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High fidelity mathematical vehicle models that can accurately capture the dynamics of car suspension system are critical in vehicle dynamics studies. System identification techniques can be employed to determine model type, order and parameters. Such techniques are well developed and usually used on linear models. Unfortunately, shock absorbers have nonlinear characteristics that are non-negligible, especially with regard the vehicle's vertical dynamics. In order to effectively employ system identification techniques on a vehicle, a nonlinear mathematical shock absorber model must be developed and then coupled to the linear vehicle model. Such an approach addresses the nonlinear nature of the shock absorber for system identification purposes. This paper presents an approach to integrate the nonlinear shock absorber model into the vehicle model for system identification.

This paper presents a methodology for designing a simple open-loop steering robot profile to simulate a double lane change maneuver for track testing of a heavy tractor/trailer combination vehicle. For track testing of vehicles in a lane change type of maneuver, a human driver is typically used with a desired path defined with visual cues such as traffic cones. Such tests have been shown to result in poor test repeatability due to natural variation in driver steering behavior. While a steering robot may be used to overcome this repeatability issue, such a robot typically implements open-loop maneuvers and cannot be guaranteed to cause the vehicle to accurately follow a pre-determined trajectory. This paper presents a method using offline simulation to design an open-loop steering maneuver resulting in a realistic approximation of a double lane change maneuver.