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

TL;DR

This paper explores using deep sequence models with attention mechanisms to predict battery lifespan and classify battery health, achieving competitive results with existing models.

Contribution

It introduces a data-driven deep learning approach with optional convolution and attention layers for battery life prediction and classification.

Findings

Achieved 95% accuracy in battery health classification.

Attained 12.5% MAPE in lifespan prediction.

Competitive results compared to state-of-the-art models.

Abstract

We use data on 124 batteries released by Stanford University to first try to solve 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 than a certain threshold of cycles), as well as the prediction problem of determining the exact number of cycles a battery will last given the first 100 cycles 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 that the Stanford team engineered by hand. For both problems, we used a similar deep network design, that included an optional 1-D convolution, LSTMs, an optional Attention layer, followed by fully connected layers to produce our output. For the classification task, we were able to achieve very competitive results, with validation accuracies above 90%, and a test accuracy of 95%, compared to the 97.5% test accuracy of the current leading model. For the prediction task, we were 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).