Deep learning powered rapid lifetime classification of lithium-ion batteries

Lithium-ion batteries (LIBs) are currently the primary energy storage devices for modern electric vehicles (EVs). Early-cycle lifetime/quality classification of LIBs is a promising technology for many EV-related applications, such as fast-charging optimization design, production evaluation, battery pack design, second-life recycling, etc. The key challenge of the research problem is to develop an accurate classification method based on very limited early-cycle data, which contain very little information regarding battery degradation. To respond to such emerging need and tackle such technical challenge, this study develops a novel deep learning powered method for enabling the rapid LIB lifetime classification via very limited early-cycle data. First, the proposed method considers an innovative high-dimensional tensor input integrating early-cycle battery voltage, current, and temperature data to organically fuse the spatial, temporal, and physical battery information. Next, a convolutional sparse autoencoder-based feature engineering framework is developed to process such tensor input, automatically extract high-level latent features, and embed high-dimensional input information into a more compact representation. Finally, a regularized logistic regression model is developed to classify batteries into different lifetime groups based on a joint consideration of latent features as well as battery nominal and operational parameters. The effectiveness and robustness of the proposed method is verified on experimental data of battery degradation with three different chemistries and under multiple charge/discharge conditions. The performance of the proposed method is competitive by comparing with a set of well-known and recent benchmarking methods. In scenarios with only first-20-cycle degradation data available, the classification accuracy of the proposed method can reach 96.6%. In scenarios with only first-5-cycle data available, our classification accuracy can still reach 92.1%.