AI Model Promises Longer Life and Safer Batteries for Electric Vehicles

Wendi Guo and Daniel Brandell have developed the model that makes it possible to better understand what happens inside batteries. Photo: Mikkel Lønsman Hukiær/Tobias Sterner, Bildbyrån

One of the biggest hurdles facing the electrification of transportation is the limited lifespan of electric vehicle (EV) batteries. These batteries, often the first component in a vehicle to degrade, not only shorten the useful life of EVs but also create waste and raise costs for consumers. Now, researchers at Uppsala University have developed an artificial intelligence model that offers a more precise way to track and predict battery ageing—potentially paving the way for safer, longer-lasting batteries.

The new AI-based model has shown the ability to improve the robustness of battery health predictions by up to 70 percent, compared with existing methods. This breakthrough could give automakers more reliable tools for battery management, ultimately supporting the broader shift from fossil fuel vehicles to sustainable electric mobility.

Tackling the Battery Ageing Problem

Battery degradation has long been a bottleneck in EV adoption. While the motors, electronics, and structural components of electric cars can last for years, batteries are often the weak link. Their reduced performance and capacity loss over time lead to shorter driving ranges, higher maintenance costs, and premature replacement—undermining consumer confidence in EVs.

To tackle this issue, the automotive industry has been investing heavily in software, including AI-driven battery management systems (BMS). These systems are designed to monitor battery health, optimize charging, and extend overall lifespan. The new model from Uppsala University marks a major advancement in this field.

“Being able to learn more about the life and ageing of batteries will benefit future control systems in electric vehicles,” explained Professor Daniel Brandell, who leads the study and heads the Ångström Advanced Battery Centre at Uppsala University. “It also shows how important it is to understand what happens inside the batteries. If we stop looking at them as black boxes that are simply expected to provide power, and instead acquire a detailed picture of the processes, we can manage them so that they stay in good condition longer.”

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Mapping the Battery’s Life Cycle

The research is the result of several years of extensive battery testing, conducted in collaboration with Aalborg University in Denmark. To build the model, the team constructed a database using numerous very short charging segments. These were then combined with a detailed electrochemical model that represents the many chemical processes occurring within the battery.

According to Wendi Guo, who conducted the study, this approach provides an unprecedented level of detail: “Altogether, this gives us a very precise picture of the various chemical reactions that result in the battery generating power, but also of how it ages during use.”

By marrying real-world data with electrochemical simulations, the AI model can effectively map out the entire life cycle of a battery. This makes it possible to predict not only how long a battery will last but also how different usage patterns, charging behaviors, and environmental conditions will influence its health.

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Enhancing Safety Alongside Longevity

Beyond improving battery life predictions, the model could also have significant implications for EV safety. Safety issues in batteries often arise from design flaws or unwanted side reactions within the cells. By analyzing data from charging and discharging, the AI system can detect these risks earlier, helping to prevent dangerous malfunctions such as overheating or thermal runaway.

Another key advantage is that the model does not require complete datasets from long-term battery use. Instead, by focusing on short charging cycles, researchers can gather enough information to make accurate predictions while sidestepping the challenges of collecting sensitive, proprietary vehicle data.

“The fact that we only use short charging segments is probably an added advantage,” Brandell said. “Battery data from electric vehicles is sensitive, both for the industry and from an anonymisation point of view for users. This research shows how far you can get without needing complete datasets.”

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A Step Toward Sustainable Electrification

The findings highlight how artificial intelligence and advanced battery science can work hand in hand to accelerate the transition to clean transportation. By giving automakers and drivers tools to better understand and manage EV batteries, the new model could reduce waste, cut costs, and increase confidence in electric vehicles.

For the wider transport sector, breakthroughs like this represent more than just a technical milestone—they are essential to achieving climate goals. As nations push toward phasing out fossil fuel vehicles, innovations that extend the life and safety of EV batteries will play a critical role in making the transition both sustainable and affordable.

“There is still much to learn about batteries, but this is a promising step forward,” Brandell concluded. “Better models and better understanding mean better management—and ultimately, longer-lasting and safer batteries for electric vehicles.”