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Lower engine emission is an important target in the evaluation of the control strategy of ECU. So the hardware in the loop simulation system (HILSS) including emission model is necessary. In this paper, a NOx emission neural network (NN) model is constructed based on the reflection relationship between the NOx formation and some direct influence factors such as concentration of oxygen, combustion temperature, combustion period. Combined with a nonlinear dynamic diesel engine model based on the filling and emptying methods, the NOx emission NN model can reach the trade-off between simulation accuracy and computational overhead. A new HILS platform based on Matlab/RTW and VxWorks real time operating system is introduced in the paper. The graphic programming and automatic code generating methods also developed to accelerate the development of HILSS.

This work is motivated by the fact that the surface of a terrain may vary with local pavement properties and number of passes of the vehicle, which means the roughness coefficient and waviness of the terrain may vary in specific intervals. However, in traditional random terrain models, the roughness coefficient and waviness of the terrain are assumed as constants. Therefore, this assumption may be not very reasonable. A novel random terrain model is presented where the roughness coefficient and waviness of the terrain are expressed by interval numbers instead of constants. A 5-degree-of-freedom ride dynamic model of the vehicle with uncertain parameters is derived. The power spectral density (PSD) and root mean square value (RMS) of the vehicle ride responses are shown and analyzed. Analysis results indicate that the vehicle responses vary in specific intervals under the random terrain excitation with interval parameters.

The soft and rough terrain on the planet's surface significantly affects the ride and safety of rovers during high-speed driving, which imposes high requirements for the control of the suspension system of planet rovers. To ensure good ride comfort of the planet rover during operation in the low-gravity environment of the planet's surface, this study develops an active suspension control strategy for torsion spring and torsional damper suspension systems for planet rovers. Firstly, an equivalent dynamic model of the suspension system is derived. Based on fractal principles, a road model of planetary surface is established. Then, a fuzzy-PID based control strategy aimed at improving ride comfort for the planet rover suspension is established and validated on both flat and rough terrains.