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Case study

Data-driven Lifetime Prediction and Charging Optimization: Predicting Cycle Life In The First 10 Cycles

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Leveraging a data-driven cycle life prediction algorithm inspired by Severson and Attia et al.’s work in Nature Energy, one battery manufacturer is now using the Aionics platform to predict the long-term cycle life of new cells after cycling them only ten times, representing a 11X+ acceleration in the otherwise time-consuming testing cycle. These models continue to improve through work within Aionics as well as work at the Stanford StorageX initiative.

To hear more about this successful implementation of a data-driven cycle life prediction model, check out Aionics Fortnightly Episode 6: Data-Driven Predictions of Battery Cycle Life from Early Cycling Data.

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