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On account of the traditional friction brake for heavy-duty truck (HDT), the massive quantity of heat accumulating constantly because of frequent using of friction brake system in the long and steep downhill road leads to brake temperature rising rapidly. Affected by structure frictional couple installed in the closed environment of the brake drum, it is difficult to dissipate the heat in time via heat conduction, heat radiation and heat convection, and the heat fade phenomenon of the brake emerges easily. The HDT would be in danger because of braking efficiency descending. This paper proposes an active water-cooled drum brake system (AWBS) to solve the problem. According to the principle of engineering thermodynamics, the structure and size of the back-stretching water jacket of brake shoe and the inner riveted the friction plate of brake drum are designed with restrained of GB 12676-2014 ‘Technical requirements and testing methods for commercial vehicle and trailer braking systems’.

In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.

The authors have proposed a new method to identify the important information which links to the basic principle of the design's physical behavior by using CAE technology, and this method was named as CAP (Computer-Aided Principle).This method can help the engineers to grasp the basic physical characteristic that governs the first-order behavior. In this study, the authors applied CAP to the simulations of the design of frontal crash phenomena, which are difficult to understand because of the problem of strong nonlinearity, and explored the possibilities for using CAP. The correlative physical parameters thus obtained can help designers to understand the essence of the phenomena involved.

The Computer-Aided Principle (CAP) is applied in this study as an effective approach to the crashworthiness design of the vehicle front-end structure. With this method, correlative parameters are extracted in a parametric study by using a cluster analysis. The results can help engineers to understand the fundamental mechanisms of structural phenomena. A simulation example of an offset frontal crash against a deformable barrier (ODB) is presented to show the effectiveness of the proposed method.

It is important to improve the deceleration characteristic and the cabin deformation for a collision safety design of vehicle. At the same time it is required to reduce the vehicle weight to satisfy the driving performance and environmental restriction. Therefore there is a strong demand to develop an optimization approach to design a safer and lighter vehicle. The authors have proposed a Statistical Design Support System (SDSS), and brought forward that the SDSS is one of the available optimal approaches for the nonlinear and dynamic phenomena. In this study, they proposed a new concept called as mode controlling approach to keep the side members in ideal buckling mode. As a result, it was shown that high deceleration performance and light body can be realized by using this approach and SDSS.

The Statistical Design Support System (SDSS) produces a new practical optimal design methodology with which optimization can be done using a small number of case studies. In prior study, the authors applied it to the optimization of design parameters of occupant restraint system, and have tried to reduce the injury criteria of occupants based on the crash simulation. However, response surfaces did not have enough accuracy in approximation. Therefore the authors made detailed investigation on the relationship between occupant restraint system and injury criteria. This paper also discusses the possibility to improve approximating accuracy of response surfaces for injury criteria. Furthermore, based upon the results of detailed effectivity study, only the important design factors were chosen for the optimum design analysis. In the optimization analysis, the occupant’s figure was handled as an uncertain factor.

CAE technology has been applied for the crash safety design of vehicles, however, the optimum design has not been completed for the crash behavior of the vehicles. The authors have proposed a Statistical Design Support System (SDSS), and brought forward that the SDSS is one of the available optimal approaches for the nonlinear and dynamic phenomena. In this study, the SDSS was applied for the multi-objective optimization design of the reinforced members of a vehicle, where the weight, cabin deformation, and collision acceleration were chosen as the objective functions, and the simultaneous optimization for the functions was carried out. It was shown that the SDSS could be used as a practical multi-objective optimization tool for the crash safety design of vehicles.

The “Statistical Design Support System” produces a new practical optimal design method. It can be used even on nonlinear behavior. The optimization can be carried out with this system using a small number of calculation results. The authors applied it to the design optimization of the occupant restraint system in order to reduce the injury criteria based on the crash simulation. In line with growing interest and improvements in technology on vehicle safety, it will be necessary to consider some different crash situations simultaneously. The authors made an optimal design taking into account the different collision conditions. This paper describes the effectivity analysis and the optimization.