Nowadays, lithium-ion is among the most used chemistry for batteries and shows an increasing market growth rate. However, to reduce failure or safety risks, the battery State-of-Charge (SoC) must be accurately monitored and predicted by a suitable Battery Management System (BMS). Artificial Intelligence (AI) techniques have been extensively applied to this field with good results. Typically, AIs are trained on dynamic profile data, emulating battery charging and discharging cycles related to the application under test. In this paper, a novel approach is presented: application independent constant current profiles are used to train a Support Vector Regression (SVR) algorithm. To enhance the estimation accuracy, the output of the obtained SVR model was post-processed. Finally, an error correction algorithm was applied to further reduce the estimation error. The system is validated over test cycles representing different application scenarios for the battery cell operations. For the development of the proposed approach, a total of 105 constant current discharge profiles for the training and 20 realistic test cycles for the validation have been considered, including standard automotive cycles and a generic battery powered power tool. The performance in the SoC estimation resulted in a Root Mean Square Error (RMSE) of 0.94% and a Mean Absolute Error (MAE) of 0.75% over all the test cycles. Error metrics are comparable to those obtained for SoC estimation AI algorithms based on traditional approaches using application dependent battery profiles for the training phase.

This paper describes the realization of a high-performance drive based on stepping motors, with a specific reference to the optimal choice of pulsewidth modulation. An original modulation technique is proposed that allows, in conjunction with current feedback, to achieve high performances together with a reduced current ripple. A prototype digital signal processor-based drive was realized for a small-size stepping motor.

Multi-variable monitoring systems for on-line monitoring of induction motors allow larger versatility and robustness. In case of rotor faults, the diagnostic procedure based on sideband current components may fail for the presence of inter-bar currents that reduce the amplitudes of these components. On the other hand the inter-bar currents modify the core vibration; therefore the analysis of both line currents and core vibration signals may increase the effectiveness of the diagnostic procedure. In this paper the features of stator currents and core vibration signals are investigated in order to achieve an accurate and reliable diagnostic system. Moreover stray flux signals are considered to explore the possibility of obtaining further information

The arrival on the market of new power devices based on wide bandgap semiconductors has raised a relevant interest due to their superior properties compared to conventional technologies. On the other hand, these devices are inherently characterized by high rates of voltage changes over time, which may result in reliability challenges in electric drives adopting them. In fact, dangerous voltage overshoots at the motor terminals and uneven voltage distributions within the machine windings may occur. These phenomena can trigger a high insulation stress and partial discharges and, as a consequence, they may concur to the premature failure of the dielectric materials. This paper proposes a flexible and comprehensive modelling approach for the accurate analysis and estimation of both voltage overshoots and voltage distributions in a typical converter-cable-motor system intended for more electric aircraft applications. The modelling results are validated against experimental measurements carried out on a physical prototype comprising a wide bandgap-based converter, a connecting cable and an electrical machine stator. The findings are then used in the companion papers (part II and part III) to investigate the dependence of partial discharge phenomena on these voltage waveforms, highlight reliability challenges in modern ±270 V DC bus voltage drives for the more electric aircraft and discuss solutions.

Environmental issues and the global need to extend sustainable access to electricity have fostered a huge amount of research in distributed generation by renewables. The challenges posed by the widespread deployment of distributed generation by renewables, such as intermittent power generation, low inertia, the need for energy storage, etc., call for the development of smart grids serving specific local areas or buildings, referred to as microgrids and nanogrids, respectively. This has led in the last decades to the proposal and actual implementation of a wide variety of system architectures and solutions, and along with that the issue of the power converters needed for interfacing the AC grid with DC micro- or nanogrids, and for DC regulation within the latter. This work offers an overview of the state of the art of research and application of nanogrid architectures, control strategies, and power converter topologies.

The MCSA diagnostic procedures for the rotor diagnosis of induction machines are mostly based on the sideband lines near the supply frequency line that appear in the input current spectrum. The left component (1-2s)f is the effect of rotor backward rotating field caused by the cage rotor asymmetry and it varies with the asymmetry degree and with the rotor current, i.e. with the machine load. The right component (1+2s)f is caused by the speed ripple created by the left component, being the combined machine-load inertia the main parameter that spreads the asymmetry effect into the two sideband components. Theoretically it can be shown that the sum of the two components is not affected by the speed ripple effect and therefore this sum may be used as an effective diagnostic index to state rotor conditions. The above results can be experimentally validated only once a suitable test set-up is realized that allows changing the inertia value. To this aim a test bed was designed so that the virtual inertia of the mechanical system can be chosen by the user. This allows to prove the theoretical claims and to obtain a thorough understanding about the effect of inertia on sideband components, improving the performance of rotor condition monitoring techniques