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

2022/05/16 by Uddin, Kotub, Schofield, James, Widanage, W. Dhammika
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2205.07561

Abstract

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.

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