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Hydrostatic (hydraulic hybrid) drives have demonstrated energy efficiency and emissions reduction benefits. This paper investigates the potential of an independent hydrostatic wheel drive system for implementing a traction-based vehicle lateral stability control system. The system allows an upper level vehicle stability controller to produce a desired corrective yaw moment via a differential distribution of torque to the independent wheel motors. In cornering maneuvers that require braking on any one wheel of the vehicle, the motors can be operated as pumps for re-generating energy into an on-board accumulator. This approach avoids or reduces activation of the friction brakes, thereby reducing energy waste as heat in the brake pads and offering potential savings in brake maintenance costs. For this study, a model of a 4×4 hydrostatic independent wheel drive system is constructed in a causal and modular fashion and is coupled to a 7 DOF vehicle handling dynamics model.

Rollover prevention is now part of complete vehicle stability control systems for many vehicles. Given that rollover is predominantly associated with vehicles with high centers of gravity, the targeted vehicles for rollover protection include medium and heavy duty commercial vehicles. Unfortunately, the chassis designs of these vehicles are often so compliant in torsion that the ends of the vehicles may have significantly different roll responses at any given time. The potential need to assess and correct for the roll behavior of the front and rear ends of the vehicle is the subject of this paper. Most rollover mitigation research to date has used rigid chassis assumptions in modeling the vehicle. This paper deals with the roll control of vehicles with torsionally flexible chassis based on a yaw-correction system.

Due to titanium's excellent strength-to-weight ratio and high corrosion resistance, titanium and its alloys have great potential to reduce energy usage in vehicles through a reduction in vehicle mass. The mass of a road vehicle is directly related to its energy consumption through inertial requirements and tire rolling resistance losses. However, when considering the manufacture of titanium automotive components, the machinability is poor, thus increasing processing cost through a trade-off between extended cycle time (labor cost) or increased tool wear (tooling cost). This fact has classified titanium as a “difficult-to-machine” material and consequently, titanium has been traditionally used for application areas having a comparatively higher end product cost such as in aerospace applications, the automotive racing segment, etc., as opposed to the consumer automotive segment.

This paper proposes a new vehicle stability control method that quantifies and uses the level of lateral force saturation on each axle/wheel of a vehicle. The magnitude of the saturation, which can be interpreted as a slip-angle deficiency, is determined from on-line estimated nonlinear tire lateral forces and their linear projections that use estimates of the cornering stiffness. Once known, the saturation levels are employed in a saturation balancing control structure that biases the drive torque to either the front or rear axles/wheels with the goal of minimizing excessive under- or over-steer, thereby stabilizing the vehicle. The method is particularly suited for a vehicle with an independent wheel drive system. Furthermore, the method can be used in conjunction with a direct yaw-moment controller to obtain enhanced stability and responsiveness.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.