  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.
