Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines.Specifically,using three-way catalytic converter involves strict control of the ...Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines.Specifically,using three-way catalytic converter involves strict control of the air/fuel ratio around the stoichiometric point and hence requires an accurate model for air charge estimation.However,high degrees of complexity and nonlinearity of the gas flow in the internal combustion engine make air charge estimation a challenging task.This is more obvious in engines with variable valve timing systems in which gas flow is more complex and depends on more functional variables.This results in models that are either quite empirical(such as look-up tables),not having interpretability and extrapolation capability,or physically based models which are not appropriate for onboard applications.Solving these problems,a novel semi-empirical model was proposed in this work which only needed engine speed,load,and valves timings for volumetric efficiency prediction.The accuracy and generalizability of the model is shown by its test on numerical and experimental data from three distinct engines.Normalized test errors are 0.0316,0.0152 and 0.24 for the three engines,respectively.Also the performance and complexity of the model were compared with neural networks as typical black box models.While the complexity of the model is less than half of the complexity of neural networks,and its computational cost is approximately 0.12 of that of neural networks and its prediction capability in the considered case studies is usually more.These results show the superiority of the proposed model over conventional black box models such as neural networks in terms of accuracy,generalizability and computational cost.展开更多
Automobiles evolved from primarily mechanical to electro-mechanical,or mechatronic,vehicles.For example,carburetors have been replaced by fuel injection and air-fuel ratio control,leading to order of magnitude improve...Automobiles evolved from primarily mechanical to electro-mechanical,or mechatronic,vehicles.For example,carburetors have been replaced by fuel injection and air-fuel ratio control,leading to order of magnitude improvements in fuel economy and emissions.Mechatronic systems are pervasive in modem automobiles and represent a synergistic integration of mechanics,electronics and computer science.They are smart systems,whose design is more challenging than the separate design of their mechanical,electronic and computer/control components.In this review paper,two recent methods for the design of mechatronic components are summarized and their applications to problems in automotive control are highlighted.First,the combined design,or co-design,of a smart artifact and its controller is considered.It is shown that the combined design of an artifact and its controller can lead to improved performance compared to sequential design.The coupling between the artifact and controller design problems is quantified,and methods for co-design are presented.The control proxy function method,which provides ease of design as in the sequential approach and approximates the performance of the co-design approach,is highlighted with application to the design of a passive/active automotive suspension.Second,the design for component swapping modularity(CSM)of a distributed controller for a smart product is discussed.CSM is realized by employing distributed controllers residing in networked smart components,with bidirectional communication over the network.Approaches to CSM design are presented,as well as applications of the method to a variable-cam-timing engine,and to enable battery swapping in a plug-in hybrid electric vehicle.展开更多
文摘Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines.Specifically,using three-way catalytic converter involves strict control of the air/fuel ratio around the stoichiometric point and hence requires an accurate model for air charge estimation.However,high degrees of complexity and nonlinearity of the gas flow in the internal combustion engine make air charge estimation a challenging task.This is more obvious in engines with variable valve timing systems in which gas flow is more complex and depends on more functional variables.This results in models that are either quite empirical(such as look-up tables),not having interpretability and extrapolation capability,or physically based models which are not appropriate for onboard applications.Solving these problems,a novel semi-empirical model was proposed in this work which only needed engine speed,load,and valves timings for volumetric efficiency prediction.The accuracy and generalizability of the model is shown by its test on numerical and experimental data from three distinct engines.Normalized test errors are 0.0316,0.0152 and 0.24 for the three engines,respectively.Also the performance and complexity of the model were compared with neural networks as typical black box models.While the complexity of the model is less than half of the complexity of neural networks,and its computational cost is approximately 0.12 of that of neural networks and its prediction capability in the considered case studies is usually more.These results show the superiority of the proposed model over conventional black box models such as neural networks in terms of accuracy,generalizability and computational cost.
基金the National Science Foundation,the U.S.A.Army Automotive Research Center,the Ford Motor Company,and United Technologies,Inc.
文摘Automobiles evolved from primarily mechanical to electro-mechanical,or mechatronic,vehicles.For example,carburetors have been replaced by fuel injection and air-fuel ratio control,leading to order of magnitude improvements in fuel economy and emissions.Mechatronic systems are pervasive in modem automobiles and represent a synergistic integration of mechanics,electronics and computer science.They are smart systems,whose design is more challenging than the separate design of their mechanical,electronic and computer/control components.In this review paper,two recent methods for the design of mechatronic components are summarized and their applications to problems in automotive control are highlighted.First,the combined design,or co-design,of a smart artifact and its controller is considered.It is shown that the combined design of an artifact and its controller can lead to improved performance compared to sequential design.The coupling between the artifact and controller design problems is quantified,and methods for co-design are presented.The control proxy function method,which provides ease of design as in the sequential approach and approximates the performance of the co-design approach,is highlighted with application to the design of a passive/active automotive suspension.Second,the design for component swapping modularity(CSM)of a distributed controller for a smart product is discussed.CSM is realized by employing distributed controllers residing in networked smart components,with bidirectional communication over the network.Approaches to CSM design are presented,as well as applications of the method to a variable-cam-timing engine,and to enable battery swapping in a plug-in hybrid electric vehicle.
