Open AccessMathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm
Author(s) -
Marcia de Fatima Brondani Binelo,
Airam Teresa Zago Romcy Sausen,
Paulo Sérgio Sausen,
Manuel Osório Binelo
Publication year - 2019
Publication title -
tema
Language(s) - English
Resource type - Journals
eISSN - 2179-8451
pISSN - 1677-1966
DOI - 10.5540/tema.2019.020.01.149
Subject(s) - parametrization (atmospheric modeling) , genetic algorithm , battery (electricity) , algorithm , computer science , heuristic , process (computing) , estimation theory , mathematical model , mathematics , power (physics) , statistics , artificial intelligence , machine learning , physics , quantum mechanics , radiative transfer , operating system
In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is used for the mathematical modeling of the Lithium-ion Polymer batteries lifetime. The model is also parametrized using the conventional procedures, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and also is a more agile process than the conventional one, been less subject to subjective aspects.