Researchers from the Massachusetts Institute of Technology (MIT) and TU Darmstadt have developed an innovative method for the early detection of battery system faults in electric vehicles (EVs) using advanced machine-learning techniques.

The collaborative research team has created a novel approach that combines physical models with recursive Gaussian processes, enabling real-time monitoring and analysis of battery cell performance. Their work addresses a critical challenge in lithium-ion battery technology: preventing potential catastrophic failures.

“Faults in individual battery cells can lead to serious problems such as fires,” explained researchers from TU Darmstadt’s Institute of Automation Technology and Mechatronics. The new method allows for continuous online monitoring of battery systems, potentially revolutionizing battery safety in electric vehicles and stationary energy storage.

According to TU Darmstadt, the research drew from an extraordinary dataset of 28 battery systems returned to manufacturers due to issues. Comprising over 133 million data series from 224 battery cells, this unique collection provided unprecedented insights into battery degradation and failure mechanisms.

Key findings revealed that often only a single cell within a battery system becomes problematic, yet can compromise the entire system’s performance and safety. The researchers’ machine learning approach can detect time-dependent and operational changes in battery cells with remarkable efficiency.

This research represents a significant step forward in understanding battery aging and failure conditions. By enabling continuous, real-time monitoring, the new methods promise to enhance the safety and reliability of lithium-ion batteries used in EVs and energy storage systems.

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As the world continues to transition towards electrification, such advances in battery technology become increasingly crucial. With this in mind, the collaborative effort between MIT and TU Darmstadt demonstrates the power of interdisciplinary research in addressing complex technological challenges.

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