Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation

Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation

Thiago H. R. Donato and Marcos G. Quiles National Space Research Institute,Sao Jose dos Campos, Brazil

ABSTRACT

In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and MultiLayer Perceptron neural network. Finally, with the application of statistical validation techniques,the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems.

KEYWORDS Lithium-ion battery, Internal impedance, State of charge, Multi Layer Perceptron,Gradient Tree Boosting, xgBoost Original Source URL: http://aircconline.com/acii/V5N1/5118acii01.pdf http://airccse.org/journal/acii/current.html