A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks

Abstract

Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks.