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Prediction of Secondary Battery Output at Cold Cranking Behavior for Automobile Application

E.Jensi Miria, S.Ambalavanan

A wide range of research works on secondary battery models are carried out with varying degrees of complexity. They capture battery behavior for specific purposes using battery design, battery available parameters ranging from performance estimation to circuit simulation. This work explains the computation model of a secondary battery based on the artificial neural network (ANN), which focuses on the prediction of discharge behavior at high rates and cold cranking behavior at low temperature for automobile application. The novelty in this communication is that we have predicted with the function of less discharge time, less working hour and saved energy. The accuracy of this method has been verified by using experimentally measured data. The computation values are in good agreement with experimental data and the related error has been considered acceptable. The final conclusion of this work will demonstrate how battery operators can have better control over their quality and the performance of the batteries of different applications. This method can be utilized as a quality assurance tool in automotive industry.