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arXiv · 2404.17174

Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model

Constantin-Daniel Nicolae·Sara Sameer·Nathan Sun·Karena Yan
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Abstract

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.

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Constantin-Daniel Nicolae, Sara Sameer, Nathan Sun, Karena Yan. 2025-03-21. Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model. https://arxiv.org/abs/2404.17174

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