MIT Researchers Use Sound to Predict Battery Degradation

View profile for Sagar Arora

Material Science | Li-ion Batteries | Polymers | Machine Learning | Data science | Design of Experiments

MIT Researchers Decode Battery Sounds to Predict Degradation Correlating electrochemical data with acoustic data MIT's Department of Chemical Engineering has pioneered a method to monitor lithium-ion battery health by analyzing acoustic emissions during charging and discharging. Their study, published in Joule, identifies specific sound patterns linked to internal degradation processes such as gas bubble formation and material fractures. This innovative approach enables real-time, non-invasive monitoring of battery systems, offering potential applications in electric vehicles and grid-scale storage. By correlating acoustic data with electrochemical performance, the researchers have developed a cost-effective technique to predict battery lifespan and detect early signs of failure. This advancement could significantly enhance battery management strategies, leading to safer and more efficient energy storage solutions. https://lnkd.in/gU_ZaexB #BatteryTechnology #AcousticEmissions #EnergyStorage #ElectricVehicles #MITResearch

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