Lithium battery state-of-charge prediction method based on improved generative adversarial network

Abstract

The invention provides a lithium battery state-of-charge prediction method based on an improved generative adversarial network. The lithium battery state-of-charge prediction method comprises the following steps: collecting modal parameters of a lithium battery and a real state-of-charge SOC in a lithium battery sample; estimating a lower bound value of mutual information between the generation model G output G (z, c) and the condition variable c by using a regression model R; enabling the generation model G and a discrimination model D to confront each other to achieve Nash equilibrium; generating a sample by utilizing the generation model G, and adding the sample into a training set used by the regression model R for training; and alternately training the generation model G, the discrimination model D and the regression model R to enable each model to tend to converge. According to the invention, the training set conforming to original distribution is expanded by using the generationmodel; at the same time, in the improved generative adversarial network, two activation functions of a random correction linear unit RReLU and an exponential linear unit Exponential Units (ELU) are used to obtain stronger model expressive force, and the nonlinear characteristics of the lithium battery are better learned.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a method for predicting the state of charge of a lithium battery. Background technique [0002] As an important part of electric vehicles, lithium batteries play a key role in the acceleration, climbing and battery life of the entire vehicle. The state of charge (SOC) of the battery is an important parameter to reflect the energy of the lithium battery. Accurately estimating the SOC can prolong the service life of the battery, avoid overcharging / discharging of the battery, and ensure the safe driving of electric vehicles. premise. SOC is an internal characteristic parameter of the battery that cannot be directly measured, and there is a strong nonlinear relationship between SOC and battery voltage, current, temperature and other parameters. Therefore, how to improve the precision and accuracy of SOC estimation is an urgent problem to be solved in the field of electric...

AI Technical Summary

Problems solved by technology

The bp neural network method has a good self-learning ability, but there are few training samples and the depth of the model is not enough, the nonlinear expressiveness is not enough, and the error of estimating the battery SOC is large

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