Lithium-Ion Batteries state of charge estimation based on electrochemical impedance spectroscopy and convolutional neural network

Emanuele Buchicchio, Alessio De Angelis, Francesco Santoni, Paolo Carbone

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 convolutional neural networks. A case study based on Samsung ICR18650-26J lithium-Ion batteries is also presented and discussed in detail. A classification accuracy of 80% and top-2 classification accuracy of 95% were achieved on a test battery not used for model training.

Download

IMEKO-TC4-2022-17.pdf

DOI

10.21014/tc4-2022.17

IMEKO TC

TC4 - Measurement of Electrical Quantities

Back to the proceedings