control algorithm design for degradation mitigation and lifetime improvement of polymer electrolyte membrane fuel cells

Abstract 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

Development of a model-based diagnosis algorithm oriented towards solid oxide fuel cell systems: approach and application

2012 - 2013The present dissertation illustrates the complete procedure of developing a model-based diagnosis algorithm and shows its application to a pre-commercial Solid Oxide Fuel Cell (SOFC) system. The main motivations of this work can be found in the increasing demand for diagnostic techniques aimed at both ensuring system optimal performance and required lifetime. The purpose of a diagnostic algorithm is to detect and isolate undesired states (i.e. faults) within the system under study (e.g. both the stack and balance of plant – BOP – components of an SOFC system). The understanding of the main mechanisms inducing malfunctions or, in the worst case, abrupt interruptions (i.e. failures) of the system allows the definition of suitable control strategies to avoid these events and to ensure the required system performance. Among all the diagnostic techniques available in literature, a model-based fault diagnosis methodology is taken into account. According to this technique, a process model is exploited to treat the data measured during the system operation to obtain insightful indicators of the system state. More in details, the measured data are compared to simulated variables to extract features, i.e. mathematical residuals, which are representative of the monitored variables behavior. The residuals computation is performed during the monitoring process. The detection of undesired or unexpected system behaviors is carried out through the comparison of the collected residuals to reference threshold values. These values are suitably tuned to take into account several uncertainties, like model inaccuracy and measurement noise, and the necessity to detect incipient faults. The comparison of the computed residuals to these thresholds allows the generation of analytical symptoms, which indicate whether an undesired event is occurring or not. The arise of a symptom points out that the behavior of the related variable is abnormal, completing the detection process. At this stage, although the occurrence of a fault is observed, its type is still unknown. To accomplish this last task, a reference set of information is exploited for the identification of the malfunction nature and for the isolation of the faulty component(s) (isolation process). These information comprise the main faults the system can be affected by and the variables conditioned by the occurrence of these faults. The symptoms collected during the detection phase, which are representative of the variables showing an irregular (or unexpected) behavior, are compared to the reference information to correctly locate the fault on the system. The first part of this manuscript entails the design procedure of a generic model-based diagnosis algorithm, describing in detail the development of the mathematical model and the definition of the reference information required by the methodology. The presented model derives from an SOFC system model, developed by Sorrentino et al. [1][2]. This model is based on a lumped approach and is able to simulate both steady and dynamic behaviors of the system state variables. The stack is assumed planar and co-flow and its voltage behavior is represented by a non-linear regression, function of fuel utilization, current density, excess of air and the temperatures at the stack inlet and at the stack outlet. On one hand, the temperature regulation of the stack inlet flows is achieved by means of two by-pass valves, one at the anode side and one at the cathode side. On the other hand, the stack inner temperature control is fulfilled through a PI controller, which acts on the air blower power to regulate the inlet air flow. The novelty of the presented model consists in several submodels specifically developed to simulate the considered system both in normal and in faulty conditions. This feature allows the utilization of the model for the offline definition of the reference information exploited for the isolation process. The reference information help in the isolation of the undesired event(s) occurring in the system during its normal operation. This task can be achieved through the correct identification of the relationships among the symptoms, generated during the detection process, and the possible faults the system can cope with. In the present work, a Fault Signature Matrix (FSM) developed by Arsie et al. [3] following a Fault Tree Analysis (FTA), is considered as the basis for the development of the aforementioned reference information. This FSM is improved through the simulation of different kind of faults in order to understand both the direct and the indirect correlations among the faults and the system variables. Moreover, the real effects induced by the considered fault on the affected variables are defined in terms of quantitative drifts of the variables values from the normal condition. To achieve this task, the fault sub-models previously introduced are exploited to simulate the effects of several faults. For the purpose of this work, five different faults related to an SOFC system are simulated, that are i) an increase in the air blower mechanical losses, ii) an air leakage, iii) a temperature controller failure, iv) a pre-reformer heat exchange surface corrosion and v) an increase in cell ohmic resistance. Through the faults simulation, a set of residuals is collected and its comparison with different threshold levels highlights the quantitative relationships among the faults and the conditioned variables. In this way, it is possible to point out the difference between an FSM developed through a heuristic approach (i.e. the FTA), accounting only for the qualitative relationships among the faults and the symptoms, and the one developed considering also the system sensitivity to the faults magnitude. The second part of this thesis entails the characterization and the validation of the developed diagnostic algorithm on a pre-commercial micro-Combined Heat and Power (μ-CHP) SOFC system, the Galileo 1000N, manufactured by the Swiss company HEXIS AG. A dedicated experimental activity has been performed in order to induce controlled faulty states in the system. The further original feature of this work consists in the design of well-defined procedures to mimic faults on a real SOFC system. In some cases, the procedure involves only suitable maneuvers via software control system, whereas in other cases, specific hardware modifications are required. Before applying the developed diagnostic algorithm to the Galileo 1000N, an adaptation process is performed, in order to suit each part of the algorithm to the system under analysis. The need for a fast and handy model, which can be rapidly tuned by the algorithm user, led to the development of a maps-based model to simulate the system in normal conditions and to extract residuals. This model exploits the average values of the monitored variables through numerical maps, which are function of the operating condition set-point values. Additionally, the FSM improved off-line via faults simulation is further modified taking into account the number of variables practically monitorable and the system control strategies. Moreover, a statistical hypothesis test is implemented in order to evaluate the probability of false alarm and missed fault. These analysis is significant for the correct interpretation of the generated symptoms during the detection phase. Concerning the impact of the present research activity, the developed algorithm aims at improving both the performance and the lifetime of an SOFC system by its implementation into a comprehensive control strategy. In this way it is possible to associate to the diagnosis of the system status specific counteractions performed by the system controller. In this way, both the manufacturer and the final users can obtain significant advantages in terms of management costs reduction (i.e. maintenance and materials costs) and overall efficiency increase. To summarize, the main contributions and innovative features of this research activity are listed in the following: · the development of a diagnostic algorithm following a model-based approach; · the improvement of an FSM, based on an FTA, through the exploitation of fault models simulation to evaluate the sensitivity of the monitored variables to the faults magnitudes; · the implementation of a statistical hypothesis test for the evaluation of false alarm and miss detection probability; · the design of specific procedures and hardware modifications to mimic faults in a controlled way on a real SOFC system (i.e. the Galileo 1000N) for the diagnostic algorithm validation; · the offline and the online validation of the proposed algorithm implemented on-board and controlled through a graphic user interface. It is worth noting that the innovative features presented in this manuscript are a pioneering contribution in the available literature. Most of the results presented in this dissertation have been carried out within the framework of the European Project GENIUS (Generic diagnosis instrument for SOFC systems) and received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) for the Fuel Cell and Hydrogen Joint Technology Initiative under grant agreement N° 245128. [edited by author]XII n.s

Application of Buckingham π theorem for scaling-up oriented fast modelling of Proton Exchange Membrane Fuel Cell impedance

Abstract This work focuses on the development of a fast PEMFC impedance model, built starting from both physical and geometrical variables. Buckingham's π theorem is proposed to define non-dimensional parameters that allow suitably describing the relationships linking the physical variables involved in the process under-study to the fundamental dimensions. This approach is a useful solution for those problems, whose first principles-based models are not known, difficult to build or computationally unfeasible. The key contributions of the proposed similarity theory-based modelling approach are presented and discussed. The major advantage resides in its straightforward online applicability, thanks to very low computational burden, while preserving good level of accuracy. This makes the model suitable for several purposes, such as design, control, diagnostics, state of health monitoring and prognostics. Experimental data, collected in different operating conditions, have been analysed to demonstrate the capability of the model to reproduce PEMFC impedance at different loads and temperatures. This results in a reduction of the experimental effort for the FCS lab characterization. Moreover, it is highlighted the possibility to use the model with scaling-up purposes to reproduce the full stack impedance from single-cell one, thus supporting FC design and development from lab-to commercial system-scale

Generalized scaling-up approach based on Buckingham theorem for Polymer Electrolyte Membrane Fuel Cells impedance simulation

Abstract 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

investigation of the energy requirements for the on board generation of oxy hydrogen on vehicles

Abstract 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

Brain flexibility increases during the peri-ovulatory phase of the menstrual cycle

The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e neuronal avalanches). This study aimed to describe how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting state magnetoencephalography (MEG) scans, acquired women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of MC. We investigated the functional repertoire (number of ways in which large bursts of activity spread through the brain) and the region-specific influence on large-scale dynamics across MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configuration patterns, in T2 than in T1, in the beta frequency band was observed. No relationship between changes in brain dynamics and sex hormones was showed. Finally, we showed that, in the beta band, the left posterior cingulate gyrus and the right insula were more present in the functional repertoire in T2 than in T1, while the right pallidum was more present in T1 than in T2. In summary, we showed a hormone independent increase of brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change

The kinectome: A comprehensive kinematic map of human motion in health and disease

Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson’s disease, observing that the patients’ kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of framework

Topological changes of brain network during mindfulness meditation: an exploratory source level magnetoencephalographic study

We have previously evidenced that Mindfulness Meditation (MM) in experienced meditators (EMs) is associated with long-lasting topological changes in resting state condition. However, what occurs during the meditative phase is still debated. Utilizing magnetoencephalography (MEG), the present study is aimed at comparing the topological features of the brain network in a group of EMs (n = 26) during the meditative phase with those of individuals who had no previous experience of any type of meditation (NM group, n = 29). A wide range of topological changes in the EM group as compared to the NM group has been shown. Specifically, in EMs, we have observed increased betweenness centrality in delta, alpha, and beta bands in both cortical (left medial orbital cortex, left postcentral area, and right visual primary cortex) and subcortical (left caudate nucleus and thalamus) areas. Furthermore, the degree of beta band in parietal and occipital areas of EMs was increased too. Our exploratory study suggests that the MM can change the functional brain network and provides an explanatory hypothesis on the brain circuits characterizing the meditative process

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the “clinical fingerprint” to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King’s disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the “clinical fingerprint” was predictive of the ALSFRS-r (p = 0.0397; β = 32.8), the King’s (p = 0.0001; β = − 7.40), and the MiToS (p = 0.0025; β = − 4.9) scores. Accordingly, it negatively correlated with the King’s (Spearman’s rho = -0.6041, p = 0.0003) and MiToS scales (Spearman’s rho = − 0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management

The Metabolomic Profile in Amyotrophic Lateral Sclerosis Changes According to the Progression of the Disease: An Exploratory Study

Amyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative pathology of the upper or lower motor neuron. Evaluation of ALS progression is based on clinical outcomes considering the impairment of body sites. ALS has been extensively investigated in the pathogenetic mechanisms and the clinical profile; however, no molecular biomarkers are used as diagnostic criteria to establish the ALS pathological staging. Using the source-reconstructed magnetoencephalography (MEG) approach, we demonstrated that global brain hyperconnectivity is associated with early and advanced clinical ALS stages. Using nuclear magnetic resonance (1H-NMR) and high resolution mass spectrometry (HRMS) spectroscopy, here we studied the metabolomic profile of ALS patients’ sera characterized by different stages of disease progression—namely early and advanced. Multivariate statistical analysis of the data integrated with the network analysis indicates that metabolites related to energy deficit, abnormal concentrations of neurotoxic metabolites and metabolites related to neurotransmitter production are pathognomonic of ALS in the advanced stage. Furthermore, analysis of the lipidomic profile indicates that advanced ALS patients report significant alteration of phosphocholine (PCs), lysophosphatidylcholine (LPCs), and sphingomyelin (SMs) metabolism, consistent with the exigency of lipid remodeling to repair advanced neuronal degeneration and inflammatio