Faults in aluminum electrolytic capacitors (AECs) are classified as the major cause for power electronics equipment breakdown, mainly due to AECs wear out through the vaporization of the electrolyte, as a result of both aging and temperature effects. Therefore, the online estimation of these two parameters can provide valuable and timely information that allows detecting any potential capacitor failure. Changes in C and ESR parameters influence the relationship between the capacitor voltage and current ripples. This ratio is dominated by C at low frequencies, and by ESR in the high-frequency range. The aim of this paper is to propose an algorithm allowing a continue estimation and tracking of both parameters (ESR and C). This algorithm should be simple, fast, and suitable for online implementation. Therefore, this paper introduces the use of the Short-Time Fourier Transform (STFT) technique to determine and track C and ESR values, starting from some always-existing harmonics at low and high frequencies. The STFT is based on applying the Discrete Fourier Transform (DFT) algorithm on a sliding window, which makes it more appropriate for online implementation. The effectiveness of the proposed method is proved by simulation and laboratory experiments, using a boost DC-DC converter

Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries’ state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries in electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries’ parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.

It is well known that the most common reason for electrical machines breakdown is the stator windings’ fault occurrence. Indeed, this type of fault represents almost 40% of faults occurring in induction machines. One of the major causes of stator defects is the occurrence of interturn short-circuit (ITSC) faults that have critical and dangerous effects on the motor itself, as well as on the related electrical equipment. Therefore, early detection and a precise severity estimation of the occurrence of ITSC faults for all working conditions can prevent failure breakdowns and increase reliability and safety of industrial facilities. In this context, this article proposes an efficient online diagnostics method based on calculating and monitoring a pertinent severity factor defined as the ratio between the zero and positive voltage symmetrical components. The online implementation of this method is performed on a LabVIEW environment, using the short-time least square Prony's (STLSP) method. It does not need any estimation of motor parameters and it requires only voltage sensors. Several tests under healthy and faulty conditions are carried out on a three-phase 3-kW induction motor. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults with high reliability, fastness, and high precision, even under load variations or unbalanced supply voltage.

Along with the growing of population and social and technological improvements, the use of energy and natural resources has risen over the past few decades. The sustainability of using coal, oil, and natural gas as the main energy sources faces, however, substantial obstacles. Fuel cells, batteries, and super-capacitors have the highest energy densities, but due to their high-power density and rapid charge-discharge speed, regular dielectric capacitors are becoming more popular for pulsed power applications. High electric breakdown strength and high maximum but low-remnant (zero in the case of linear dielectrics) polarization are necessary for high energy density in dielectric capacitors. The high performance, multi-functionality, and high integration of electronic devices are made possible in large part by the multilayer ceramic capacitors (MLCCs). Due to their low cost, compact size, wide capacitance range, low ESL and ESR, and excellent frequency response, MLCCs play a significant role in contemporary electronic devices. From the standpoint of the underlying theories of energy storage in dielectrics, this paper emphasizes the significant problems and recent advancements in building extremely volumetric-efficient MLCCs. Following a thorough examination of the state-of-the-art, important parameters that may be used to improve energy-storage qualities are highlighted, such as controlling local structure, phase assembly, dielectric layer thickness, microstructure, conductivity, different failure modes, and the specific performance during the failure mechanism. The summary of some conclusions on the impending need for innovative materials and diagnostic methods in high-power/energy density capacitor applications appears at the end of the paper.

Aluminum electrolytic capacitors (AECs) are the most common capacitors used in power electronics. They are frequently used for filtering and storage functions. However, AECs also represent the major cause for power electronics equipment breakdown, mainly due to their wear out through the vaporization of the electrolyte, due to both aging and temperature effects. The aging of AECs is expressed by the increase of their equivalent series resistance (ESR) and the decrease of their capacitance (C). Therefore, estimating these two parameters can provide a good indicator for a potential capacitor failure. Additionally, these changes in C and ESR parameters strongly influence the ratio between the capacitors voltage ripple and their current ripple. This ratio is dominated by C at low frequencies and by the ESR in the high frequency range. Hence, this paper suggests a method for on-line estimating and tracking of both C and ESR using that ratio, which is computed starting from some always existing harmonics at low and high frequencies. The proposed method uses the Short Time Least Square Prony's (STLSP) technique to determine and track the amplitudes of the target harmonics. It is shown that compared with the Discrete Fourier Transform (DFT) method, the STLSP technique is well suitable for such application, since it has the ability to determine and track the amplitude and frequency of any spectral component, even for noisy and non-stationary signals, using a small number of data samples. The method's effectiveness is proved by simulation and experimental tests using a boost DC-DC converter.