A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).Th...A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).The results revealed that Ho_(2)CoMnO_(6) crystallizes in a monoclinic structure with the P2_(1)/n space group.In contrast,the other compounds HoRCoMnO_(6)(R=Gd,Eu,or Nd) exhibit an orthorhombic structure with the Pnma space group.As a result,the average crystallite size also changes as a function of rare-earth element doping.This investigation reveals that the magnetic properties of the compounds studied are significantly dependent on the doping elements.The Curie temperature T_C,for example,increases from 80 to 118℃ with the ionic radii of rare earths increasing.Furthermore,the study of the magnetocaloric effect(MCE) shows that the maximum of the entropy variation(-ΔS_(M)^(max)) increases from 4.97 to 6.06 J/(kg·K) under a magnetic field of 5 T with substitution by rare-earth ions.To examine the efficiency of MCE materials,the relative cooling power(RCP) was evaluated and is found to increase with increment of rare-earth radius till 406.69 J/kg for Nd.The mean entropy variation with tempe rature(TEC) was also studied.Due to their significant magnetocaloric performance,HoRCoMnO_(6)(noted as HRCMO) compounds(with R=Ho,Gd,Eu or Nd) could be good candidates for low-temperature magnetic cooling applications.展开更多
With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy e...With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.展开更多
Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind e...Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.展开更多
The measurement of solar irradiation is still a necessary basis for planning the installation of photovoltaic parks and concentrating solar power systems. The meteorological stations for the measurement of the solar f...The measurement of solar irradiation is still a necessary basis for planning the installation of photovoltaic parks and concentrating solar power systems. The meteorological stations for the measurement of the solar flux at any point of the earth’s surface are still insufficient worldwide;moreover, these measurements on the ground are expensive, and rare. To overcome this shortcoming, the exploitation of images from the European meteorological satellites of the second generation MSG is a reliable solution to estimate the global horizontal irradiance GHI on the ground with a good spatial and temporal coverage. Since 2004, the new generation MSG satellites provide images of Africa and Europe every 15 minutes with a spatial resolution of about 1 km × 1 km at the sub-satellite point. The objective of this work was to apply the Brazil-SR method to evaluate the global horizontal GHI irradiance for the entire Moroccan national territory from the European Meteosat Second Generation MSG satellite images. This bibliographic review also exposed the standard model of calculation of GHI in clear sky by exploiting the terrestrial meteorological measurements.展开更多
Controlling marine pollution caused by hydrocarbons spilling from oil tanker accidents and oil rigs is urgently needed.Conventional pollution control vessels currently in service worldwide do not meet certain safety c...Controlling marine pollution caused by hydrocarbons spilling from oil tanker accidents and oil rigs is urgently needed.Conventional pollution control vessels currently in service worldwide do not meet certain safety criteria,storage capacities,and response times owing to their technical shortcomings.This study proposes a new concept of multimission and autonomous antipollution vessels capable of acting quickly and efficiently to counter such pollution threats.The objective of this study is to carry out a total and rapid recovery of the spilled oil slick in complete safety.Hence,optimizing the bulbous bow adapted to the pollution control vessel during its displacement is necessary to horizontally straighten the accompanying waves formed around the hull and to laminate the flow upstream of the side openings for the recovery of spilled oil.This optimization improves the nautical qualities specific to this ship to reduce the total resistance to progress and to standardize the flow upstream of the side openings to allow the collection of spilled oil at high speed.This optimization study can open a field of application for the construction of modern multi-mission pollution control vessels.Tests in hull basins will be planned to validate and adjust the results obtained from the simulations.展开更多
A large part of the energy savings in the building sector comes from the choice of materials used and their structures. We are interested, through a numerical study, in establishing the link between the thermal perfor...A large part of the energy savings in the building sector comes from the choice of materials used and their structures. We are interested, through a numerical study, in establishing the link between the thermal performance of composite materials and their microstructures. The work begins with the generation of a two-phase 3D composite structure, the application of the Random Sequential Addition (RSA) algorithm, and then the finite element method (FE) is used to evaluate, in steady-state, the effective thermal conductivity of these composites. The result of the effective thermal conductivity of composite building material based on clay and olive waste at a volume fraction of 10% obtained by simulation is 0.573 W·m<sup>?1</sup>·K<sup>?1</sup>, this result differs by 3.6% from the value measured experimentally using modern metrology methods. The calculated value is also compared to those of existing analytical models in the literature. It can be noticed also that the effective thermal conductivity is not only related to the volume fraction of the inclusions but also to other parameters such as the shape of the inclusions and their distribution. The small difference between the numerical and experimental thermal conductivity results shows the performance of the code used and its validation for random heterogeneous materials. The homogenization technique remains a reliable way of evaluating the effective thermal properties of clay-based building materials and exploring new composite material designs.展开更多
The successful manufacture of thick composites is challenging since the highly exothermic nature of thermoset resins and limited temperature control make avoiding the onset of detrimental thermal gradients within the ...The successful manufacture of thick composites is challenging since the highly exothermic nature of thermoset resins and limited temperature control make avoiding the onset of detrimental thermal gradients within the composite relatively difficult.This phenomenon is mainly caused by exothermic heat reactions.The so-called Michaud's model has been largely used in the literature to reduce the gap between experience and simulation with regard to the effective prediction of the temperature cycle in these processes.In this work,another solution is proposed to simulate the curing process for thick composites,namely preheating the resin to activate the curing reaction before resin injection into the mold.A good agreement between the experiment and the simulation is found.Moreover,in order to minimize the thermal gradient in the final composite,the thermophysical properties of the fiber and the torque(temperature,time)of the Plate have been varied leading to interesting results.展开更多
Most of the energy savings in the building sector come from the choice of the materials used and their microphysical properties.In the present study,through numerical simulations a link is established between the ther...Most of the energy savings in the building sector come from the choice of the materials used and their microphysical properties.In the present study,through numerical simulations a link is established between the thermal performance of composite materials and their microstructures.First,a two-phase 3D composite structure is modeled,then the RSA(Random Sequential Addition)algorithm and a finite element method(FE)are applied to evaluate the effective thermal conductivity of these composites in the steady-state.In particular,building composites based on gypsum and clay,consolidated with peanut shell additives and/or cork are considered.The numerically determined thermal conductivities are compared with values experimentally calculated using the typical tools of modern metrology,and with available analytical models.The calculated thermal conductivities of the clay-based materials are 0.453 and 0.301 W.m^(−1).K^(−1) with peanut shells and cork,respectively.Those of the gypsum-based materials are 0.245 and 0.165 W.m^(−1).K^(−1) with peanut shells and cork,respectively.It is shown that,in addition to its dependence on the volume fraction of inclusions,the effective thermal conductivity is also influenced by other parameters such as the shape of inclusions and their distribution.The relative deviations,on average,do not exceed 6.8%,which provides evidence for the reliability of the used approach for random heterogeneous materials.展开更多
The switched reluctance motor is appreciated in variable speed drives through his simplicity of design, his low cost, his high reliability and high ability to regulate which allow him to compete with the other common ...The switched reluctance motor is appreciated in variable speed drives through his simplicity of design, his low cost, his high reliability and high ability to regulate which allow him to compete with the other common types of electric drives. The mathematical modelling of switched reluctance motors (SRM) allows his study in the areas of research and conception. In this paper, we present the developing of a methodology using the combined model of switched reluctance motor drive bases in standard equations of electrical machines with the field theory analysis of magnetic circuit. Then, the elaboration of mathematical models for switching reluctance motor (SRM) proposes his application based on a similarity with series DC motor to simplify the conception of control of electrical drives systems [1] with Closed Loop Control.展开更多
This paper proposes an advanced method for estimating numerous parameters in a wind-energy-conversion system with high precision,especially in a transient state,including the rotation speed and mechanical torque of th...This paper proposes an advanced method for estimating numerous parameters in a wind-energy-conversion system with high precision,especially in a transient state,including the rotation speed and mechanical torque of the turbine as well as wind velocity.The suggested approach is designed into two parts.First,a fourth-order Luenberger observer is proposed to take into account the significant fluctuations of the mechanical torque that can be caused by wind gusts.This observer provides an accurate estimate of speed and mechanical torque in all weather conditions and especially when the wind is gusting.At the same time,the wind velocity is calculated using the Luenberger observer outputs and a model of the mechanical power generated by the turbine.Second,these estimated parameters are exploited as input in a maximum-power-point tracking(MPPT)algorithm using the tip-speed ratio(TSR)to improve the sensorless strategy control.Simulation results were performed using MATLAB®/Simulink®for both wind gust and real wind profiles.We have verified that for wind gusts with jumps ranging from 3 to 7 m/s,the new observer manages to better follow the rotation speed and the torque of the turbine compared to a usual observer.In addition,we demonstrated that by applying the proposed estimator in the improved TSR-MPPT strategy,it is possible to extract 3.3%more energy compared to traditional approaches.展开更多
This paper develops an adaptive neural network(NN)observer for proton-exchange membrane fuel cells(PEMFCs).Indeed,information on the oxygen excess ratio(OER)value is crucial to ensure optimal management of the durabil...This paper develops an adaptive neural network(NN)observer for proton-exchange membrane fuel cells(PEMFCs).Indeed,information on the oxygen excess ratio(OER)value is crucial to ensure optimal management of the durability and reliability of the PEMFC.The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode.Unfortunately,the measurement process of both these masses is difficult and costly.To solve this problem,the design of a PEMFC state observer is attractive.However,the behaviour of the fuel cell system is highly non-linear and its modelling is complex.Due to this constraint,a multilayer perceptron neural network(MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses.One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN.Indeed,the weights of the NN are updated in real time using the output error.In addition,the observer parameters,namely the learning rate and the damping factor,are online adapted using the optimization tools of extremum seeking.Moreover,the proposed observer stability analysis is performed using the Lyapunov theory.The observer performances are validated by simulation under MATLAB®/Simulink®.The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer(HGO).The mean rela-tive error value of the excess oxygen rate is considered the performance index,which is equal to 1.01%for an adaptive MLPNN and 3.95%and 9.95%for a fixed MLPNN and HGO,respectively.Finally,a robustness test of the proposed observer with respect to measurement noise is performed.展开更多
Designing high-gain observers(HGOs)for the state estimation of an electric vehicle’s electrohydraulic brake(EHB)system is challenging.This type of observer is applicable to model nonlinearities and constant feature g...Designing high-gain observers(HGOs)for the state estimation of an electric vehicle’s electrohydraulic brake(EHB)system is challenging.This type of observer is applicable to model nonlinearities and constant feature gains.However,they are very sensitive to measurement noise,which is unavoidable in EHB.The first novelty of this study is that it compensates for the measurement noise using a filtered high-gain observer(FHGO)to ensure EHB state estimation.The proposed FHGO provides an estimate of the master cylinder pressure,motor current,and rotor speed from measurements of the rotor position.The second novelty is the design of an extremum-seeking(ES)optimization loop to adjust the FHGO gains online.The performance of the developed FHGO with ES-based online gain optimization was highlighted in the presence of model uncertainties and output measurement noise using a Matlab/Simulink simulation.The superiority of the FHGO(even with a fixed gain)over a standard high gain observer(SHGO)was also demonstrated.展开更多
文摘A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).The results revealed that Ho_(2)CoMnO_(6) crystallizes in a monoclinic structure with the P2_(1)/n space group.In contrast,the other compounds HoRCoMnO_(6)(R=Gd,Eu,or Nd) exhibit an orthorhombic structure with the Pnma space group.As a result,the average crystallite size also changes as a function of rare-earth element doping.This investigation reveals that the magnetic properties of the compounds studied are significantly dependent on the doping elements.The Curie temperature T_C,for example,increases from 80 to 118℃ with the ionic radii of rare earths increasing.Furthermore,the study of the magnetocaloric effect(MCE) shows that the maximum of the entropy variation(-ΔS_(M)^(max)) increases from 4.97 to 6.06 J/(kg·K) under a magnetic field of 5 T with substitution by rare-earth ions.To examine the efficiency of MCE materials,the relative cooling power(RCP) was evaluated and is found to increase with increment of rare-earth radius till 406.69 J/kg for Nd.The mean entropy variation with tempe rature(TEC) was also studied.Due to their significant magnetocaloric performance,HoRCoMnO_(6)(noted as HRCMO) compounds(with R=Ho,Gd,Eu or Nd) could be good candidates for low-temperature magnetic cooling applications.
文摘With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.
文摘Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.
文摘The measurement of solar irradiation is still a necessary basis for planning the installation of photovoltaic parks and concentrating solar power systems. The meteorological stations for the measurement of the solar flux at any point of the earth’s surface are still insufficient worldwide;moreover, these measurements on the ground are expensive, and rare. To overcome this shortcoming, the exploitation of images from the European meteorological satellites of the second generation MSG is a reliable solution to estimate the global horizontal irradiance GHI on the ground with a good spatial and temporal coverage. Since 2004, the new generation MSG satellites provide images of Africa and Europe every 15 minutes with a spatial resolution of about 1 km × 1 km at the sub-satellite point. The objective of this work was to apply the Brazil-SR method to evaluate the global horizontal GHI irradiance for the entire Moroccan national territory from the European Meteosat Second Generation MSG satellite images. This bibliographic review also exposed the standard model of calculation of GHI in clear sky by exploiting the terrestrial meteorological measurements.
文摘Controlling marine pollution caused by hydrocarbons spilling from oil tanker accidents and oil rigs is urgently needed.Conventional pollution control vessels currently in service worldwide do not meet certain safety criteria,storage capacities,and response times owing to their technical shortcomings.This study proposes a new concept of multimission and autonomous antipollution vessels capable of acting quickly and efficiently to counter such pollution threats.The objective of this study is to carry out a total and rapid recovery of the spilled oil slick in complete safety.Hence,optimizing the bulbous bow adapted to the pollution control vessel during its displacement is necessary to horizontally straighten the accompanying waves formed around the hull and to laminate the flow upstream of the side openings for the recovery of spilled oil.This optimization improves the nautical qualities specific to this ship to reduce the total resistance to progress and to standardize the flow upstream of the side openings to allow the collection of spilled oil at high speed.This optimization study can open a field of application for the construction of modern multi-mission pollution control vessels.Tests in hull basins will be planned to validate and adjust the results obtained from the simulations.
文摘A large part of the energy savings in the building sector comes from the choice of materials used and their structures. We are interested, through a numerical study, in establishing the link between the thermal performance of composite materials and their microstructures. The work begins with the generation of a two-phase 3D composite structure, the application of the Random Sequential Addition (RSA) algorithm, and then the finite element method (FE) is used to evaluate, in steady-state, the effective thermal conductivity of these composites. The result of the effective thermal conductivity of composite building material based on clay and olive waste at a volume fraction of 10% obtained by simulation is 0.573 W·m<sup>?1</sup>·K<sup>?1</sup>, this result differs by 3.6% from the value measured experimentally using modern metrology methods. The calculated value is also compared to those of existing analytical models in the literature. It can be noticed also that the effective thermal conductivity is not only related to the volume fraction of the inclusions but also to other parameters such as the shape of the inclusions and their distribution. The small difference between the numerical and experimental thermal conductivity results shows the performance of the code used and its validation for random heterogeneous materials. The homogenization technique remains a reliable way of evaluating the effective thermal properties of clay-based building materials and exploring new composite material designs.
文摘The successful manufacture of thick composites is challenging since the highly exothermic nature of thermoset resins and limited temperature control make avoiding the onset of detrimental thermal gradients within the composite relatively difficult.This phenomenon is mainly caused by exothermic heat reactions.The so-called Michaud's model has been largely used in the literature to reduce the gap between experience and simulation with regard to the effective prediction of the temperature cycle in these processes.In this work,another solution is proposed to simulate the curing process for thick composites,namely preheating the resin to activate the curing reaction before resin injection into the mold.A good agreement between the experiment and the simulation is found.Moreover,in order to minimize the thermal gradient in the final composite,the thermophysical properties of the fiber and the torque(temperature,time)of the Plate have been varied leading to interesting results.
文摘Most of the energy savings in the building sector come from the choice of the materials used and their microphysical properties.In the present study,through numerical simulations a link is established between the thermal performance of composite materials and their microstructures.First,a two-phase 3D composite structure is modeled,then the RSA(Random Sequential Addition)algorithm and a finite element method(FE)are applied to evaluate the effective thermal conductivity of these composites in the steady-state.In particular,building composites based on gypsum and clay,consolidated with peanut shell additives and/or cork are considered.The numerically determined thermal conductivities are compared with values experimentally calculated using the typical tools of modern metrology,and with available analytical models.The calculated thermal conductivities of the clay-based materials are 0.453 and 0.301 W.m^(−1).K^(−1) with peanut shells and cork,respectively.Those of the gypsum-based materials are 0.245 and 0.165 W.m^(−1).K^(−1) with peanut shells and cork,respectively.It is shown that,in addition to its dependence on the volume fraction of inclusions,the effective thermal conductivity is also influenced by other parameters such as the shape of inclusions and their distribution.The relative deviations,on average,do not exceed 6.8%,which provides evidence for the reliability of the used approach for random heterogeneous materials.
文摘The switched reluctance motor is appreciated in variable speed drives through his simplicity of design, his low cost, his high reliability and high ability to regulate which allow him to compete with the other common types of electric drives. The mathematical modelling of switched reluctance motors (SRM) allows his study in the areas of research and conception. In this paper, we present the developing of a methodology using the combined model of switched reluctance motor drive bases in standard equations of electrical machines with the field theory analysis of magnetic circuit. Then, the elaboration of mathematical models for switching reluctance motor (SRM) proposes his application based on a similarity with series DC motor to simplify the conception of control of electrical drives systems [1] with Closed Loop Control.
基金co-financed by the Interreg Atlantic Area Program through the European Regional Development Fund and the PORTOS project.
文摘This paper proposes an advanced method for estimating numerous parameters in a wind-energy-conversion system with high precision,especially in a transient state,including the rotation speed and mechanical torque of the turbine as well as wind velocity.The suggested approach is designed into two parts.First,a fourth-order Luenberger observer is proposed to take into account the significant fluctuations of the mechanical torque that can be caused by wind gusts.This observer provides an accurate estimate of speed and mechanical torque in all weather conditions and especially when the wind is gusting.At the same time,the wind velocity is calculated using the Luenberger observer outputs and a model of the mechanical power generated by the turbine.Second,these estimated parameters are exploited as input in a maximum-power-point tracking(MPPT)algorithm using the tip-speed ratio(TSR)to improve the sensorless strategy control.Simulation results were performed using MATLAB®/Simulink®for both wind gust and real wind profiles.We have verified that for wind gusts with jumps ranging from 3 to 7 m/s,the new observer manages to better follow the rotation speed and the torque of the turbine compared to a usual observer.In addition,we demonstrated that by applying the proposed estimator in the improved TSR-MPPT strategy,it is possible to extract 3.3%more energy compared to traditional approaches.
基金supported by the Ministry of Higher Education,Scientific Research and Innovation,the Digital Development Agency and the CNRST of Morocco(Alkhawarizmi/2020/39).
文摘This paper develops an adaptive neural network(NN)observer for proton-exchange membrane fuel cells(PEMFCs).Indeed,information on the oxygen excess ratio(OER)value is crucial to ensure optimal management of the durability and reliability of the PEMFC.The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode.Unfortunately,the measurement process of both these masses is difficult and costly.To solve this problem,the design of a PEMFC state observer is attractive.However,the behaviour of the fuel cell system is highly non-linear and its modelling is complex.Due to this constraint,a multilayer perceptron neural network(MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses.One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN.Indeed,the weights of the NN are updated in real time using the output error.In addition,the observer parameters,namely the learning rate and the damping factor,are online adapted using the optimization tools of extremum seeking.Moreover,the proposed observer stability analysis is performed using the Lyapunov theory.The observer performances are validated by simulation under MATLAB®/Simulink®.The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer(HGO).The mean rela-tive error value of the excess oxygen rate is considered the performance index,which is equal to 1.01%for an adaptive MLPNN and 3.95%and 9.95%for a fixed MLPNN and HGO,respectively.Finally,a robustness test of the proposed observer with respect to measurement noise is performed.
文摘Designing high-gain observers(HGOs)for the state estimation of an electric vehicle’s electrohydraulic brake(EHB)system is challenging.This type of observer is applicable to model nonlinearities and constant feature gains.However,they are very sensitive to measurement noise,which is unavoidable in EHB.The first novelty of this study is that it compensates for the measurement noise using a filtered high-gain observer(FHGO)to ensure EHB state estimation.The proposed FHGO provides an estimate of the master cylinder pressure,motor current,and rotor speed from measurements of the rotor position.The second novelty is the design of an extremum-seeking(ES)optimization loop to adjust the FHGO gains online.The performance of the developed FHGO with ES-based online gain optimization was highlighted in the presence of model uncertainties and output measurement noise using a Matlab/Simulink simulation.The superiority of the FHGO(even with a fixed gain)over a standard high gain observer(SHGO)was also demonstrated.
