Predicting Li-ion Battery Cycle Life with LSTM RNN

Pengcheng Xu, Yunfeng Lu
7/8/2022
cs.LGeess.SP

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Abstract

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.

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Cite this paper

@article{xu2022predicting,
  title  = {Predicting Li-ion Battery Cycle Life with LSTM RNN},
  author = {Pengcheng Xu and Yunfeng Lu},
  year   = {2022},
  eprint = {2207.03687},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.03687v1}
}

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