Lithium battery SOC estimation method based on competitive generative adversarial neural network
A neural network and lithium battery technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve the problems of large amount of calculation in the modeling process, low estimation accuracy, unstable estimation results, etc. Low accuracy, overcoming the effects of low robustness
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[0081] like figure 1 As shown, in the embodiment of the present invention, a method for estimating lithium battery SOC based on a competitive generative adversarial neural network is proposed, including the following steps:
[0082] S1: Select three physical characteristics that are closely related to the SOC value of the lithium battery and can be directly measured, and collect the above characteristic data during the charging and discharging process of the lithium battery;
[0083] S2: Normalize the lithium battery SOC data set, and then randomly divide the processed data set into training set and test set according to the ratio of 60% and 40%;
[0084] S3: Use the support vector regression model to build 7 generators in the competitive generative adversarial neural network, denoted as generator 1, generator 2, ..., generator 7, and initialize the internal parameters of each generator; Layer perceptron neural network, build a discriminator in a competitive generative advers...
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