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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.

Hydraulic retarder braking torque is needed to be controlled. Retarder braking torque refers to oil volume in retarder chamber, thus the key of braking torque control is oil charging & discharging control. The hydraulic oil charging & discharging system should have fast accurate response and provide enough flow rate. To achieve the requirement, the system is often complicated, many parameters need 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 on hydraulic retarder oil charging & discharging control system. The complete system is divided into some parts and every part is analyzed respectively based on mechanical theory and hydraulic theory. To build the precise model of all parts, the AMESim software is used.

Speed ratio and clamping force are two of the metal-belt CVT control targets. Conventional control strategies can not correspond to the driver's intention or provide various driving environment. A fuzzy logic ratio control algorithm and a fuzzy logic clamping force control algorithm for a metal-belt CVT are proposed. Nevertheless, high-quality fuzzy control rule base and factors of FLC are difficult to gain because repeated tests and experts' experience are needed. Therefore, genetic algorithm (GA) is introduced to optimize the fuzzy control algorithms. Using the optimized fuzzy control algorithms, Metal-belt CVT control simulations were implemented. The results show that a faster response and better robustness can be gained when compared with those of the PID control.

Blade Electric Vehicle (BEV) with a light body plays an important role in saving the energy and reducing the exhaust emission. However, reducing the body weight need to meet the heterogeneous attributes such as structural, safety and NVH (Noise, Vibration and Harshness) performance. With the rapid development of finite element (FE) analysis technology, simulation analysis is widely used for researching the complex engineering design problem. Multidisciplinary Design Optimization (MDO) of a BEV body is a challenging but meaningful task in the automotive lightweight. In present research, the MDO is introduced to optimize a BEV Body-in-White (BIW).