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arXiv · 2207.03687

Predicting Li-ion Battery Cycle Life with LSTM RNN

Pengcheng Xu·Yunfeng Lu
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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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Pengcheng Xu, Yunfeng Lu. 2022-07-08. Predicting Li-ion Battery Cycle Life with LSTM RNN. https://arxiv.org/abs/2207.03687

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