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This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a single constraint to write the matrix equation. The model is algebraically manipulated to obtain a matrix equation, and a signal processing system is developed to prepare the signals for the identification algorithms. The proposed method builds on the results obtained using an unconstrained ordinary least-squares (OLS) technique. The method is tested both in simulation and experimentally, using a specially-designed experimental rig. A current ramp input is used to generate the corresponding output voltage and its derivatives. The results obtained from the constrained off-line minimization algorithm are compared with those obtained using a traditional off-line estimation method. The discussion of the results shows that the proposed method outperforms the traditional estimation technique. In summary, this paper contributes to the field of DLC parameter estimation by introducing a new off-line constrained minimization technique. The results obtained from the simulations and experimental rig demonstrate the effectiveness of the proposed method with two of three parameters showing relative errors less than 5%.

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range.

The efficiency of Hybrid Electric Vehicles (HEVs) may be substantially increased if the energy of the exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered by means of a properly designed turbogenerator and employed for vehicle propulsion; previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% by employing a 20 kW turbine on a 100 HP rated power thermal unit. The innovative thermal unit here proposed is composed of a supercharged engine endowed with a properly designed turbogenerator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In these two parts, the realistic efficiency of the innovative thermal unit for hybrid vehicle is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors present a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, owing to some assumptions and approximations, allows a fast determination of the proper turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine considered is quite different from the ones commonly employed for turbocharging application; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In the Part 2 paper, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantage that could be achieved by the implementation of the turbogenerator on a hybrid propulsion system is evaluated through the performance prediction model under the different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbocompound system could gain vehicle efficiency improvement between 3.1% and 17.9%, depending on the output power level, while an average efficiency increment of 10.9% was determined for the whole operating range.

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

The importance of renewable energy exploitation reduces the energy dependence on fossil fuels. Despite technological progress, in several remote areas and small islands the energy production is nowadays dominated by the utilization of fossil fuels. With new, increasingly stringent laws on polluting emissions and the need to lower production costs, it is necessary to exploit as many renewable sources as possible. In order to implement these considerations, it was decided to study renewable energy production. The study was carried out by estimating the energy production on a monthly and annual basis considering a mix of three plants, namely marine, solar, and wind. Simulations on wave production were carried out on a new device developed by the research team at the University of Palermo. In order to be able to perform these simulations, input climate data are required. These data are normally available in literature or obtainable by using specific GIS tools. As criterium, the Levelized Cost of Energy, normally applied to a single technology, is extended to the entire energy mix. Minimizing this parameter, the best solution is individuated, and capable of supplying 50% of the summer electrical load with renewable energy sources. The results carried out from a case study based in aeolian islands show that the solar production reaches 10.2%, the wind production reaches 45.47% and sea wave production reaches 3.04%. In this way, the diesel production decreases to 41.29%. This method can be easily applied for several small islands, estimating for several sites the ability to reduce the energy production from fossil fuels.