Parameter estimation of lithium ion polymer battery mathematical model using genetic algorithm
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Parameter estimation of lithium ion polymer battery mathematical model using genetic algorithm Marcia de Fatima Brondani1 · Airam Teresa Zago Romcy Sausen1 · Paulo Sérgio Sausen1 · Manuel Osório Binelo1
Received: 29 March 2017 / Revised: 21 October 2017 / Accepted: 11 November 2017 © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2017
Abstract 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 timeconsuming. 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. Keywords Parameter estimation · Genetic algorithms · Mathematical modeling · Battery lifetime Mathematics Subject Classification 93A30
1 Introduction Rechargeable batteries have been widely used in applications such as mobile devices, electric vehicles, and hybrid vehicles (Malik et al. 2014). These applications drive the necessity of
Communicated by Jose Roberto Castilho Piqueira, Elbert E N Macau, Luiz de Siqueira Martins Filho.
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Marcia de Fatima Brondani [email protected] Department of Exact Sciences and Engineering, Regional University of Northwestern Rio Grande do Sul State, Ijuí, RS, Brazil
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more efficient battery technologies, with more energetic density, resulting in better charge capacity. Considering these challenges, Lithium Ion Polymer (LiPo) batteries standout for having important characteristics such as high energy density, allowing thin battery designs, being lighter and more compact than most battery types (Meyer 1998; Hammond and Hazeldine 2015), having a relatively low content of metals harmful to the environment, and the absence of metals with a high CO2 emission during the manufacturing process such as nickel (Hammond and Hazeldine 2015). In contrast, the useful lifetime cycles of these batteries are relatively short, with approximately 400 charge/discharge cycles at a temperature range of 20–45 ◦ C (Hammond and Hazeldine 2015), causing a more premature substitution of the batteries and the increase in the energy expended in the manufacturing and transportation of new batteries. In addition, t
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