An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement

The prediction ability of all traditional machine learning models is limited to a few batteries. When the RUL of more batteries needs to be predicted, the prediction performance of traditional machine learning is not very good. For long-term battery capacity prediction, it is obtained in a recursive way. However, adding the predicted value to the input sequence will cause a large cumulative error after multiple recursive predictions. Based on the codec model, this paper introduces Gaussian process regression, which is used in multi-step prediction to improve the codec fusion method and the Savitzky-Golay method is utilized to smooth the training set. A new kernel function was designed to further improve the accuracy. The method of dynamic weights is adopted to minimize accumulated error. The experimental results show that the fusion prediction method can reduce the cumulative error of recursive prediction without losing the declining trend of the actual capacity of the battery, and can predict the RUL of zinc-ion batteries more precisely than other models.

Automated summary

The prediction ability of traditional machine learning models for battery life is limited. When predicting the remaining useful life (RUL) of multiple batteries, the performance of traditional machine learning is not satisfactory. This paper introduces Gaussian process regression and improves the codec fusion method for multi-step prediction. The Savitzky-Golay method is used to smooth the training set, and a new kernel function is designed to enhance accuracy. The method of dynamic weights is adopted to minimize accumulated error. Experimental results demonstrate that the fusion prediction method effectively reduces cumulative prediction error and accurately predicts RUL of zinc-ion batteries.