Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
Nowadays, automotive engine compensation control is\ncarried out electronically by utilizing many compensation\nmaps in engine management systems; such that the engine\ncan sustain its performance under the variations in engine\noperating conditions and environmental parameters. In\ntraditional engine compensation map calibration, the\nparameters are normally set by a trial and error method\nbecause the exact mathematical engine model has not been\nderived. In this paper, a new framework, namely multiinput/\noutput least-squares support vector committee\nmachine, is proposed to construct the engine compensation\ncontrol system (ECCS) models based on experimental data.\nAs the number of adjustable parameters involved in the\nECCS is very huge, the model accuracy and training time are\nusually degraded. Nonlinear regression is therefore\nemployed to perform dimension reduction before modelling.\nThe ECCS models are then embedded in an objective\nfunction for parameter optimization. Two widely-used\nevolutionary optimization algorithms, Genetic algorithm\n(GA) and particle swarm optimization (PSO), are applied to\nthe objective function to determine the optimal calibration\nmaps automatically. Experimental results show that the\nproposed modelling and optimization framework is effective\nand PSO is superior to the GA in compensation map\ncalibration....
In this paper, the engine output torque model and fuel\nconsumption model are established on the basis of engine\ntest results. In particular, it focuses on setting up the\noptimization model where the objective function is\ncomposed of driving loss rate and efficiency loss rate with\neach weighting genes, highlighting the introduction of the\ngenes which vary as transmission gears with different needs\nfor the performance of power and fuel economy. The model\nresults in a optimal matching which gives a good\ncompromise to power performance and fuel economy,\ncatering to the very bus actually and general kinds of\nworking conditions. The optimization programs are written,\ndealing with constraint problems based on the penalty\nstrategy in improved GA. The feasibility and validity of this\noptimization approach are assessed on a practical example,\nwhose optimization values of the transmission ratios and the\nfinal drive ratio are obtained and composite performances\nare improved....
The contactmechanics for a rigid wheel and deformable terrain are complicated owing to the rigid flexible coupling characteristics.\nBekker�s equations are used as the basis to establish the equations of the sinking rolling wheel, to vertical load pressure relationship.\nSince vehicle movement on the Moon is a complex and on-going problem, the researcher is poised to simplify this problem of\nvertical loading of the wheel. In this paper, the quarter kinetic models of a manned lunar rover, which are both based on the rigid\nroad and deformable lunar terrain, are used as the simulation models. With these kinetic models, the vibration simulations were\nconducted. The simulation results indicate that the quarter kinetic model based on the deformable lunar terrain accurately reflects\nthe deformable terrain�s influence on the vibration characteristics of a manned lunar rover. Additionally, with the quarter kinetic\nmodel of the deformable terrain, the vibration simulations of a manned lunar rover were conducted, which include a parametric\nanalysis of the wheel parameters, vehicle speed, and suspension parameters. The results show that a manned lunar rover requires a\nlower damping value and stiffness to achieve better vibration performance....
This paper investigates the GENSIS air spring suspension system equivalence to a passive suspension system. The SIMULINK\nsimulation together with the OptiY optimization is used to obtain the air spring suspension model equivalent to passive suspension\nsystem, where the car body response difference from both systems with the same road profile inputs is used as the objective function\nfor optimization (OptiY program). The parameters of air spring system such as initial pressure, volume of bag, length of surge pipe,\ndiameter of surge pipe, and volume of reservoir are obtained from optimization. The simulation results show that the air spring\nsuspension equivalent system can produce responses very close to the passive suspension system....
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