Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
<|reference_start|>Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model: 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.<|reference_end|>
@article{nicolae2024optimizing,
    title={Optimizing Cycle Life Prediction of Lithium-ion Batteries via a
  Physics-Informed Model},
    author={Constantin-Daniel Nicolae, Sara Sameer, Nathan Sun, Karena Yan},
    journal={arXiv preprint arXiv:2404.17174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17174},
    primaryClass={cs.LG}
}