The regularization theory has successfully enabled the removal of gravitational singularities associated with celestial bodies.In this study,regularizing techniques are merged into a multi-impulse trajectory design fr...The regularization theory has successfully enabled the removal of gravitational singularities associated with celestial bodies.In this study,regularizing techniques are merged into a multi-impulse trajectory design framework that requires delicate computations,particularly for a fuel minimization problem.Regularized variables based on the Levi–Civita or Kustaanheimo–Stiefel transformations express instantaneous velocity changes in a gradient-based direct optimization method.The formulation removes the adverse singularities associated with the null thrust impulses from the derivatives of an objective function in the fuel minimization problem.The favorite singularity-free property enables the accurate reduction of unnecessary impulses and the generation of necessary impulses for local optimal solutions in an automatic manner.Examples of fuel-optimal multi-impulse trajectories are presented,including novel transfer solutions between a near-rectilinear halo orbit and a distant retrograde orbit.展开更多
Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid...Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.展开更多
Today,much information from traffic infrastructures and sensors of ego vehicle is available.Using such information has a potential for internal combustion engine vehicle to reduce fuel consumption in real world.In thi...Today,much information from traffic infrastructures and sensors of ego vehicle is available.Using such information has a potential for internal combustion engine vehicle to reduce fuel consumption in real world.In this paper,a powertrain controller for a hybrid electric vehicle aiming to reduce fuel consumption is introduced,which uses information from traffic signals,the global positioning system and sensors,and the preceding vehicle.This study was carried out as a benchmark problem of engine and powertrain control simulation and modeling 2021(E-COSM 2021).The developed controller firstly decides reference acceleration of the ego vehicle using the traffic signal and the position information and the preceding vehicle speed.The acceleration and deceleration leading to increase in unnecessary fuel consumption is avoided.Next,the reference engine,generator,and motor torques are decided to achieve the reference acceleration and minimize fuel consumption.In addition,the reference engine,generator and motor torques were decided by the given fuel consumption map for the engine,and by the virtual fuel consumption maps for the generator and the motor.The virtual fuel consumption is derived from the efficiency maps of the generator and the motor using a given equivalent factor,which converts electricity consumption to fuel for the generator and the motor.In this study,a controller was designed through the benchmark problem of E-COSM 2021 for minimizing total fuel consumption of the engine,the generator,and the motor.The developed controller was evaluated in driving simulations.The result shows that operating the powertrain in efficient area is a key factor in reducing total fuel consumption.展开更多
文摘The regularization theory has successfully enabled the removal of gravitational singularities associated with celestial bodies.In this study,regularizing techniques are merged into a multi-impulse trajectory design framework that requires delicate computations,particularly for a fuel minimization problem.Regularized variables based on the Levi–Civita or Kustaanheimo–Stiefel transformations express instantaneous velocity changes in a gradient-based direct optimization method.The formulation removes the adverse singularities associated with the null thrust impulses from the derivatives of an objective function in the fuel minimization problem.The favorite singularity-free property enables the accurate reduction of unnecessary impulses and the generation of necessary impulses for local optimal solutions in an automatic manner.Examples of fuel-optimal multi-impulse trajectories are presented,including novel transfer solutions between a near-rectilinear halo orbit and a distant retrograde orbit.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444)The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR65.
文摘Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.
文摘Today,much information from traffic infrastructures and sensors of ego vehicle is available.Using such information has a potential for internal combustion engine vehicle to reduce fuel consumption in real world.In this paper,a powertrain controller for a hybrid electric vehicle aiming to reduce fuel consumption is introduced,which uses information from traffic signals,the global positioning system and sensors,and the preceding vehicle.This study was carried out as a benchmark problem of engine and powertrain control simulation and modeling 2021(E-COSM 2021).The developed controller firstly decides reference acceleration of the ego vehicle using the traffic signal and the position information and the preceding vehicle speed.The acceleration and deceleration leading to increase in unnecessary fuel consumption is avoided.Next,the reference engine,generator,and motor torques are decided to achieve the reference acceleration and minimize fuel consumption.In addition,the reference engine,generator and motor torques were decided by the given fuel consumption map for the engine,and by the virtual fuel consumption maps for the generator and the motor.The virtual fuel consumption is derived from the efficiency maps of the generator and the motor using a given equivalent factor,which converts electricity consumption to fuel for the generator and the motor.In this study,a controller was designed through the benchmark problem of E-COSM 2021 for minimizing total fuel consumption of the engine,the generator,and the motor.The developed controller was evaluated in driving simulations.The result shows that operating the powertrain in efficient area is a key factor in reducing total fuel consumption.