions, starting with the most important ones: Is this possible, and how can we make it so?