Path Signature-Based Life Prognostics of Li-ion Battery Using Pulse Test Data

Common models predicting the End of Life (EOL) and Remaining Useful Life (RUL) of Li-ion cells make use of long cycling data samples. This is a bottleneck when predictions are needed for decision-making but no historical data is available. A machine learning model to predict the EOL and RUL of Li-ion cells using only data contained in a single Hybrid Pulse Power Characterization (HPPC) test is proposed. The model ignores the cell’s prior cycling usage and is validated across nine different datasets each with its cathode chemistry. A model able to classify cells on whether they have passed EOL given an HPPC test is also developed. The underpinning data-centric modeling concept for feature generation is the notion of ‘path signature’ which is combined with an explainable tree-based machine learning model and an in-depth study of the models is provided. Model validation across different SOC ranges shows that data collected from the HPPC test across a 20% SOC window suffices for effective prediction. The EOL and RUL models achieve 85 and 91 cycles MAE respectively while the classification model has an accuracy of 94% on the test data. Code for data processing and modelling is publicly available.