A Recurrent Neural Network Model for Battery Capacity Fade Curve Prediction Using Early Life Data

Abstract

Batteries are often considered the most performance and reliability-limiting component of any electronic and electrical assembly. Thus their degradation trend characterization through extensive testing and development of life prediction algorithms using testing data have been at the forefront of battery research. However, battery testing is also time-consuming, and it can take at least few months, even years, to test a battery for cycle life performance under normal operating conditions. Hence, there is a need to develop tools for battery life and degradation trend prediction using limited cycle data. Early life prediction tools can facilitate battery qualification, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we present a recurrent neural network-linear network model to predict the entire battery capacity fade curve using less than 100 cycles of data (~ 1 month of testing). The discharge voltage-capacity curves have been used as input to the model. Our approach is also able to predict the rollover point (knee point) in the capacity fade curve of the battery, which indicates the onset of increased capacity fade rate. We use the publicly available MIT dataset to show the validity of the approach and extend the previous work from Severson et al. [1] on the end-of-life prediction to the entire capacity fade trend prediction. The recurrent neural network can capture the sequential changes in discharge voltage-capacity curves with cycles and provide flexibility in terms of the input data size.