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The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability.

To improve the power and economy performance of pure electric vehicles, chaos particle swarm optimization (CPSO) algorithm is adopted in this study to optimize the parameters of the power system. The optimized parameter is then imported into CRUISE. The whole vehicle performance simulation in power system optimization for pure electric vehicle is carried out in CRUISE. Simulation results show that optimized vehicles can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the power performance and economic performance, which provides a reference for the development of vehicle power performance.

A new extended planar model for multi-axle articulated vehicle with nonlinear tire model is presented. This nonlinear multi-axle articulated vehicle model is specifically intended for improving the model performance in operating regimes where tire lateral force is near the point of saturation, and it has the potential to extend the specific axles model to any representative configuration of articulated vehicle model. At the same time, the extended nonlinear vehicle model can reduce the model's sensitivity to the tire cornering coefficients. Firstly, a nonlinear tire model is used in conjunction with the 6-axle planar articulated vehicle model to extend the ranges of the original linear model into the nonlinear regimes of operation. Secondly, the performance analysis of proposed nonlinear vehicle model is verified through the double lane change maneuver on different road adhesion coefficients using TruckSim software.