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.

The second life use of lithium-ion batteries has gained significant attention in recent years, driven by the potential to repurpose cells from electric vehicles for less demanding applications. A critical aspect of this repurposing is accurately estimating the Remaining Useful Life (RUL) of the batteries. Traditional techniques often rely on data from the battery’s first life, which may not be available in practical scenarios. To address this issue, we propose a data-driven method for RUL estimation that does not depend on first-life information. Our approach considers a realistic scenario where an aged battery cell, lacking previous usage data, is evaluated for second life use through a limited number of test cycles. We compute features such as incremental capacity curves, and other health indicators from the measured voltage and current waveforms of the used cell. These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This methodology achieves an average error of only 62 cycles for cells with a lifespan of up to 1200 cycles and a RUL error of less than 10% for deeply aged batteries. These results outperform state-of-the-art algorithms that utilize data from the cell’s entire lifespan, demonstrating the efficacy and robustness of this approach.

This paper explores a new methodology based on data-driven approaches to identify and track degradation processes in Li-ion batteries. Our goal is to study if it is possible to differentiate the state of degradation of cells that present similar aging in terms of overall parameters (similar remaining capacity, state of health or internal resistance), but that have had different applications or conditions of use (different discharge currents, depth of discharges, temperatures, etc.). For this purpose, this study proposed to analyze voltage waveforms of cells obtained in cycling tests by using an unsupervised neural network, the Self-Organizing Map (SOM). In this work, a laboratory dataset of real Li-ion cells was used, and the SOM algorithm processed battery cell features, thus carrying out smart sensing of the battery. It was shown that our methodology differentiates the previous conditions of use (history) of a cell, complementing conventional metrics such as the state of health, which could be useful for the growing second-life market because it allows for determining more precisely the state of disease of a battery and assesses its suitability for a specific application.

In this paper a nonlinear dynamic lumped circuit model of a switched reluctance motor is proposed. This model includes the effect of core losses and leakage inductance. The parameters of the nonlinear circuit are identified from the experimental flux linkage and phase current. The proposed model, compared to other available static models, allows the coupling between electrical and mechanical phenomena and, therefore, it is suitable for the characterization of the full rotating behaviour of the motor. This nonlinear model is based on the space-state framework and it is implemented in Matlab-Simulink. Model accuracy has been verified at both, constant speed and accelerating; the correct fit between the model and a real switched reluctance machine is shown. The accurate fit of the stator waveform obtained in model simulations with experimental measurements proves both, the accuracy of the proposed model and the correct estimation of its components. All these parameters are non-linearly dependent on the current and the actual rotor position.