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In this paper,the flywheel is considered to be linear elastic composition in accordance with its operating characteristic, mechanical and mathematical model are presented,the finite element method is employed to carry out the numerical analysis of steady and unsteady stresses within the high speed flywheel in the regenerative braking system of a city bus,and these provide the basis foŕ its structural modification and further rational design.

Although Hydrostatic Transmission System (HTS) had been used in many places, such as machine tools, agriculture machinery, construction machinery, and vehicles, it had not been used in good performance. Twenty years ago many people began to design new hydrostatic transmission with higher efficiency. Hydrostatic Energy Storage Transmission System (HESTS) is one of new hydrostatic transmission system with higher efficiency. HESTS is more fit for being used in vehicle that is always running in undulating ground or starting and braking frequently. Construction of vehicular HESTS was analyzed, mathematical model of vehicular HESTS was established. The needed control strategies of vehicular HESTS were analyzed because there are many variables would be controlled in the new transmission system.

In this paper, the intelligent control theory and method using neural networks are presented, the multilayer feedforward networks employed, the back propagation learning algorithm proposed. Finally the angular velocity control result of the hydrostatic drive system of secondary regulation with such type of control is provided. Relative studies revealed that the intelligent control for the system using neural networks can be effective.

The cruise control as one of promising vehicle active control has been receiving a considerable attention in recent years, so the related problems are discussed in this paper. Mathematical model of the engine under the steady state and general dynamics equation of the Continuously Variable Transmission(CVT) are given, the regulating principle of the operating state of the engine is analyzed, and the conjugate gradient method employed to solve the optimal control problem of the vehicle in the cruise control is described, so the operating state of the engine could be controlled. At end of paper, some results are provided to verify the approach based on the vehicular powertrain with a hydromechanical CVT.

In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.