Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves

Abstract

The accurate estimation of the retained capacity in a lithium-ion battery is an essential requirement for the electric vehicles. The aging of the batteries depends on parameters and factors that are not easily monitored by the battery management system. This paper analyzes the ability of various machine learning algorithms to deal with the data generated by the battery management system during the partial charging/discharging process to instantly diagnose and estimate the retained capacity of the battery. Experimental data from an online dataset containing thousands of battery cycles are used for training and validation of the different models. Results demonstrate that the developed convolutional neural network outperforms the rest of the machine learning algorithms implemented, regardless of the portion of the cycle registered by the battery management system. The estimates obtained outperform most previous references. However, the estimation error values registered when analyzing partial cycles with depths lower than 50 % (above 1.5 %) remain too high to validate any of the analyzed algorithms as a solution for commercial systems.