Failure of a phase current sensor in permanent magnet synchronous machine (PMSM) drive can highly impact the performance of the drive. Especially, offset faults can cause torque oscillations, which can lead to an unacceptable deterioration of the drive's performance and mechanical damages. The diagnosis of this fault condition is of fundamental importance in many applications and it is often solved by means of highly computational state estimation techniques. This article presents a new model-based approach to systematically and accurately detect and estimate phase current sensor offsets in PMSM drives. The steady-state analytical solution of the PMSM drive model, including the control loop of the field oriented control and the sensor offset disturbance, has been found, allowing for estimating the effect of the fault on the electric drive performance. Then, a simple and effective diagnostic algorithm has been developed to detect, isolate, and accurately estimate the location and the magnitude of the fault eliminating any need for state estimation or observers. Numerical and experimental results have been reported to validate the proposed model and diagnostic algorithm even in case of motor parameters variation.

Lithium-ion batteries are widely used in electric vehicles due to their high specific energy value. The continuous charge/discharge cycles pf batteries, determining a change of their State of Health (SoH) and a reduction of their Remaining Useful Life (RUL). The relevant literature describes a variety of methods for the prognostic of battery degradation and RUL estimation, based on electrochemical models or on statistical and artificial intelligence techniques. However, the capacity degradation of lithium-ion batteries depends on many uncertain parameters that are suitable to be estimated and modeled within a probabilistic framework. Therefore, the development of new probabilistic methodologies for the prognostic of lithium-ion batteries can be useful and effective. In this paper, probabilistic models based on time series (AutoRegressive Integrated Moving Average model with eXogenous predictors, (ARIMAX)) and regression approaches (Linear Quantile Regression (LQR), Bootstrap Multiple Linear Regression (B-MLR) and Bayesian Bootstrap Multiple Linear Regression (BB-MLR)) are developed and compared for this purpose. All the considered models are specifically suited up to exploit data coming from Accelerated Degradation Tests (ADTs), and developed under two different approaches (i.e., non-differentiation and differentiation of the target time series) to predict SoH and RUL. Moreover, a dedicated procedure to extract a single, point value from the probabilistic predictions is presented to let the models work also in deterministic scenarios. The performance and the comparison between the proposals and several benchmarks taken from the literature are carried out using data from publicly available databases, confirming the validity and the accuracy of the proposed solutions.

Hybrid electric propulsion in the aviation field is becoming an effective alternative propulsion technology with potential advantages, including fuel savings, lower pollution, and reduced noise emission. On the one hand, the aeroengine manufacturers are working to improve fuel consumption and reduce pollutant emissions with new combustion systems; on the other hand, much attention is given to reducing the weight of the batteries increasing the energy density. Hybrid electric propulsion systems (HEPS) can take advantage of the synergy between two technologies by utilizing both internal combustion engines (ICEs) and electric motors (EMs) together, each operating at their respective optimum conditions. In the present work, some numerical investigations were carried out by using a zero-dimensional code able to simulate the flight mission of a turboprop aircraft, comparing fuel consumption and pollutant emissions of the original engine with other two smaller gas turbines working in hybrid configuration. An algorithm has been implemented to calculate the weight of the batteries for the different configurations examined, evaluating the feasibility of the hybrid propulsion system in terms of number of non-revenue passengers.

European energy policy guidelines recognise renewable energy sources the main mean to contrast the rapid fossil fuels depletion and the related global warming. Marine energy source represents an attractive and inexhaustible reservoir from which to draw. One of the major difficulties in integrating sea wave generation systems or equivalently wave energy converters (WECs) with existing electrical systems is the management of their generation intermittency. This is essentially due to the inherent nature of the sea wave source. Energy storage represents an effective enabling technology for mitigating such an effect. To this aim, this study proposes an efficient control strategy for embedded floating buoy generation systems with energy storage technologies in order to regularise the injected grid power while minimising the contractual power established by the distribution system operator. The control strategy has been tested numerically on a grid connected DC microgrid formed by a DC bus at which a floating buoy generation system is interfaced with an energy storage system, supercapacitors‐based, having the purpose of smoothing the natural power fluctuations of the WEC.

The aim of the paper is the study of an optimal integration of a supercapacitor-based storage system and inductive power transfer system for the free-catenary operation of a tramway. Starting from the definition of the topology of inductive power transfer system, we propose and analyze a design procedure for the pads of the inductive power system. In the second part of the paper, an optimal integration strategy for the correct size of on-board supercapacitors and the power of the inductive energy transfer are proposed. The results obtained by means of numerical simulations are discussed.

The growing interest in the use of energy storage systems to improve the performance of tramways has prompted the development of control techniques and optimal storage devices, displacement, and sizing to obtain the maximum profit and reduce the total installation cost. Recently, the rapid diffusion of renewable energy generation from photovoltaic panels has also created a large interest in coupling renewable energy and storage units. This study analyzed the integration of a photovoltaic power plant, supercapacitor energy storage system, and railway power system. Random optimization was used to verify the feasibility of this integration in a real tramway electric system operating in the city of Naples, and the benefits and total cost of this integration were evaluated.