An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement. (March 2023) - Dang, Wei; Liao, Shengjun; Yang, Bo; Yin, Zhengtong; Liu, Mingzhe; Yin, Lirong; Zheng, Wenfeng
Abstract: 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. Highlights: Introduces Gaussian process regression to improve the codec fusion method. Adopts the method of dynamic weights is adopted to minimize accumulated error. This method improves the robustness to the interference of specular reflection. This method can reduce the cumulative error without losing the declining trend of the actual battery capacity. This method can predict the RUL of zinc-ion batteries more precisely