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

arXiv: https://arxiv.org/abs/2404.17174 · HTML: https://mlsanity.com/paper/2404.17174/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)

## Built on by (most cited first, 1 of 1)

- [Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation](https://mlsanity.com/paper/2512.11332/) (2025)

## Abstract (arXiv)

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.