Abstract
Estimating the state of charge of batteries is a critical task for every battery-powered device. In this work, we propose a machine learning approach based on electrochemical impedance spectroscopy and 2D convolutional neural networks. A case study based on Samsung ICR18650-26J lithium ion batteries is also presented and discussed in detail. An application-specific data augmentation technique is developed and applied. The proposed system achieves a classification accuracy of 93% on a test dataset of new measurements from the same battery and 88% accuracy on a different battery without prior calibration. The uncertainty of the state of charge classification provided by the proposed method is evaluated using Monte Carlo Simulations and Monte Carlo dropout methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have