Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition.
@article{arxiv.2404.17174,
title = {Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model},
author = {Constantin-Daniel Nicolae and Sara Sameer and Nathan Sun and Karena Yan},
journal= {arXiv preprint arXiv:2404.17174},
year = {2025}
}