Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
Nicolae, Constantin-Daniel, Sameer, Sara, Sun, Nathan, Yan, Karena
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Apr-26-2024
- Genre:
- Research Report (1.00)
- Industry:
- Electrical Industrial Apparatus (1.00)
- Energy > Energy Storage (1.00)
- Materials > Metals & Mining
- Lithium (0.45)
- Transportation > Ground
- Road (0.47)
- Technology: