Parameter estimation of lithium ion polymer battery mathematical model using genetic algorithm

The accurate prediction of the rechargeable battery lifetime is of paramount importance for mobile device use optimization. The parameter estimation of battery models utilizes experimental methods that are expensive, require high computational effort, and are time-consuming. This paper presents both the proposition of a methodology based on Genetic Algorithm (GA) for the parameter estimation and the mathematical modeling of Lithium Ion Polymer (LiPo) battery lifetime, model PL383562-2C, using the battery model. The proposed GA method is compared with other empirical methodology that is generally applied to this estimation problem. It stands that the GA employed to estimate these parameters turned the estimation into a more systematic and less subjective process. The model validation is performed based on the comparison between the lifetimes simulated by the battery model and the average experimental lifetimes obtained from a test platform. The results demonstrate both the effectiveness of the battery model to predict the LiPo battery lifetime and the efficiency of GA in its parameter estimation.

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