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

State of Health Estimation of Lithium-Ion Batteries in Vehicle-to-Grid  Applications Using Recurrent Neural Networks for Learning the Impact of  Degradation Stress Factors