Comparison of different approaches for lead acid battery state of health estimation based on artificial neural networks algorithms

Predicting the State of Health (SoH) of lead acid batteries in renewable energy field application is well known as a hard feasible task, due the fact that the operation condition is very spotty. This paper presents a SoH prediction based on artificial neural network model that can estimate the impac...

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Bibliographic Details
Published in2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) pp. 79 - 84
Main Authors Tossoli de Sousa, Thais, Torquato Arioli, Vitor, Sobral Vieira, Cesar, Rocha dos Santos, Sender, Pereira Franca, Alex
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
5G mobile communication
Adaptive systems
Artificial Neural Network
Conferences
FAA
Intelligent systems
Iron
Lead Acid Battery
State of Health
Three-dimensional displays
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Summary:Predicting the State of Health (SoH) of lead acid batteries in renewable energy field application is well known as a hard feasible task, due the fact that the operation condition is very spotty. This paper presents a SoH prediction based on artificial neural network model that can estimate the impact of different field operating conditions. In the presented study, the experimental data based on different cycling regime test of lead acid batteries is used to validate the proposed approach for lead acid battery.
DOI:10.1109/EAIS.2016.7502495