In induction home appliances, the characterization of the physical properties of the vessel allows for better control strategies and new cooking functionalities. Current modelling techniques calculate a simple ${R}$ –… Click to show full abstract
In induction home appliances, the characterization of the physical properties of the vessel allows for better control strategies and new cooking functionalities. Current modelling techniques calculate a simple ${R}$ –${L}$ equivalent circuit that models the inductor impedance taking into account all the physical coupling parameters, but these techniques do not allow to directly extract the physical parameters, such as vessel material. In this paper, a new methodology for online detection of the pot material is presented. On this approach, the inductor included in the hob simultaneously serves as heating element and sensor. First, harmonic impedances are calculated by spectral estimation from the currents and voltages waveforms recorded at the inductor. Then, a feature set, including four harmonic impedances and the power factor, is processed by machine learning algorithms for the identification of the pot material. The material identification system is implemented onto an embedded processor similar to those included in current induction hobs; thus, smart sensing features are incorporated onto a domestic cooker without using additional sensors, at no additional cost.
