Lithium battery SOC prediction method of bayes regularization LM-BP neural network

Abstract

The invention discloses a lithium battery SOC prediction method of a bayes regularization LM-BP neural network. The lithium battery SOC prediction method comprises the following steps: a, establishinga BP neural network model; b, establishing a bayes regularization LM-BP neural network algorithm; c, acquiring sample data and calculating sample SOC; and d, performing the normalization processing of data. The neural network has good nonlinear fitting capacity and does not need to consider a complicated chemical structure inside the battery, dynamic characteristics of the lithium battery can bewell fit, by combining the bayes regularization algorithm, the generalization capacity of the network can be improved, by combining the LM algorithm, the convergence rate of the network can be increased, and the approximation accuracy can be improved; and therefore, the lithium battery SOC prediction method of the bayes regularization LM-BP neural network has the characteristics of high predictionprecision, high convergence speed, and high generalization capacity and is suitable for various power batteries.

Description

technical field [0001] The invention relates to the testing field of power batteries, in particular to a lithium battery SOC prediction method of a Bayesian regularized LM-BP neural network. Background technique [0002] Due to the rapid development of human society, environmental and energy issues have become increasingly prominent, and green energy is gradually entering human production and life. Among them, electric vehicles powered by new energy power generation have been vigorously developed due to their advantages of no pollution and low noise. However, due to the imperfect battery management system, the battery will be overcharged or overdischarged, which will affect the use of electric vehicles. safety. As an important parameter of the battery, SOC is as important as the heart of the human body, and because the battery is an extremely complex system, the relationship between the battery SOC and other parameters of the battery is highly nonlinear, so it is difficult ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies in the existing methods, to provide a lithium battery SOC prediction method of a Bayesian regularized LM-BP neural network, by combining the Bayesian regularized LM algorithm with the BP neural network algorithm Solve the problems of low accuracy and poor generalization ability of lithium-ion power battery SOC estimation

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