To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

The state of health (SOH) estimation is of great importance in the battery management system. However, the accurate real time state of health 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 paper proposes an online SOH monitoring method for lithium-ion battery (LIB) based on the charge-discharge feature of battery in real time. Firstly, 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. Secondly, 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 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.

Data-driven methods for estimating the state of health (SOH) of lithium-ion batteries (LIBs) are widely used by extracting health indicator (HI) from charge-discharge measurements. However, many existing HIs have shortcoming of heavy computing burden, which causes the difficulty on online implementation. Therefore, this paper proposes a novel HI called Equal Voltage Range Sampling Count Number (EVRSCN), which is used to estimate SOH of LIBs. The proposed HI is extracted from the charging process. The EVRSCN HI can be extracted online with reduced calculation burden of BMS. Gaussian Process Regression (GPR) is used to quickly achieve accurate SOH estimation based on EVRSCN. The SOH estimation which applies three typical and widely-used datasets of Oxford, SNL and CALCE shows that the proposed method can achieve a promised accuracy, and the root-mean-square error (RMSE) could lower than 0.5% in some typical cases. In addition, the noise immunity of EVRSCN has been evaluated and compared with existing HIs. The results show that the proposed EVRSCN has stable and promised SOH estimation accuracy, as well as good noise immunity.

Recent years have witnessed emerging oscillation stability issues in DC microgrid via power electronic converters. Considering the confidentiality of the parameters and structure of power electronic devices, the impedance method is widely used to study the small signal stability. However, the stability assessment based on traditional impedance model and Gershgorin Theorem have some shortcomings for multiple converters DC microgrid. This may lead to the problem that the stability analysis results are not accurate enough or the criteria cannot be applied. In this article, the system is modeled as a medium and high dimensional negative feedback system. Further, a sufficient stability criterion based on extended Gershgorin Theorem is developed through reshaping the range of eigenvalues estimation for the non-diagonal advantage system. Finally, the validity of the criterion is verified by a 6 converters system. Test results indicate the proposed method has the potential to assess the high-order system stability with low computational complexity. Moreover, its low conservatism as a sufficient criterion is helpful to guide the system design.