This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with DBSCAN (density-based spatial clustering of applications with noise) clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The DBSCAN clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency.

To ensure a safe and reliable operation, accurate estimation of the state of health (SOH) of lithium-ion batteries is necessary. In order to improve the accuracy and practicability of the SOH estimation, this paper proposes a SOH estimation method based on differential temperature-incremental capacity-voltage (DT-IC-V) health features (HFs). A new DT-related health feature extraction method is proposed by analyzing the potential relationship between the temperature difference profile and SOH. A set of DT-IC-V HFs are designed in a relatively small charging segment to reduce the difficulty of obtaining data in practice. And a battery SOH estimation model based on deep belief network (DBN) and extreme learning machine (ELM) is designed. The number of nodes in each hidden layer of the DBN-ELM model is determined by the Sparrow Search Algorithm (SSA). The proposed method is validated on different types of batteries. The results show that the method can accurately estimate the SOH, with the mean absolute percent error remaining within 0.43% and 1.35% in the Oxford and NASA datasets, respectively.

With the development of electric vehicles, the demand for lithium-ion batteries has been increasing annually. Accurately estimating the State of Health (SOH) of lithium-ion batteries is crucial for their efficient and reliable use. Most of the existing research on SOH estimation is based on parameters such as current, voltage, and temperature, which are prone to fluctuations. Estimating the SOH of lithium-ion batteries based on Electrochemical Impedance Spectroscopy (EIS) and data-driven approaches has been proven effective. In this paper, we explore a novel SOH estimation model for lithium batteries based on EIS and Convolutional Neural Network (CNN)-Vision Transformer (VIT). The EIS data is treated as a grayscale image, eliminating the need for manual feature extraction and simultaneously capturing both local and global features in the data. To validate the effectiveness of the proposed model, a series of simulation experiments are conducted, comparing it with various traditional machine learning models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). The simulation results demonstrate that the proposed model performs best overall in the testing dataset at three different temperatures. This confirms that the model can accurately and stably estimate the SOH of lithium-ion batteries without requiring manual feature extraction and knowledge of battery aging temperature.

This paper proposes a network analysis-based distributed load shedding strategy for voltage collapse prevention. The two-level framework of the distributed load shedding strategy is proposed. The lower one is consisted of local load shedding controllers, and the upper one is designed to coordinate the local controllers to reduce the total load shedding amount. Through network analysis, two sensitivity indices are derived to form local zones, and determine the load shedding locations and amounts. The voltage control sensitivity is proposed for network partitioning. The local load shedding controller is solved by load shedding sensitivity based piecewise linearization. A load shedding sensitivity based linear programming problem is formulated for the center load shedding controller to coordinate the load shedding amounts of local controllers. Case studies have been conducted to verify the effectiveness of the proposed methods.

The inconsistency of cells in the battery pack is one of the main causes of battery failure. In practical applications, the terminal voltage is an important parameter that is easy to obtain and can characterize the inconsistency of cells. In this paper, a fault diagnosis method based on piecewise dimensionality reduction and outlier identification is proposed according to the voltage inconsistency of cells in the battery pack. This method uses a piecewise aggregate approximation (PAA) algorithm with a shift factor to reduce the dimension of the cell voltage time series, after which a deletion mechanism is designed based on the clustering algorithm and outlier identification to calculate the clustering quality after deleting each cell, reflecting the deviate degree of each cell. In addition, a safety management strategy is designed based on the Z-score method, and an abnormality coefficient is set to evaluate the inconsistency of cells. The effectiveness of the proposed diagnosis method is verified by monitoring the voltage data of two real-world electric vehicles. The verification results show that the method can not only detect the inconsistency before the failure of the faulty cell in the battery pack in advance, but also reduce the risk of computational explosion caused by the voltage time series and accurately locate the faulty cell.