In a remarkable blend of machine intelligence and electric mobility, the ABB FIA Formula E World Championship recently achieved a technological milestone rarely witnessed in the world of motorsports. During a demonstration held in the Dolomite Mountains of northern Italy, Formula E engineers—backed by Google Cloud’s artificial intelligence—showcased a potential paradigm shift for electric racing: optimizing regenerative energy to conquer steep terrains.

At the center of the effort was Formula E’s GENBETA electric race car, tasked with a two-part challenge. First, the vehicle would ascend a 1,531-meter climb up the Passo Valparola, a notorious alpine pass. Following this energy-intensive ascent, the car would regenerate maximum energy through state-of-the-art Monte Carlo-based AI software as it descended the four-kilometer stretch back toward the mountain’s base.

Formula E said this was the first time such advanced AI-driven simulation and data analysis technology, developed with Google Cloud, had been used in real-time to inform energy and regeneration strategy during a live electric vehicle demonstration.

“AI-powered decision-making is helping us unlock new frontiers for electric motorsport, both on and off the track,” Formula E shared in its release. The event underscored how artificial intelligence can extend the range and performance of electric vehicles (EVs) by constantly recalculating the most optimal energy usage—and crucially, how much energy can be recuperated under challenging conditions.

The demonstration follows a broader trend in automotive and tech circles: harnessing cloud computing and AI to tackle range anxiety and enhance the practical use-case for EVs, especially outside the urban environment. For the Formula E demo, Google Cloud deployed machine learning algorithms to predict the car’s energy requirements and capacity for regeneration based on terrain, speed, temperature, and battery state.

The entire process leveraged Vertex AI, Google’s integrated platform for building and deploying machine learning models, and BigQuery, Google Cloud’s data analytics engine. The architecture enabled engineers to compare real-time sensor data against AI-driven predictions, optimizing on-the-fly decision making. Engineers described an ability to make “millions of simulations within minutes” to pinpoint the optimum regeneration levels.

For fans and engineers alike, regenerative braking is not a new concept—it allows EVs to recover and store energy otherwise lost during braking. However, optimizing this process dynamically—adjusting for factors such as incline, descent speed, and available grip—remains a computationally complex task, especially in high-performance environments like racing.

While the demonstration was not part of an official championship race, Formula E and Google Cloud said the findings could pave the way for smarter race strategies and further bridge the gap between race-track technology and road-ready EV products.

“This work highlights the very real potential of AI to fundamentally enhance electric vehicle efficiency,” said Formula E in the announcement.

Broader commercial implications loom large. As automakers and technology providers invest in extending battery life and improving user experience, predictive energy management—especially in challenging geographies—is expected to play a critical role in mass EV adoption. Formula E’s demonstration with Google Cloud signals a step toward data-driven energy management that could eventually appear in consumers’ vehicles, enabling smarter route planning and better use of regenerative braking on mountainous drives.

The collaboration represents a growing intersection between the motorsport world’s relentless drive for efficiency and Silicon Valley’s prowess in artificial intelligence. With electric mobility’s rise, data-driven solutions such as those showcased in the Dolomites may soon move from experimental challenges to everyday realities.