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

Predicting Li-ion Battery Cycle Life with LSTM RNN