%0 Journal Article %1 donatomachine %A Donato, Thiago H. R. %D 2018 %J Advanced Computational Intelligence: An International Journal %K Boosting Gradient Internal Layer Lithium-ion Multi Perceptron State Tree battery charge impedance of xgBoost %N 1 %P 01 - 20 %R 10.5121/acii.2018.5101 %T Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in Satellite Lithium-ion Battery Sets Impedance Estimation %U http://airccse.org/journal/acii/current.html %V 5 %X 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 Multi Layer 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.

@article{donatomachine, 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 Multi Layer 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.}, added-at = {2020-06-24T10:54:15.000+0200}, author = {Donato, Thiago H. R.}, biburl = {https://www.bibsonomy.org/bibtex/22bd90f83b46c39f152fa22269a6aace1/janakirob}, doi = {10.5121/acii.2018.5101}, interhash = {79bc18c30c76e22cb9bab71a883a8d8b}, intrahash = {2bd90f83b46c39f152fa22269a6aace1}, journal = {Advanced Computational Intelligence: An International Journal}, keywords = {Boosting Gradient Internal Layer Lithium-ion Multi Perceptron State Tree battery charge impedance of xgBoost}, month = {January}, number = 1, pages = {01 - 20}, timestamp = {2020-06-24T10:54:15.000+0200}, title = {Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in Satellite Lithium-ion Battery Sets Impedance Estimation}, url = {http://airccse.org/journal/acii/current.html}, volume = 5, year = 2018 }