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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.

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.

This work analyzes the transmission efficiency of vehicle driveline including the gearbox, universal transmission and differential. Based on the structure of transmission, mathematic models are built to analyze transmission's characteristics. However, an experiment reveals the limitation of this method. Then, the paper statistically analyzes the experimental data and mainly analyzes the influencing factors. Then Neural Network is used to build the efficiency model. A method called “filling data and gradually extrapolating” is used when building neural network model. Finally, the neural network model is used in the simulation of fuel consumption. The conclusion is Neural Network model can imitate the transmission efficiency of vehicle driveline efficiently, but its internal structure is not clear so other modeling methods are needed to be found.

In order to improve the drivability of passenger cars with dual clutch transmission (DCT) and reveal the criteria for objective evaluation criteria and characteristic index and feature index division of vehicles under specific working conditions, a drivability evaluation system that integrates data-driven and the consistency between subjective and objective is proposed. At first, combined with the control principle and dynamics theory of specific working conditions, a quantitative index system of vehicle drivability is constructed, including three modules: data source, evaluation working conditions and objective indicators. Then, a novel intelligent drivability objective evaluation tools (I-DOET) is designed, including data acquisition, de-noising, working condition recognition, feature extraction and automatic scoring.