Algorithm for Predicting Risk of Lithium Battery Failure Based on Electron Microscopy Data
Researchers at the University of California San Diego have developed a groundbreaking method to characterize the performance of lithium metal batteries using scanning electron microscopy (SEM). This innovative approach could lead to safer, longer-lasting, and more energy-dense batteries for electric vehicles and grid-scale energy storage.
The method, published in the Proceedings of the National Academy of Sciences, involves a simple algorithm that analyses how evenly lithium is spread across SEM images. To use the method, researchers first take SEM images of battery electrodes and convert them to black and white pixels. White pixels represent lithium deposits, while black pixels represent the substrate or inactive lithium. The algorithm then divides these images into multiple regions, counts the white pixels in each, and calculates a metric called the index of dispersion (ID). This metric quantifies lithium uniformity: the closer the ID is to zero, the more uniform the lithium deposits; a higher ID indicates more clustering and nonuniformity.
The team validated the method on 2,048 synthetic SEM images with known particle size distributions. When applied to real lithium metal battery electrode images over cycling time, the ID increased as batteries aged, indicating more uneven lithium deposits. This increase in ID correlated with an increase in energy required for lithium deposition, signaling degradation.
Importantly, the algorithm detected local peaks and dips in the ID just before battery cells failed, providing an early warning of short circuits and failure risk. This approach improves on previous subjective visual assessments by creating a standardized, quantitative measure of lithium deposition uniformity. It can serve as a predictive tool for battery lifespan and safety, facilitating more consistent communication among researchers and aiding in optimizing battery cycling protocols to extend battery life and prevent dangerous failures.
Lithium metal batteries have the potential to store twice as much energy as today's lithium-ion batteries. Controlling lithium morphology, or how lithium deposits on the electrodes during charging and discharging, is a longstanding challenge in lithium metal battery research. Uneven lithium deposition can form needle-like structures known as dendrites that can cause the battery to short-circuit and fail. The UCSD algorithm offers a promising solution to this problem by providing a quantitative, standardized method for assessing lithium deposition uniformity.
The team's tool can enhance battery research by employing image analysis to its fullest potential. By leveraging SEM imaging for detailed, 2D grayscale electrode surface characterization, using the index of dispersion metric to quantify and track lithium deposit uniformity, and predicting failure risk through characteristic ID changes preceding cell failure, the UCSD algorithm is set to revolutionize the field of lithium metal battery research.
[1] Proceedings of the National Academy of Sciences [2] University of California San Diego [5] Nature Energy
Note: This article is generated by AI and has not been peer-reviewed or edited by a human.
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