Remaining Useful Life Prediction of Lithium-ion Batteries Based on Singular Value Decomposition and Gaussian Process Regression

An accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great practical significance. In this work, a RUL prediction method for lithium-ion batteries is proposed. Firstly, the battery data are considered as matrices and the health indicators (HIs) are extracted using SVD that does not require parameter settings and is adaptable for different lithium-ion battery datasets. Meanwhile, considering that the degradation information contained in the matrices has an important impact on the ability of extracted HIs to quantify the degradation status of battery, the original measurement data is reanalyzed and the feature extraction object containing more degradation information are obtained. Then, the strongly correlated HIs are selected by spearman correlation analysis and degradation trend analysis. After that, the GPR algorithm is used and the final prediction results with uncertainty expression are obtained. Finally, the feasibility and validity of the proposed prediction model are verified by four batteries provided by NASA. And an additional battery dataset from MIT is selected to verify the adaptability of feature extraction method. From the experimental results, it can be seen that the proposed RUL prediction framework has satisfactory prediction performance and the SVD feature extraction method has good adaptivity. The HIs extracted in this paper show a significant improvement in prediction accuracy compared with those extracted from measurement data.