Lithium Battery Remaining Useful Life Prediction Based on Multi-Kernel Support Vector Regression With Hybrid Optimization Algorithm

Abstract

Abstract Accurately and reliably predicting the remaining useful life (RUL) of lithium battery is very important for the lithium battery health management system. However, most of the existing methods rely on complex multidimensional input features, which require a large number of sensors, increase the application cost and introduce redundant measurement errors. Therefore, this paper, only based on the battery capacity curve itself, proposes a method to construct a prediction model of support vector regression (SVR) by fusing multiple kernel functions. The linear equation coefficients of multiple kernel function combinations are optimized by the hybrid optimization algorithm. It is found that the hybrid kernel function can effectively overcome the problem that the single-kernel function is not capable of mapping the capacity fading trend of lithium battery. Hybrid optimization algorithm can avoid the problems of local optimization and global search ability deficiency. The proposed method is validated by experiments using the battery attenuation datasets from NASA, the University of Maryland, and a high-rate lithium battery in the laboratory stage. It can be seen from the experimental results that the prediction accuracy of this method is high. The mean prediction error, mean RMSE, and mean MAE are 2%, 0.0198, and 0.0157.