Researchers at Chalmers University of Technology have shown that it is possible to combine rapid charging with significantly reduced battery wear using AI.

In a new study, the researchers present an AI-based charging strategy that extends battery lifetime by 22.9 per cent compared to today’s standard method, without increasing charging time.

When a battery is charged rapidly, high currents are pushed into the cell. This can trigger unwanted side reactions. One of the most critical is lithium plating, where metallic lithium deposits on the electrode instead of being properly stored within the battery’s structure. Lithium plating reduces capacity, increases internal resistance, and in severe cases can affect safety. The risk becomes more pronounced as the battery ages.

Using the new method, the battery achieved a 22.9 per cent extension of its lifetime compared with the conventional charging approach. At the same time, charging time remained virtually unchanged: 24.12 minutes on average, compared to 24.15 minutes for the standard method.

Conventional charging strategies use fixed voltage and current limits, regardless of whether the battery is new or has already been used for several years. but researchers point out that batteries change over time but charging strategies typically do not.

The new method is based on ‘reinforcement learning’, a type of machine learning in which an algorithm learns by interacting with an environment and gradually improving its decisions. The AI system is trained to find a charging strategy that keeps charging time short while minimising harmful degradation mechanisms. By being rewarded for good long-term outcomes, the algorithm learns how to adapt charging current dynamically.

An important advantage of the approach is that the trained AI model does not require specialised laboratory sensors during operation. In principle, the strategy could therefore be implemented through software updates in existing battery management systems.

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