Application of Improved VAE-GAN Model in Battery EIS Data Enhanced Applications
Electrochemical Impedance Spectroscopy(EIS)is a testing method used to characterize the internal electrochemical processes of batteries.Electrochemical impedance spectroscopy data can be used to analyze,evaluate,and optimize battery performance.Testing EIS data requires the use of professional instruments and equipment,which is costly.The amount of test data is often limited,and data augmentation methods can be used to increase the amount of EIS data.Variational Autoencoder(VAE)is a generative model that can generate new samples by sampling from potential distributions.Generative Adversarial Networks(GANs)are also a type of generative model,whose principle is to achieve the task of generating and discriminating data through two opposing network models.Both VAE and GAN models can be used separately for data augmentation,but both have some drawbacks.By combining VAE and GAN methods to construct VAE-GAN models,some shortcomings can be compensated and achieve better generation results and performance.In traditional VAE-GAN models,the encoder and decoder in the VAE model,as well as the discriminator in the GAN model,generally use fully connected neural networks or convolutional neural network models.The network structure of the VAE-GAN model was optimized by using the Transformer model in the encoder and decoder of the VAE model,as well as in the discriminator of the GAN model,which improved the model performance.Improved VAE-GAN model was used to construct a prediction model for EIS using EIS data as input.The generator generates EIS enhancement data,and the discriminator determines whether the newly generated EIS data is effective enhancement data.Experiments have shown that the method proposed in this article can generate high-quality EIS data.
万方数据
维普
