soh estimation

soc estimation

battery management

Journal of power sources

Early prediction of battery performance degradation trends can facilitate research of new materials and cell designs, rapid deployment of batteries in real-world applications, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we design a convolutional neural network model to predict the entire battery capacity fade curve – a critical indicator of battery performance degradation – using first 100 cycles of data (∼ three weeks of testing). We use the discharge voltage-capacity curves as input to the model and automate the feature extraction process through the convolutional layers of the network. Our approach can predict the per cycle capacity fade rate and rollover cycle (knee point) in the capacity fade curve, which indicate the onset of rapid capacity decay. On the publicly available graphite/LiFePO 4 battery dataset, optimized networks predict the capacity fade curves, rollover cycle, and end of life with 3.7% (worst-case), 19%, and 17% mean absolute percentage errors, respectively. • A convolutional neural network to predict the entire battery capacity fade trend. • Capacity fade trend prediction using first 100 cycles of data. • Model training and validation using dataset of 178 graphite/LiFePO 4 batteries. • A bilinear equation to describe capacity fade trend, fade rate, and knee point.

A convolutional neural network model for battery capacity fade curve prediction using early life data