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Ride comfort and handling performances are known conflicts for off-road vehicles. Recent publications focus on passenger vehicles on class B and class C roads, while, for off-road vehicles, they should be able to run on rougher roads: class D, class E, or class F roads. In this paper, a quarter vehicle model with nonlinear damping is established to analyze the suspension performance of a medium off-road vehicle on the class F road. The ride comfort, road holding, and handling performance of the vehicle are indicated by the weighted root mean square (RMS) value of the vertical acceleration of the sprung mass, suspension travel, and tire deflection. To optimize these objectives, the genetic algorithm (GA), particle swarm optimization (PSO), and a genetic algorithm based on the particle swarm optimization (GA-PSO) are initiated. The efficiency and accuracy of these algorithms are compared to find the best suspension parameters. The effect of the optimized method is validated by the field test result. The ride comfort, road holding, and handling performance are improved by approximately 20%.

This paper investigates the effects of tire characteristics on vehicular rollover and lateral stability. Two tire types with different adhesion coefficients were selected to evaluate the relation between vehicular rollover propensity and lateral stability. Simulations were used to calculate the critical rollover factor and to analyze the effects of vehicular parameters on handling, including the center of gravity, payload condition, and vehicle speed, with the two proposed types of tires. To replicate an actual vehicular response, particularly during extreme driving operations, a two-degrees of freedom (DOF) planar two-track model with nonlinear Pacejka’s Magic Tire Formula was applied. Subsequently, a 7-DOF vehicle vibration and roll model was developed to consider the effects of suspension and road excitation. The tire, steering, and vehicle vibration models were implemented in MATLAB/Simulink by subjecting them to the Fishhook maneuver steering input defined by the National Highway Traffic Safety Administration. The results confirm that the adhesion capacities of tires have an opposite effect on lateral vehicle stability and rollover propensity, while both suspension parameters and road excitation inputs significantly influence vehicle rollover and lateral stability. Additionally, we identified a positive correlation between vehicle properties and lateral handling, especially when tire characteristics are considered.

Vehicle overload seriously affects the traffic safety, damages the road infrastructure, and the road service life is reduced. There are many shortcomings in the current detection methods for overloaded vehicles. Traditional static weighing stations are relatively fixed and affect the road traffic efficiency; the cost of weighing in motion station is high, while the precision is not enough; on-board weighing electronic products require the modification of vehicles, which is difficult to promote. In this paper, a non-contact overload detection method based on body vibration is proposed. After the analyzation of the body vibration response of dynamic vehicles under specific vibrational excitation, the load value data can be obtained combined with standard parameters and mathematical calculation model. Firstly, the body vibration response model under specific excitation is established. Then, roadbed facilities are arranged according to specific requirements, cameras calibration are finished, and the identification environment is built. Machine vision technology is used to identify the vibration track of the characteristic point on the vehicle body in the vertical direction. The vibration response characteristic parameters are extracted using the established response model. Finally, the vehicle load value data can be obtained by resolve the characteristic parameters. Compared with the rated load data in the database, the overload judgment of the vehicle is obtained. In the experiment part, the road speed-control hump was used as the vibration excitation source. The vehicle experiments were carried out with Dongfeng Aeolus S30 and Yuejin Shangjun X500. The results show that the load identification error can be controlled within 20%–30%. This method above can detect overload vehicles without affecting the traffic efficiency and also has certain guiding significance for the development of intelligent vehicles.