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When vehicle traveling on the bumpy road or vehicle acceleration and deceleration, which will cause the body vibration of vehicle, at the same time, a large part of energy would be absorbed by the shock absorber transforms the mechanical energy into heat energy dissipated. In order to recycle the energy of vibration and keep the stability of running car, this paper provides the shock absorber of energy recovery that recycling the energy dissipated from the traditional absorber. The shock absorber includes rod and rodless chamber cavity, the two parts contain oil outlet and oil inlet, which connected to a bridge type loop of hydraulic to make pulsating oil pressure towards one direction, when the shock absorber vibration causes pulsating oil pressure, it drives hydraulic pump operation. Because the output shaft of the hydraulic pump fixedly attached to the input shaft of generator, so the generator produces electricity for recycling energy[1].

On road simulation, both the traditional iterative method based on frequency response function (FRF) and adaptive control method based on the CARMA model are realized by using linear model to identify the target test system. However the real test system is very complicated because of various nonlinear factors. Linear models approximately describe the system only in a small range. Therefore, system simulation methods can not be used to validate the developed control algorithm and the uncertainty of test accordingly increases. As mentioned above, this paper presents a model to identify the nonlinear test system using NARMA dynamic neural network and discusses how to make the model parameters in detail. Using the test input-output series data, this network was trained by Levenberg-Marquardt method. Results of verification simulation show the validation of the nonlinear model.

Hydraulic retarder is one of main auxiliary braking devices of the vehicle. When the vehicle is braking, a great pressure from high-speed fluid is received by hydraulic retarder blades. It is difficult to predict rational hydraulic retarder strength, owing to the complexity of the internal flow of oil. An optimal calculation way of hydraulic retarder strength is proposed based on CFD and FEA, concluding a reasonable result. The 3-D model of hydraulic retarder is built in the general CAD software. The model of fluid passage is extracted, according to the condition when the whole flow passage is filled with oil, and imported to CFD software. The inner flow field of hydraulic retarder is analyzed and the hydraulic surface pressure distribution of the hydraulic retarder blade is obtained at the highest rotary speed of turbine wheel.

Hydraulic retarder has been widely applied on military vehicles and heavy commercial vehicles because of it could provide great brake torque and has lasting working time [1]. In order to reduce driver's frequent actions in braking process and prevent hydraulic retarder system from overheating, it is need to apply constant braking torque control, this control target has a strict requirement to hydraulic control system design. Many parameters often require repeated test to determine, which increases the R&D cost and extends the research cycle. This paper tries to find a time-efficient research method of hydraulic retarder control system through studying on a heavy military vehicle hydraulic retarder system. Hydraulic retarder model is set up through test data. The hydraulic control system is built based on AMESim. Controller model is set up based on PID control. The whole vehicle brake model is built based on MATLAB/Simulink.

Road simulation experiment in laboratory is a most important method to enhance the design quality of vehicle products. Presently, two main control techniques for road simulation—remote parameter control (RPC) and minimum variance adaptive control—are both defective: the former becomes an open-loop control after generating the drive signals, however the latter is essentially a kind of gradual control. To realize the closed-loop control and increase the control quality, this article brings forward a PID open-closed loop control method. Firstly taking the original road simulator as a group to identify, a nonlinear autoregressive moving average (NARMA) model was built with the dynamic neural network. Subsequently, this plant model was used to build the open-closed loop control system mentioned above. In the closed-loop a discrete PID controller was introduced to stabilize the system, while a P-type iterative learning control (ILC) was adopted to increase the control quality.

Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact.