A solution framework for the experimental data shortage problem of lithium-ion batteries: Generative adversarial network-based data augmentation for battery state estimation

This work proposes a framework for generating experimental data based on generative adversarial networks. While applying transfer learning to lower the threshold of use, this framework can significantly improve the efficiency of battery state estimators. In order to address the widespread data shortage problem in battery research, this paper proposes a generative adversarial network model that combines it with deep convolutional networks, the Wasserstein distance, and the gradient penalty to achieve data augmentation. To lower the threshold for implementing the proposed method, transfer learning is further introduced. The W-DC-GAN-GP-TL framework is thereby formed. This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data. Through visual comparisons and the examination of two visualization methods (probability density function (PDF) and principal component analysis (PCA)), it is demonstrated that the generated data is hard to distinguish from the real data. The application of generated data for training a battery state model using transfer learning is further evaluated. Specifically, Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health (SOH) and state of charge (SOC), respectively. The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios: for the data replacement scenario, where real data are removed and replaced with generated data, the state estimator accuracy decreases only slightly; for the data enhancement scenario, the estimator accuracy is further improved. The estimation accuracy of SOH and SOC is as low as 0.69% and 0.58% root mean square error (RMSE) after applying the proposed framework. This framework provides a reliable method for enriching battery measurement data. It is a generalized framework capable of generating a variety of time series data.