Search

Battery degradation, caused by multiple coupled degradation mechanisms, severely affects the safety and sustainability of a battery management system (BMS). The battery state of health (SOH) is a commonly-adopted metric to evaluate a battery's degradation condition, which should be carefully modeled to facilitate the safety and reliability of a BMS. Recently, owing to the rapid progress of data science-related techniques, data-driven models for battery SOH estimation have attracted great attentions from both academia and industry communities. This paper aims to provide the scientists and engineers with a general overview of data-driven battery SOH estimation technology for BMSs. State-of-the-art models published during 2018–2022 are reviewed with care, including a) feature extraction and selection methods; b) benchmarks, variants and extensions of data-driven SOH estimation models; and c) publicly-available battery SOH datasets. Afterwards, experiments are conducted and analyzed on the Toyota & Stanford-MIT battery SOH datasets for benchmark study. Finally, existing challenges and feature trends are summarized. [ABSTRACT FROM AUTHOR]

Recently, deep learning techniques have been widely applied to fault diagnosis due to their outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis methods is highly dependent on the quantity and quality of the training data. In practical scenarios, it is challenging to obtain sufficient high-quality training data for fault diagnosis tasks due to complex environments, which would affect the effectiveness of the deep learning methods. In this paper, a new fault diagnosis method is proposed for motor bearing fault diagnosis under small samples. The Siamese neural networks (SNNs) are employed to extract the fault features. A multi-stage training strategy is proposed to train the SNNs with the aim to prevent the training stagnation problem and handle the small sample problem. A multi-source feature fusion network is developed to make full use of the multi-source sensor data by fusing the extracted fault features for further fault diagnosis. The proposed method is applied to motor bearing fault diagnosis on two real-world datasets. Experimental results demonstrate the effectiveness and feasibility of the introduced method for motor bearing fault diagnosis under small samples. [ABSTRACT FROM AUTHOR]

• Feature extraction : Aiming at the problems existing in the fault feature extraction methods, it decomposed the fault signal into several IMF components using the VMD algorithm with adaptive characteristics. Then the IMF components with transient fault characteristics are accurately selected to achieve fault feature extraction. • Wave-head calibration: The discrete kurtosis value is used to detect the arrival time of the traveling wave head. The discrete kurtosis value is calculated for the IMF component with the highest frequency. The time corresponding to the maximum kurtosis value is the traveling wave head moment, and the wave-head calibration is realized. • Wave velocity selection : Given the problem of low fault location accuracy due to different wave speeds of mixed lines, it is proposed to convert the length of cable lines to overhead lines and used unified wave velocity to reduce location errors. There are many affected factors for the traditional traveling wave detection method applied to distribution networks, and the wave head calibration needs to be more accurate. This paper proposes a new method for fault location in resonant grounding systems based on variable modal decomposition (VMD) and kurtosis calibration. Firstly, the feeder currents are collected for phase mode transformation to obtain the aerial mode components. Secondly, the VMD algorithm decomposes the aerial mode components into several intrinsic mode functions (IMFs). Then, the discrete kurtosis is calculated for the high-frequency IMF, and the moment of maximum kurtosis value is determined as the first arrival moment of the faulty wave head. Finally, the faulty section is preliminarily determined using the calibrated moments of the head and end devices of the main line. If the fault is located on the main line, the fault location is determined directly. If it is located on a branch line, the distance of the fault is calculated using the wave head between the head and branch line ends to achieve a precise location. The PSCAD simulation results show that the proposed method can accurately calibrate the arrival moment of the fault wave head and achieve fault location. It is not affected by the fault location and the initial angle of the fault. [ABSTRACT FROM AUTHOR]

• It proposed a novel noninvasive location method based on MF. It can collect line information in a low-cost, non-contact manner, and is not affected by the complex topology of the distribution network. No power cut is required for installation and maintenance. At the same time, the MF is directly used to reflect the physical characteristics of the fault, and the analysis process is simple, reducing the introduction of complex algorithms for feature extraction and other steps, which has greater advantages in practical applications. • It proposed a multi criteria fusion location method based on dual-axis MF component. We deeply studied the application of MF in fault location, and analyzes the MF waveform from two dimensions of x -axis and y -axis. We measured the fault information from the perspective of five criteria, which is more comprehensive and suitable for all kinds of operation conditions. D-S evidence theory is introduced to improve the fault location reliability through multi criteria fusion. • The variation of MF in each axis and its influence on the method are further considered when the sag, galloping and sampling data window are different. The distribution network has many branches and complex structure, and the existing protection is difficult to achieve accurate fault section location. Moreover, the monitoring device based on electrical quantity has some problems, such as high cost, power failure in installation and maintenance, and the information collected can not meet the requirements of distribution network for operation monitoring. Therefore, fault location is still a difficult problem in the existing distribution network. The magnetic field (MF) under the overhead line can reflect the change of line current, locate the fault position between two pole towers, and the magnetoresistive sensor has the advantages of low cost and uninterrupted installation and maintenance. Therefore, in recent years, the non-contact fault feeder detection and location method based on MF has begun to develop rapidly. Based on the similarity analysis of MF waveform under overhead line from different aspects by using five criteria: relative entropy, Frobenius norm, maximum mean discrepancy, cosine similarity and correlation coefficient, it proposed a multi criteria fusion fault location method based on D-S evidence theory, which makes full use of the advantages of each criterion and overcomes the limitations of single criterion. Simulation results show that the method is not affected by conductor sag, galloping and data window length, and it fit for the noise interference and high impedance fault (HIF). Compared with the other MF method, the method has stronger adaptability. [ABSTRACT FROM AUTHOR]