# Predicting Li-ion Battery Cycle Life with LSTM RNN

arXiv: https://arxiv.org/abs/2207.03687 · HTML: https://mlsanity.com/paper/2207.03687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LSTM Benchmarks for Deep Learning Frameworks](https://mlsanity.com/paper/1806.01818/) (2018)

## Abstract (arXiv)

Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under different conditions. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.