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We investigated the influence of a fuel change from pure hydrogen to a hydrogen–ammonia mixture at different percentages on the electrochemical behavior of 50 mm in diameter Solid Oxide Fuel Cells (SOFCs) with sputtered thin buffer layers of Gd-doped ceria, varying the working temperatures from 800 °C to 650 °C. The results show that the performances of the cells are not affected by the fuel change for high working temperatures (800 °C and 750 °C). As an example, a power density value of 802 mW∙cm−2 at 1 A∙cm−2 is found when directly feeding the cell with 8 NmL∙min−1cm−2 of ammonia and with an equivalent flowrate of 12 NmL∙min−1cm−2 of H2. These power density output values are higher than those obtained in industrial state-of-art (SoA) SOFCs with screen-printed buffer layers fed with equivalent hydrogen flowrates, thanks to the improved electrochemical performances obtained in the case of cells with sputtered thin buffer layers of Gd-doped ceria. At lower working temperatures (700 °C and 650 °C), slight changes in the electrochemical behavior of the cells are observed. Nevertheless, in this temperature range, we also obtain an output current density value of 0.54 A∙cm−2 in a pure ammonia flowrate of 12 NmL min−1cm−2 at 800 mV and 700 °C, equal to the value observed in SoA button cells with industrial screen-printed GDC barrier layer fueled with 16 NmL∙min−1cm−2 of H2. These results pave the way towards the use of innovative SOFC structures with sputtered thin buffer layers fueled by ammonia.

The Industry 4.0 (I4.0) paradigm is considered one of the most trending topics in the academic and industrial context, that involves emerging technologies that can make the processes increasingly integrated and provide digital solutions for supporting companies towards the greater flexibility required by the market. To date, the scientific literature strongly addressed the development of enabling technologies and the assessment of their impacts in different industrial contexts. However, there is a lack of studies providing empirical evidence about how manufacturing companies are facing the digital transformation, in particular for smaller industrial realities. For this reason, this paper aims to study the knowledge, readiness, and dissemination level of the I4.0 paradigm and enabling technologies for Italian Micro, Small, and Medium Enterprises (MSMEs). A web-based survey was conducted, and 77 companies were interviewed. The survey results underline that MSMEs still have limited knowledge about I4.0 and are not well prepared for its implementation.

Using room temperature sputtering, we have deposited Gd0.1Ce0·9O1.95 buffer layers at the cathode/electrolyte interface of 10 cm2 circular Solid Oxide Fuel Cells. By suitably selecting the in air annealing step, we show the possibility to control the granular properties of the Gd0.1Ce0·9O1.95 buffer layers in order to optimize the Solid Oxide Fuel Cell's electrical performances. In particular, we maximise the buffer layer grain density via an annealing process with a plateau temperature of 800 °C, obtaining improvements in the final Solid Oxide Fuel Cell performances with an 8% increase in the current density at 800 mV and 700 °C with respect to the Solid Oxide Fuel Cell annealed at 1050 °C, and with a 59% increase in the current density at the same voltage and temperature when compared to the standard cells with the Gadolinium Doped Ceria buffer layer produced by lithographic processes. The achieved optimization procedure clearly illustrates the potentiality of the room temperature sputtering process followed by in-air annealing step in the control of the granularity of the deposited layers. Moreover, the obtained results open the way to further studies about the industrial scalability of the used technique and to its implementation in practical large scale cell production process.

The NOχ Storage Catalyst is currently envisaged to be implemented in light-duty passenger cars for nitrogen oxides reduction, in order to comply with strict emission legislation targets. Since robustness and durability of the engine and emission control system is the first priority in automotive application, to satisfy the need of robust on-board real time monitoring, diagnosis and control, computing efficient methods are needed. In this framework, a control oriented model that describes the dynamics of the main physical-chemical processes within the NSC catalyst, while still maintaining affordable computational burden, has been developed and validated. Model calibration has been performed, for light-duty application, along the NEDC test cycle, by using a statistical-based sub-optimal procedure, based on a parametric analysis which allows identifying the more suitable section of NeDC cycle for model identification, without the need to perform cost- and time- expensive experiments on the engine test bench. The procedure also accounts for missing information and sensors inaccuracies. The great potential of this methodology is the possibility to adopt not optimal designed tests for model parameters identification. The proposed methodology is proven to be effective for real time control strategy, directly embedded in ECU, and provide a sub-optimal but effective strategy for complex models calibration.

We investigate the influence of position, under large circular sputtering targets, on the final electrochemical performance of 35 mm diameter button solid oxide fuel cells with sputter-deposited Gadolinium doped Ceria barrier layers, positioned in order to almost cover the entirety of the area associated with a 120 × 80 mm2 industrial cell. We compare the results obtained via structural and morphological analysis to the Electrochemical Impedance Spectroscopy (EIS) measurements performed on the button cells, disentangling the role of different parameters. The Atomic Force Microscopy analysis makes it possible to observe a decrease in the roughness values from the peripheral to the central zones under the sputtering target, with peak-to-valley roughness values, respectively, decreasing from 380 nm to 300 nm, while Scanning Electron Microscopy and Energy Dispersive Spectroscopy show a dependence of the layer coverage from the position. The electrochemical performances of button cells with buffer layers of only 200 nm in thickness, and with negligible thickness gradients across them, show current density values of up to 478 mA/cm2 at 0.8 V and 650 °C, with an improvement of more than 67% with respect to button cells with standard (screen printed) buffer layers. These results point out the major influence exerted by parameters such as the thickness gradient and the coverage of the sputtered buffer layers in determining the final electrochemical performances.

Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%.

A benchmark problem for fuel efficient control of a truck on a given road profile has been formulated and solved. Six different solution strategies utilizing varying degrees of off-line and on-line computations are described and compared. A vehicle model is used to benchmark the solutions on different driving missions. The vehicle model was presented at the IFAC AAC2016 symposium and is compiled from model components validated in previous research projects. The driving scenario is provided as a road slope profile and a desired trip time. The problem to solve is a combination of engine-, driveline- and vehicle-control while fulfilling demands on emissions, driving time, legislative speed, and engine protections. The strength of this publication is the collection of all six different solutions in one paper. This paper is intended to provide a starting point for practicing engineers or researchers who work with optimal and/or model based vehicle control. Funding Agencies|Vinnova Industrial Excellence Center "Linkoping Center for Sensor Informatics and Control" LINK-SIC

The present paper describes a generalized scaling-up methodology applied to Polymer Electrolyte Membrane Fuel Cells. The use of proper scaling-up algorithms can reduce testing costs within fuel cell manufacturing process by evaluating full stack performance (i.e., impedance behavior) from a single cell/short stack measurement. The algorithm here described relies on a former approach developed by the authors and consists in a generalized methodology combining information measured on single cell and simple physical models (e.g., charge transfer resistance expressed through Tafel equation). A robust technique for the identification of cell reference operational state, such as membrane hydration, from non-scaled data is also introduced. Connection between charge transfer resistance and limiting current is established through diffusion losses modelling. Single cell internal states are estimated by means of inverse models function of numerical intercepts of measured cell spectrum. Stack impedance estimation is then performed through stack internal states assumptions. To prove the consistency and robustness of the proposed methodology, literature data used to design and test the former algorithm version are here considered for algorithm testing and verification.

In the recent years, engine developer attention is focused on the application of advanced control strategies for the SCR system, considered as the leading technology for the abatement of NOX emissions in Diesel engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to a one-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity Ω, is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced estimation accuracy with a slight increase of the computational burden that is still suitable with on-board application.

This work presents an innovative model-based approach for the development of mathematical transfer functions capable of correlating Solid Oxide Fuel Cells (SOFCs) degradation rate to the applied operating conditions. Such functions can be used for fuel cell lifetime prediction and Accelerated Stress Test (AST) protocols design. The proposed approach relies on multiscale modelling methodology and links local degradation to high level performance models to evaluate the key operating variables accelerating voltage decay over time. A thorough simulation analysis is performed to convey the correlation among operating variables and degradation rate into mathematical transfer functions. To better illustrate the overall design and application process of such functions, a case study accounting for Ni agglomeration is addressed. The multiscale modelling framework is applied to correlate microscale (i.e., Ni particles size change) and macroscale (i.e., SOFC voltage reduction) levels through the most affected mesoscale parameters. The model is then used to simulate voltage decay over time and link degradation rates to the applied operating conditions. Afterwards, a parametric analysis is performed to investigate the influence exerted by each operating variable on the degradation rate and derive the transfer functions. An example of application for Accelerated Stress Test (AST) protocols design is then given.

The present paper details a performance analysis made through model-based simulation of a dual stage multi-level control algorithm applied to heavy duty vehicles that accounts for traffic information. Such work exploits a control algorithm at different level of traffic congestion. In the two stages control, the first level entails a supervisory optimizer, which evaluates the optimal speed and gear shifting profile according to road and vehicle information to minimize fuel consumption; the second level is an actuator control that computes the powertrain signals to be applied to the engine actuators to comply with the indications given by the supervisory control. The advancement brought by the present work resides in the introduction of speed constraints related to different traffic levels: i) free flow, ii) free flow/conditioned, iii) conditioned and iv) congested. To each state, a maximum limiting speed is associated and several scenarios are investigated in simulated environment. All the results are compared to a conventional Cruise Controller to assess the achievements in terms of fuel consumption reduction, assessing that the proposed control strategy ensures an average reduction of about 3.5% over all the investigated conditions.

The introduction of actual and upcoming emission standards, move the industrial attentions from laboratory towards real-world emission performance, In-Service Conformity testing (ISC) and life-time periods. Besides advanced air management, fuel injection pattern optimization and aftertreatment systems, the goal to realize optimal and robust powertrain setting under varying operating conditions, while ensuring the proper operation of ATs is challenging and require a massive calibration effort. To accomplish this task, the present research deals with supervisory controller for the integrated engine-SCR system, also referred in literature as EGR-SCR balancing. The goal is to comply with NOx emissions limit and, at the same time, to minimize global costs over transient operating conditions. The potential of this IEM strategy is demonstrated for a typical type-approval test case. The optimization identifies the effective EGR actuation, based on the actual powertrain state, engine settings and ATs performances. The resulting control strategy optimizes the overall performance.

This work aims at implementing an advanced monitoring and diagnostic tool for μ-CHP PEM fuel cell systems running on-board. Such a tool is able to determine the FC current status (condition monitoring) to support stack failures detection. Six faults are investigated: fuel starvation, air starvation, flooding, drying, CO contamination and sulphur poisoning. The developed methodology is based on the use of the Electrochemical Impedance Spectroscopy (EIS) measurements and Equivalent Circuit Model (ECM) approach to infer on them. Experimental data from HEALTH-CODE project are exploited for training and to finally validate the model-based algorithm. The fault detection and isolation algorithm, which works on the relevant features extracted from EIS data, is herein reported. Eventually, the offline validation results of the algorithm are presented.

This work focuses on the development of a control and diagnosis-oriented model of a Solid Oxide Fuel Cell (SOFC) integrated system module (ISM). Fuel cell researchers and developers are currently investing significant resources on such fuel cell-based energy conversion systems, as a consequence of their adaptability to a variety of applications and power size. In this configuration, heat exchanges among components are difficult to manage and, consequently, reliable model-based tools are required to enhance the design phase. Therefore, the main heat exchange mechanisms (including radiation, here modeled via view-factors-based approach) are evaluated by simplifying 3-D geometries, as well as assuming the outlet temperature from each component as its state variable. The resulting lumped parameters model was proven effective in achieving the required compromise between accuracy and computational time, particularly in view of its real-world deployment for advanced balance of plant (BoP) analyses, as well as within control and diagnostic model-based tasks. Moreover, the entire SOFC ISM model is generic and can be suitably applied to different layouts, through the characterization of main model parameters. Simulation results are in agreement with experimental data collected on a non-conventional micro-CHP system, named HoTBox™, tested under nominal operating conditions within the EU funded project DIAMOND.

The present study investigates the energy needs for the on-board generation of oxyhydrogen (HHO) used as fuel additive on vehicles. HHO production is performed through the use of an alkaline electrolyzer, directly taking energy from the equipped internal combustion engine. A longitudinal vehicle dynamic model is used to evaluate the driving power to be supplied by the engine for two reference speed profiles, NEDC and WLTC. The performed investigation determines the engine brake thermal efficiency gain required to ensure HHO production without increase in fuel consumption. The results can be used as guidelines for the development of on-board control strategies.

In this paper, a multi-level, comprehensive control technique for a heavy duty vehicle, capable of reducing fuel consumption while complying with engine constraints on both operating conditions and smoke emissions has been developed. The higher control layer, using road data, engine and vehicle parameters, computes the optimal speed and gear shifting profiles through an off-line, dual step dynamic programming algorithm. Since fuel consumption alone could be potentially decreased by driving at lower speeds and thus increasing travel time, this last aspect is also taken into account in the presented algorithm. The lower control layer, operating in real time, computes suitable powertrain control signals assuring the tracking of both reference speed and gear trajectories, while respecting the constraints imposed on engine operation. After the general description of the optimal control problem aimed at minimizing fuel consumption, an approach for its solution, implemented in the higher control layer, is presented. Afterwards, the lower control layer is described. In conclusion, the effectiveness of the developed control structure and the achievable improvement in terms of fuel consumption in comparison with a traditional fixed-point cruise controller are assessed through simulations in different driving scenarios.

This paper proposes an analytical model of the water content of the electrolyte of a Proton Exchange Membrane Fuel Cell. The model is designed by accounting for several simplifying assumptions, which make the model suitable for on-board/online water management applications, while ensuring a good accuracy of the considered phenomena, with respect to advanced numerical solutions. The achieved analytical solution, expressing electrolyte water content, is compared with that obtained by means of a complex numerical approach, used to solve the same mathematical problem. The achieved results show that the mean error is below 5% for electrodes water content values ranging from 2 to 15 (given as boundary conditions), and it does not overcome 0.26% for electrodes water content above 5. These results prove the capability of the solution to correctly model electrolyte water content at any operating condition, aiming at embodiment into more complex frameworks (e.g., cell or stack models), related to fuel cell simulation, monitoring, control, diagnosis and prognosis.

The improvement of reliability, durability and availability of fuel cell systems represents a key factor for their mass-market deployment in several application areas. The development of advanced algorithms oriented towards fuel cell system monitoring, diagnostics, prognostics and control can significantly reduce the incidence of degradation mechanisms and faulty events on fuel cell performance and durability. Therefore, a valuable increase in system efficiency and lifetime can be achieved by proper design of such algorithms, especially for applications working under highly variable load profiles. The present work deals with the design of a model-based control algorithm aimed at mitigating degradation effects on a Polymer Electrolyte Membrane Fuel Cell (PEMFC) system. Such an algorithm embeds cell degradation models, describing Ostwald ripening and Platinum dissolution mechanisms, which affects the cell Electrochemical Surface Area. The control algorithm is developed aiming at PEMFC durability improvement, while ensuring user power request, and its performance is evaluated in simulated environment addressing stationary power generation applications with variable load profile and comparing the cell degradation decay under different control strategies.

In reversible solid oxide cell (rSOC)-based renewable microgrids connected to the network, control logic optimizing the power split with the grid is needed. The benefits associated with such configuration are assessed and compared to an islanded reversible microgrid, previously designed via a model-based approach. Multiple loads are met, including those of a residential complex as well as electric and fuel cell vehicle fleets. The energy storage system consists of a rSOC and hydrogen and thermal storage tanks. The targeted optimal power split annual trajectory is found through dynamic programming. Suitable simplifying assumptions are introduced to develop a fast reduced-order sub-model from the original islanded rSOC microgrid, thus conceiving the optimal control problem as a one state dynamic programming task. Due to the connection to the grid, the energy storage system no longer has to compensate for the difference between generation and demand, thus enabling economic rSOC sub-scaling. Finding the best control strategy and resizing jointly lead to capital and operating expense reductions. The optimization outcomes indicate how a 60% reduction in rSOC nominal power allows for a simple payback period of 40% less than for the islanded design, as well as for proper rSOC capacity exploitation.

This work illustrates an innovative diagnostic technique able to improve fault isolability in Solid Oxide Fuel Cell (SOFC) energy conversion systems. On-board sensor reduction may induce fault clustering and, thus, hinder univocal fault isolation. According to the proposed technique, isolated system component sub-models, fed with faulty inputs, can be used to solve this issue. These models provide a set of redundant residuals, which react only if the related component is under faulty state. The technique is characterized and tested in simulated environment on an SOFC Anode Off-Gas Recycling (AOGR) system. Hydrogen external leakage, fuel and air heat exchangers efficiency reduction and recirculation unit malfunction are addressed and implemented in the complete system model. This latter is used to simulate system variables in both nominal and faulty conditions and compute residuals for fault detection and isolation. The sub-models are then used to introduce further residuals, and their behaviour is investigated at different fault magnitudes. The analysis is firstly performed in an ideal case scenario, considering the fault isolability that can be theoretically achieved. Then, practical application of the diagnostic algorithm is discussed, considering quantitative residuals deviation and properly analysing the effects of feeding the sub-models with inputs provided by both faulty and nominal models. The achieved results confirm the capability of the proposed approach to univocally isolate the considered faults in all the investigated conditions. Moreover, the analysis of the real case scenario proved the proposed algorithm suitable also for real applications.

Power generation systems based on Solid Oxide Fuel Cell Systems (SOFC) are one of the most promising technology in renewable (or more efficient) energy generation thanks to low emissions, high performance, modularity and noiseless. This work aims at developing a dynamic model of an integrated system module called Diamond-A using solid oxide fuel cells. The model allows simulating the behavior of a non-conventional micro-CHP system, starting from operating variables, like temperatures or molar flows of chemical species taking part into the reactions. A lumped model approach is used to reach a good compromise between accuracy and computational time (necessary for on-board applications). Starting from detailed technical data and the system layout, the model solves a set of balance equations considering both energy flows and chemical reactions taking part in each component of the module. In the Diamond-A, the thermal exchanges among components are evaluated assuming the outlet temperature from each component as state variable. Pipes thermal losses are neglected. The entire model is generic and can be suitably applied for different layouts through the characterization of configuration model parameters. The capability of the model has been evaluated, simulating a non-conventional micro-CHP system, named HoTbox TM , under nominal operating conditions. Results are in agreement with experimental data provided in the frame of the EU project Diamond.

To meet current Diesel engine pollutant legislation, it is important to manage after-treatment devices. The paper describes the development of Neural Network based virtual sensors used to estimate NOx emissions at the exhaust of automotive Diesel engines. Suitable identification methodologies and experimental tests were developed with the aim of meeting the conflicting needs of feasible on-board implementation and satisfactory prediction accuracy. In addition, since the prediction of control-oriented models is typically affected by engine aging and production spread as well as components drift, least square technique features were exploited in order to overcome these issues by adapting the virtual sensor output. The NOx adaptive virtual sensor was tested via comparison with experimental data, measured at the engine test bench on a turbocharged common-rail automotive Diesel engine. Furthermore, besides model validation, the experimental measurements were modified to simulate a sensor drift in order to enable full assessment of the proposed LS-based algorithm adaptation capabilities.

Degradation and poisoning of solid oxide fuel cell (SOFC) stacks are continuously shortening the lifespan of SOFC systems. Poisoning mechanisms, such as carbon deposition, form a coating layer, hence rapidly decreasing the efficiency of the fuel cells. Gas composition of inlet gases is known to have great impact on the rate of coke formation. Therefore, monitoring of these variables can be of great benefit for overall management of SOFCs. Although measuring the gas composition of the gas stream is feasible, it is too costly for commercial applications. This paper proposes three distinct approaches for the design of gas composition estimators of an SOFC system in anode off-gas recycle configuration which are (i.) accurate, and (ii.) easy to implement on a programmable logic controller. Firstly, a classical approach is briefly revisited and problems related to implementation complexity are discussed. Secondly, the model is simplified and adapted for easy implementation. Further, an alternative data-driven approach for gas composition estimation is developed. Finally, a hybrid estimator employing experimental data and 1st-principles is proposed. Despite the structural simplicity of the estimators, the experimental validation shows a high precision for all of the approaches. Experimental validation is performed on a 10 kW SOFC system.

The paper presents model-based scenario analyses of micro and mild tri-hybrid vehicle configurations, related to existing conventional vehicles equipped with small power proton exchange membrane (PEM) fuel cell (FC) systems and, eventually, additional battery pack. The analyses are intended to support short-to medium-term development of fuel cell powered Light Duty Vehicles (LDVs) technology and are useful for carmakers to quantitatively assess the potential advantages associated to the afore-mentioned opportunities in terms of short-term clean and cost-efficient production of hydrogen. The analyses are performed making use of a comprehensive vehicle model, based on a hybrid (black-box and lumped parameters) approach, of a medium passenger car equipped with a turbocharged SI engine. The model has been enhanced to account for the additional components of two different powertrain configurations, namely conventional powertrain with ICE and fuel cell Auxiliary Power Unit (APU) and hybrid powertrain ICE/FC. Simulations have been carried out vs. standard driving cycles for two energy management strategies. The results allow evaluating the impact of powertrain configuration on CO2 emissions, in case of both Tank-to-Wheel (TTW) and Well-to-Wheel (WTW) analyses.

This article addresses a generalized scaling-up technique applied to proton exchange membrane fuel cells. Such a methodology allows simulating the impedance related to a full stack from that of a single cell or short stack, combining experimental data with physical reasoning and phenomenological modelling. The use of such technique can reduce electrochemical impedance spectroscopy testing costs, with a significant impact on fuel cell manufacturing and performance assessment processes. The procedure described in this paper relies on a former approach, already presented by the authors, which has been improved with respect to two main aspects. Firstly, non-dimensional parameters are computed, by referring to the Buckingham Pi theorem, through a generalized physical model. Secondly, single cell or short stack internal states (i.e., water content and limiting current) are evaluated through significant information gained from impedance measurements and inverse modelling. Stack impedance simulation is performed through proper internal states distribution assumptions. To prove the consistency and robustness of the proposed technique, experimental data taken from the literature and the European funded project HEALTH-CODE are used, with stack impedance accurately reproduced by means of only a single cell spectrum.

The paper illustrates the design and verification of a model-based diagnostic algorithm for fault detection and isolation of a Diesel Particulate Filter (DPF). The algorithm relies on the Structural Analysis theory, which identifies proper fault indicators (i.e., residuals) upon the knowledge of the mathematical relationships within the system model. The main innovation of the proposed work consists in applying for the first time such theory to a Diesel after-treatment system. To check theoretical fault detectability and isolability, the approach requires only qualitative correlations among the physical variables and parameters representative of the faults that may occur into the system, without the need for quantitative information. Nevertheless, to build the residuals, the form of the equations needs to be defined and properly implemented. The mathematical model developed in this work is a lumped dynamic model that accounts for two main control volumes, one related to the Diesel Oxidation Catalyst (DOC) and the other to the DPF. Mass and energy balance equations are introduced in both volumes. Within the DPF, soot inlet flow is related to the engine exhaust flow, and its oxidation kinetics is modelled by means of Arrhenius-type equation. Four faulty states are introduced into this study with respect to: i) variation in fuel-post injection amount (i.e., injector malfunction), ii) partial or missed fuel combustion in the DOC, iii) partial or missed residual fuel combustion in the DPF, and iv) degradation of the substrate viscosity. Each faulty state is modelled through a dedicated parameter and the overall model is then used to perform faults isolability through Structural Analysis. The theoretical results show that the proposed diagnostic algorithm can detect and correctly isolate all the considered faults. Then, residuals are built and verified in simulated environment on specific cases, proving the capability of the algorithm to successfully detect and isolate the considered faults.