Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics

TL;DR

This paper evaluates the effectiveness of data-driven methods for predicting Li-ion battery lifetime across various aging conditions, highlighting their potential and limitations in real-world applications.

Contribution

It demonstrates the conditions under which data-driven battery prognostics are reliable and identifies challenges when applying these methods in practical scenarios.

Findings

Correlation between capacity variance and end-of-life across diverse aging conditions

Features from cycling data can predict battery life without slow characterization cycles

Reduced voltage data window diminishes prediction accuracy

Abstract

Recent data-driven approaches have shown great potential in early prediction of battery cycle life by utilizing features from the discharge voltage curve. However, these studies caution that data-driven approaches must be combined with specific design of experiments in order to limit the range of aging conditions, since the expected life of Li-ion batteries is a complex function of various aging factors. In this work, we investigate the performance of the data-driven approach for battery lifetime prognostics with Li-ion batteries cycled under a variety of aging conditions, in order to determine when the data-driven approach can successfully be applied. Results show a correlation between the variance of the discharge capacity difference and the end-of-life for cells aged under a wide range of charge/discharge C-rates and operating temperatures. This holds despite the different conditions being used not only to cycle the batteries but also to obtain the features: the features are calculated directly from cycling data without separate slow characterization cycles at a controlled temperature. However, the correlation weakens considerably when the voltage data window for feature extraction is reduced, or when features from the charge voltage curve instead of discharge are used. As deep constant-current discharges rarely happen in practice, this imposes new challenges for applying this method in a real-world system.