AI Being Applied to Improve Health, Better Predict Life of Batteries

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Researchers are employing AI techniques to study how to extend the life of batteries needed to power next generation devices. (Thomas Kelley on Unsplash)

By AI Trends Staff

AI techniques are being applied by researchers aiming to extend the life and monitor the health of batteries, with the aim of powering the next generation of electric vehicles and consumer electronics.

Researchers at Cambridge and Newcastle Universities have designed a machine learning method that can predict battery health with ten times the accuracy of the current industry standard, according to an account in ScienceDaily. The promise is to develop safer and more reliable batteries.

In a new way to monitor batteries, the researchers sent electrical pulses into them and monitored the response. The measurements were then processed by a machine learning algorithm to enable a prediction of the battery’s health and useful life. The method is non-invasive and can be added on to any battery system.

The inability to predict the remaining useful charge in lithium-ion batteries is a limitation to the adoption of electric vehicles, and annoyance to mobile phone users. Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. The new methods capture more about what is happening inside the battery and can better detect subtle changes.

“Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space,” stated Dr. Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research. “By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance.”

Dr. Alpha Lee, Cavendish Laboratory, Cambridge University

The researchers performed over 20,000 experimental measurements to train the model in how to spot signs of battery aging. The model learns how to distinguish important signals from irrelevant noise. The model learns which electrical signals are most correlated with aging, which then allows the researchers to design specific experiments to probe more deeply why batteries degrade.

“Machine learning complements and augments physical understanding,” stated co-author Dr Yunwei Zhang, also from the Cavendish Laboratory, in .”The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

Department of Energy Researchers Using AI Computer Vision Techniques

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory are using AI computer vision techniques to study battery life. The scientists are combining machine learning algorithms with X-ray tomography data to produce a detailed picture of degradation in one battery component, the cathode, according to an account in SciTechDaily. The referenced study was published in Nature Communications.

Dr. Yunwei Zhang, Cavendish Laboratory, Cambridge University

For cathodes made of nickel-manganese-cobalt (NMC) particles are held together by a conductive carbon matrix. Researchers have speculated that a cause of battery performance decline could be particles breaking away from that matrix. The team had access to advanced capabilities at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL), a unit of the Department of Energy operated by Stanford University, and the European Synchrotron Radiation Facility (ESRF), a European collaboration for the advancement of X-rays, based in Grenoble, France. The goal was to build a picture of how NMC particles break apart and away from the matrix, and how that relates to battery performance loss.

The team turned to computer vision with AI capability to help conduct the research. They needed a machine learning model to train the data in how to recognize different types of particles, so they could develop a three-dimensional picture of how NMC particles, large or small, break away from the cathode.

The authors encouraged more research into battery health. “Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity,” the authors stated.

(Citation: Jiang, Z., Li, J., Yang, Y. et al. Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes. Nat Commun 11, 2310 (2020). https://doi.org/10.1038/s41467-020-16233-5)

(For an account of how researchers from Stanford University, MIT and the Toyota Research Institute are studying radical reductions in electric-vehicle charging times, see AI Trends.).

See the source articles in ScienceDaily, SciTech Daily and Nature Communications.

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