Online State-of-Health Estimation Method for Lithium-Ion Battery Based on CEEMDAN for Feature Analysis and RBF Neural Network

Abstract

The state-of-health (SOH) estimation is of great importance in the battery management system. However, the accurate real-time SOH estimation is still a challenge because the capacity of the battery cannot be precisely monitored and measured in real-time. In response to this problem, this article proposes an online SOH monitoring method for lithium-ion battery (LIB) based on the charge–discharge feature of battery in real-time. First, the relationship between the charge–discharge feature and SOH through principal component analysis (PCA) is analyzed, and the appropriate feature which can precisely describe the battery aging process is then selected. Second, as to decompose and denoise the collected charge–discharge feature data, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied. The major trend obtained is predicted by logistic regression based on the sliding time window (LR-STW), and the minor fluctuation is predicted by Kalman filter (KF). Finally, an online SOH estimation method based on RBF neural network is proposed to establish the mapping relationship between feature and SOH. The proposed method was verified by the NASA experimental data. The experimental results show that the proposed method can accurately predict the LIB SOH in real-time, and the robustness of the proposed method is strengthened and promised.