Battery SOH enhanced solution: Voltage reconstruction and image recognition response to loss of data scenarios

Abstract

Accurate estimation of battery health status plays a crucial role in battery management systems. However, the lack of operational data still affects the accuracy of battery state of health (SOH) estimation. For this reason, a SOH estimation method is proposed based on charging data reconstruction combined with image processing. The charging voltage data is used to train the least squares generative adversarial network (LSGAN), which is validated under different levels of missing data. From a visual perspective, the Gram angle field method is applied to convert one-dimensional time series data into image data. This method fully preserves the time series characteristics and nonlinear evolution patterns, which avoids the difficulties and limited expressive power associated with manual feature extraction. At the same time, the Swin Transformer model is introduced to extract global structures and local details from images, enabling better capture of sequence change trends. Combined with the long short-term memory network (LSTM), this enables accurate estimation of battery SOH. Two different types of batteries are used to validate the test. The experimental results show that the proposed method has good estimation accuracy under different training proportions.