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Elman neural network based prediction of the degree of deterioration lithium

Author: ShiChunYuan
Tutor: WangHaiYing
School: Harbin University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Lithium Battery The degree of deterioration Parameter Identification Elman neural network
CLC: TM912
Type: Master's thesis
Year: 2011
Downloads: 159
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Abstract


A lithium battery is ideal for today's internationally recognized chemical energy, which as the newest secondary battery , because of its superior performance and has become widely accepted . With the constantly improving performance of lithium batteries , battery testing techniques have become more sophisticated , the life of lithium battery test system designs become critical issues . In order to keep abreast of the battery life and health, people use the degree of deterioration of the battery parameters most measurable indicators . Therefore , this paper establishes the degree of deterioration of lithium batteries prediction model for real-time monitoring of the health status of the battery to provide an effective way . This stage, the degree of deterioration of the battery is a battery for the general evaluation of the external characteristics , various parameters external to predict the degree of deterioration of the battery , the actual prediction general. Therefore , this article uses resistance parameter identification method , the internal characteristics of the battery and battery deterioration degree of correlation analysis , in order to predict the degree of deterioration of the battery propose a new method . Through internal resistance of the battery parameters such as the degree of deterioration prediction , accuracy is greatly improved , so as to solve the deficiencies of traditional forecasting methods . In this paper, lithium iron phosphate battery for the study, by analyzing the internal resistance of the battery ohmic resistance and polarization characteristics , the establishment of a simplified equivalent model of the battery , combined with recursive least squares method for the establishment of model parameter identification . Identification of the model is applied to the internal resistance of the battery charge and discharge cycle life test , in order to analyze the internal resistance of the battery and the degree of deterioration of the relationship between the battery . In this paper, Elman neural network to establish the degree of deterioration of the lithium prediction model, the internal resistance of the input parameters as the model to predict the results achieved the expected results, and the use of genetic algorithms for building predictive models for the weight optimization through simulation comparison so predictable effect is more ideal . Studies show that the use of lithium batteries as a predicted degree of deterioration of the internal resistance of the parameters, the method is feasible , the method will predict the degree of deterioration of the battery provides a new direction .

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CLC: > Industrial Technology > Electrotechnical > Independent power supply technology (direct power) > Battery
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