Multiple-Output ZVS Resonant Inverter Architecture for Flexible Induction Heating Appliances

Flexible cooking surfaces have changed the domestic induction heating product paradigm enabling the use of a wider range of cookware materials, shapes, and positions. In order to implement such systems, multiple-output resonant inverters featuring high-performance and high-efficiency operation while achieving a cost-effective implementation are required. This paper proposes a multiple-output zero-voltage-switching resonant inverter for flexible induction heating appliances. The proposed converter features a matrix structure, enabling a cost-effective implementation with a reduced number of power devices while achieving high performance and low switching losses. It has been tested by means of an experimental prototype featuring 48 induction heating coils, proving the feasibility of the proposed approach

Vessel Recognition in Induction Heating Appliances - A Deep-Learning Approach

The selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs

Mains-Synchronized Pulse Density Modulation Strategy Applied to a ZVS Resonant Matrix Inverter

Multi-output inverters have become a key enabling technology to increase surface flexibility in domestic induction heating appliances. The most commonly used power converter topologies are based on electromechanical relays in order to multiplex the connected loads and obtain a proper heat distribution. This solution, which is used in combination with other modulations such as square waveform, relies on the thermal inertia of the pot as it needs long power-averaging periods to reduce the reiteration of the switching noise. However, it presents a significant limitation in terms of acoustic noise, reliability, and thermal performance. To overcome these limitations, complete solid-state inverters that can be operated at higher frequencies are proposed. This change in the design paradigm of the pulse density modulation strategies leads to improved thermal control in the pot and better user experience, but at the same time increases challenges due to design constraints imposed by electromagnetic compatibility regulations. This article analyzes the possibilities of a new mains-synchronized pulse density modulation applied to a flexible induction cooktop that uses a multiple-output ZVS resonant inverter topology. The feasibility of the control strategies has been tested by means of a prototype featuring 12 2-kW induction heating loads. © 1982-2012 IEEE

Asymmetrical Modulation Strategies for Partially Covered Inductors in Flexible Induction Heating Appliances

Cost-effective multi-output resonant inverter topologies are a key enabling technology for the development of flexible surfaces for induction heating appliances. These topologies present several challenges when applied to a wide range of IH-loads simultaneously. In this paper, two asymmetrical modulations are proposed as an alternative solution to control output power. The proposed approach has been verified using an experimental prototype featuring 2 induction heating loads up to 3.6 kW with output power control in the whole operating range

Soc-based in-cycle load identification of induction heating appliances

The equivalent load of an induction hob is strongly dependent on many parameters such as the switching frequency, the excitation level and the size, type, and material of the vessel. However, real-time methods with the ability to capture the variation of the load with the excitation level have not been proposed in the literature. This is an essential issue as most of the commercial induction hobs are based on an ac-bus voltage arrangement. This article proposes a method based on a phase-sensitive detector that offers an online tracking of the equivalent impedance for this type of arrangements. This algorithm enables advanced control functionalities such as clustering of vessels, material recognition, and premature detection of ferromagnetic saturation, among others. After simulation and experimental validation, the method is implemented into a prototype with a system-on-chip to verify its real-time behavior. The proposed approach is applied to different real-life situations that prove its great performance and applicability

A versatile resonant tank identificationm methodology for induction heating systems

Induction heating has become the most advance heating process due to benefits, such as efficiency, performance, cleanness, and safety. In this process, the electromagnetic coupling between the induction coil and the induction target is a key, determining the heating performance as well as the resonant power converter operation. This letter proposes an accurate and cost-effective resonant tank identification method applied to induction heating systems. It is based on monitoring the resonant capacitor voltage in order to calculate the resonant tank quality factor. The proposed methodology has been tested using a versatile power electronics test bench applied to domestic induction heating, proving the feasibility of this proposal

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-Time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances

Acoustic noise analysis of multiplexed strategies in multi-output converters for induction cooktops

Recent developments in domestic induction heating seek to increase the cooking surface flexibility while maintaining cost-effective implementations. In order to do so, the interconnection of classical topologies or the optimized design of new ones is being prospected. This approach usually leads to a higher complexity in the power control strategies. Thus, multiplexed load power control is used due to its versatility with the different topologies. Despite the advantages of this technique, power variations over the different pots generate a change in the forces between those and the inductors that might generate acoustic noise. This paper analyses multiplexed load power control and its restrictions and presents a modulation strategy based on switching frequency and duty cycle variation that allows a soft-transient load activation and deactivation, reducing the generated acoustic noise

Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters

Flexible-surface induction cooktops rely on multi-coil structures which are powered by means of advanced resonant power converters that achieve high versatility while maintaining high efficiency and power density. The study of multi-output converters has led to cost-effective and reliable implementations even if they present complex control challenges to provide high performance. For this scenario, model predictive control arises as a modern control technique that is capable of handling multivariable problems while dealing with nonlinearities and constraints. However, these controllers are based on the computationally-demanding solution of an optimization problem, which is a challenge for high-frequency real-time implementations. In this context, deep learning presents a potent solution to approximate the optimal control policy while achieving a time-efficient evaluation, which permits an online implementation. This paper proposes and evaluates a multi-output-resonant-inverter model predictive controller and its implementation on an embedded system by means of a deep neural network. The proposal is experimentally validated by a resonant converter applied to domestic induction heating featuring a two-coil 3.6 kW architecture controlled by means of a FPGA. Autho