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Predicting Li-ion Battery Cycle Life with LSTM RNN. (arXiv:2207.03687v1 [cs.LG])
July 11, 2022, 1:10 a.m. | Pengcheng Xu, Yunfeng Lu
cs.LG updates on arXiv.org arxiv.org
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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