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We use data on 124 batteries released by Stanford University to first try tosolve the binary classification problem of determining if a battery is "good"or "bad" given only the first 5 cycles of data (i.e., will it last longer thana certain threshold of cycles), as well as the prediction problem ofdetermining the exact number of cycles a battery will last given the first 100cycles of data. We approach the problem from a purely data-driven standpoint,hoping to use deep learning to learn the patterns in the sequences of data thatthe Stanford team engineered by hand. For both problems, we used a similar deepnetwork design, that included an optional 1-D convolution, LSTMs, an optionalAttention layer, followed by fully connected layers to produce our output. Forthe classification task, we were able to achieve very competitive results, withvalidation accuracies above 90%, and a test accuracy of 95%, compared to the97.5% test accuracy of the current leading model. For the prediction task, wewere also able to achieve competitive results, with a test MAPE error of 12.5%as compared with a 9.1% MAPE error achieved by the current leading model(Severson et al. 2019).

Pay Attention: Leveraging Sequence Models to Predict the Useful Life of Batteries