This paper presents a complete design methodology of a Class-E inverter with a loosely-coupled transformer under zero-voltage switching conditions. At a selected maximum coupling coefficient, the inverter satisfies both zero-voltage switching (ZVS) and zero-derivative switching (ZDS) conditions to achieve a high efficiency. As the coupling coefficient is decreased from its maximum value to zero, the inverter satisfies the ZVS condition over a wide range of load resistance. The proposed method absorbs the magnetizing inductance and the leakage inductance into the inverter topology. The closed-form expressions for all the Class-E inverter parameters are derived in terms of the coupling coefficient including (a) input impedance of the transformer with a complex load impedance, (b) equivalent phase of the resonant circuit for both nominal and sub-optimum operations, and (c) slope of the switch voltage waveform. The relationship between the voltage slope and the coupling coefficient is introduced, which indicates the proximity of the operating point from ZVS and ZDS conditions. A design example of the Class-E inverter is presented to achieve both ZVS and ZDS at an optimum coupling coefficient of 0.77 and ZVS condition for any coupling coefficient lower than 0.77 and over a wide range of load resistance.

Electromagnetic compatibility (EMC) is crucial when designing and operating wireless power transfer (WPT) systems for charging Electric Vehicles (EVs). WPT technology allows the transmission of electrical energy from a power source to a device without requiring physical contact. However, it can generate electromagnetic interference (EMI) that could disturb nearby electronic devices or harm people nearby. Shielding techniques are commonly employed to mitigate EMI in WPT systems. However, implementing effective shielding techniques can be complex, involving trade-offs between effectiveness, cost, and shielding weight. In this article, different types of existing shielding techniques for wireless charging systems of EVs are compared. The main concepts regarding the functioning of electromagnetic shields and the theory of the functioning of WPT systems have been also briefly introduced.

This paper presents an effective approach to the fault diagnosis of pulsewidth modulated (PWM) DC-DC power converters. It is based on a multilayer multivalued neuron neural network with a complex QR decomposition. This network is used to identify the parameter values running out of tolerance range in two topologies of PWM DC-DC converters, namely, the buck and boost circuits. The proposed technique is based on measurements of steady-state voltages and currents waveforms.

In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.

This paper presents the design procedure of an electric circuit that can perform the battery state diagnosis and, simultaneously, provide its charging. A fast and embedded impedance measurement method is also proposed; this is based on a broadband current signal excitation on the battery during the constant current charging phase. The proposed solution performs the electrochemical impedance spectroscopy (EIS), which is known to provide useful information about battery chemical–physical property changes due to aging or failure events. To demonstrate the functionalities of the proposed method, the spectroscopy is implemented in the control in the wireless charging system. An EIS charging test is simulated on an equivalent circuit model, which emulates the battery impedance properties in a specified frequency band. Circuit parameters are evaluated by experimental data. According to the obtained results, the proposed method allows us to reach an accurate estimation of the battery state and represents a promising solution for an embedded diagnostic of battery health thanks to its simplicity and speed.