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

Abstract

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.INDEX TERMS Inductive heating, resonant power conversion, wireless power transfer, deep learning, neural network, electromagnetic design, induction heating, home appliances.

Modeling and Design of Cookware for Induction Heating Technology With Balanced Electromagnetic and Thermal Characteristics

“…Advances in power electronics include resonant inverters with improved performance or increased flexibility of use [6], [7]. Machine learning techniques are also being currently applied to automatic pot recognition [8], [9], whereas inverse models were proposed to predict the cookware temperature [10] from the electromagnetic energy profile. These works mainly consider the different enabling technologies separately.…”

Load Estimation for Induction Heating Cookers Based on Series RLC Natural Resonant Current

“…[22] detected equivalent parameters by using the power factor and the absolute amplitude of the impedance |Z|. As IHCs based on quasi-resonant converters have the shortage of high-voltage stress on the power transistors [23], many studies [24][25][26][27][28][29][30] have focused on estimation methods for HBSRCs; specifically, these studies have proposed estimation methods based on single values or the average value of feedback electrical signals within a bus voltage cycle [24][25][26][27]. One of these studies [24] used a phase-sensitive detector, the inputs of which are multiplied by sine and cosine signals of the feedback voltage and current, respectively, and calculated R eq and L eq by using a low-pass filter.…”

“…Finally, [27] measured the resonant capacitor voltage and voltage harmonics for estimation. A study also determined the EHCR of cookware by using deep learning [28]. Two studies [29,30] have considered the influence of bus voltage variations on R eq and L eq estimations.…”

EMI Reduction Via Resonator Coils in Glassless Integrated Domestic Induction Systems

“…Nowadays, an intense research activity is still detected in this field, mainly focused on improving the performance and reducing the cost of current cooktops. This research activity encompasses power electronics converters for an efficient use of the energy [2], [3], coil arrangements for the improvement of the user flexibility [4], [5], [6], load identification systems [7], [8], sensing functionalities [9], [10], [11], inductor PCB implementations [12], magnetic materials [13], robotized mobile inductors [14], or adapting non-metallic cookware for induction cooktops [15].…”