  This work presents an effective state of health indicator to indicate
lithium-ion battery degradation based on a long short-term memory (LSTM)
recurrent neural network (RNN) coupled with a sliding-window. The developed
LSTM RNN is able to capture the underlying long-term dependencies of degraded
cell capacity on battery degradation stress factors. The learning performance
was robust when there was sufficient training data, with an error of < 5% if
more than 1.15 years worth of data was supplied for training.
