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

Pei Wang,Xue Dan,Yong Yang

doi:10.1177/1550147719839637

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.

Highlights

Lithium-ion batteries, as an energy source for many complex electronic systems, play an important role in the functional capabilities of electronic systems
In this article, inspired by fusion approach,[29] we propose a multi-scale fusion prediction method based on ensemble empirical mode decomposition (EEMD) and nonlinear autoregressive (NAR) neural networks
The steps of multi-scale fusion prediction method based on EEMD and NAR neural networks are as follows: 1. absolute error (AE)

Summary

Introduction

Lithium-ion batteries, as an energy source for many complex electronic systems, play an important role in the functional capabilities of electronic systems. Many investigations show that power system degradation is the main cause of system fault or failure.[1] the life health status of power source directly affects the safety and reliability of a hybrid power system.[2] In this case, an effective technique to monitor battery status can greatly improve power system reliability. For lithium-ion battery, remaining useful life (RUL) prediction has become a hot research subject.[3] Several research works of RUL prediction can be founded, such as the world’s leading University of Maryland Advanced Life Week Engineering Center[4,5] and NASA’s Prognostics Center of Excellence (PCoE)

Methods

Results

Conclusion