  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.
