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The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability.

Hydraulic retarder, as an auxiliary braking device, is widely used in commercial vehicles. Nowadays, the hydraulic retarder’s internal oil is mainly cooled by the coolant circuit directly. It not only aggravates the load of engine cooling system, but also makes the abundant heat energy not be recycled properly. In this study, an independent energy supply device with organic Rankine cycles is applied to solve the problems above. In the structure of this energy supply device, the evaporator’s inlet and outlet is connected in parallel with the oil outlet and inlet of the retarder respectively. A part of oil enters the evaporator to transfer heat with the organic fluid, and the rest of oil enters the oil-water heat exchanger to be cooled by the coolant circuit. According to the different braking conditions of the retarder, the oil temperature in the inlet of the hydraulic retarder can be kept within the proper range through adjusting the oil flow rate into the evaporator properly.

Radial piston units find several applications in fluid power, offering benefits of low noise and high power density. The capability to generate high pressures makes radial piston pumps suitable for clamping function in machine tools and also to operate presses for sheet metal forming. This study is aimed at developing a comprehensive multidomain simulation tool to model the operation of a rotating cam type radial piston pump, with particular reference to the lubricating gap flow between the pistons and the cylinder block. The model consists of a first module which simulates the main flow through the unit according to a lumped parameter approach. This module evaluates the features of the displacing action accounting for the detailed evaluation of the machine kinematics and for the mechanical dynamics of the check valves used to control the timing for the connection of each piston chamber with the inlet and outlet port.

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.

In this paper the working principle of hydraulic branch of the hydromechanical transmission is introduced, dynamic characteristic analysed, its actuating medium enclosed in the working space consists that of pump, soft pipeline and motor considered to be a torsional hydraulic spring whose torsional stiffness and damping provided. The multidegrees-of-freedom of the concentrated mass-elasticity discrete mechanics model is utilized to analyze the transmission system, and the finite element method is employed to model it, the torsional natural characteristic of the system are calculated, these provide the scientific basis for structural modification of controlling the vibration and noise.

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.