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This paper focuses on developing an advanced analytical tire-terrain interaction model for full vehicle performance prediction purposes. The truck tire size 315/80R22.5 is modeled using the Finite Element Analysis (FEA) technique and validated against manufacturer experimental data in static and dynamic domains. While the terrain is modeled using Smoothed-Particle Hydrodynamics (SPH) technique and calibrated using experimental results of pressure-sinkage and direct shear tests. The contact between the FEA tire model and the SPH soil model is defined using the node symmetric node to segment with the edge treatment algorithm. The model setup consists of four tires appended back to back over a box filled with soil particles to represent a multi-axle off-road truck. The distances between the four tires are similar to the distances between the four axles of an off-road truck.

Lateral dynamic control considered to be crucial to enhance the handling characteristics and stabilization of a vehicle as a safety demand. In this paper, active rear axles steering control system will be developed using an optimal quadratic regulator (LQR) control methodology. The controller aims to minimize the vehicle side slip and consequently increase its handling stability and transient state performance. The controller design has been utilized the independent steering of the vehicle’s 3rd and 4th axles as control inputs. Furthermore, the developed controller will be combined with a feedforward zero side slip (ZSS) controller based on the steady-state model of the vehicle and satisfying the Ackermann steering condition. In addition, the steady-state handling performance will be evaluated using the Skid Pad test.

Modern Finite Element Analysis (FEA) techniques allow for accurate simulation of various non-linear systems. However they are limited in their simulation of particulate matter. This research uses smooth particle hydrodynamics (SPH) in addition to FEA techniques to model the properties of soils, which allows for particle-level replication of soils. Selected soils are simulated in a virtual environment and validated using the pressure-sinkage and shear tests. A truck tire model is created based on standard heavy vehicle tires and validated using static deflection, contact footprint, and dynamic first mode of vibration tests. The validated tires and soils are used to create a virtual terrain and the tire is placed on the soil, loaded, and run over the soil at various speeds. The results of these simulations show that the SPH modeling technique offers higher accuracy than comparable FEA models for soft soils at a higher computational cost.

The rigid-ring tire model is a simplified tire model that describes a tire's behaviour under known conditions through various in-plane and out-of-plane parameters. The complex structure of the tire model is simplified into a spring-mass-damper system and can have its behaviour parameterized using principles of mechanical vibrations. By designing non-linear simulations of the tire model in specific situations, these parameters can be determined. They include, but are not limited to, the cornering stiffness, vertical damping constants, self-aligning torque stiffness and relaxation length. In addition, off-road parameters can be determined using similar methods to parameterize the tire model's behaviour in soft soils. By using Finite Element Analysis (FEA) modeling methods, validated soil models are introduced to the simulations to find additional soft soil parameters.

With the rise in demand, advanced steering control and electric vehicle technology are rapidly developing in modern times. Due to a controller's role as a backbone for the modern vehicle, its study has become increasingly crucial. This research proposes a novel 4th axle steering (4AS) feedforward controller that utilizes the first, second and fourth axle steering control for an 8x8 scaled electric combat vehicle. The vehicle is tested using the predefined path following. The novel 4AS controller is then compared to the Ackermann steering condition at different speeds. In the scaled vehicle used for this research, each wheel is independently driven by an in-wheel motor, while the steering is carried out by linear actuators. Individual eight-wheel steering control systems are designed and installed on the scaled vehicle to evaluate the driving performance from low speed to high speed. The 4AS steering method is implemented to improve the stability of the scaled vehicle at high speeds.

This work investigates a multi-objective optimization approach for minimizing design parameters for Front Underride Protection Devices (FUPDs). FUPDs are a structural element for heavy vehicles to improve crashworthiness and prevent underride in head-on collision with another vehicle. The developed dsFUPD F9 design for a Volvo VNL was subjected to modified ECE R93 testing with results utilized in the optimization process. The optimization function utilized varying member thickness to minimize deformation and system mass. Enhancements to the function were investigated by introducing variable materials and objectifying material cost. Alternative approaches for optimization was also needed to be explored. Metamodel-based and Direct simulation optimization strategies were compared to observe there performance and optimal designs. NSGA-II, SPEA-II Genetic Algorithms and Adaptive Simulated Annealing algorithms were under investigation in combination with three meta-modeling techniques.

This paper investigates the tire-road interaction for tires equipped with two different solid rubber material definitions within a Finite Element Analysis virtual environment, ESI PAMCRASH. A Mixed Service Drive truck tire sized 315/80R22.5 is designed with two different solid rubber material definitions: a legacy hyperelastic solid Mooney-Rivlin material definition and an Ogden hyperelastic solid material definition. The popular Mooney-Rivlin is a material definition for solid rubber simulation that is not built with element elimination and is not easily applicable to thermal applications. The Ogden hyperelastic material definition for rubber simulations allows for element destruction. Therefore, it is of interest and more suited for designing a tire model with wear and thermal capabilities.

This paper focuses on predicting the rolling resistance and hydroplaning of a wide base truck tire (Size: 445/50R22.5) on dry and wet surfaces. The rolling resistance and hydroplaning are predicted at various inflation pressures, loads, velocities, and water depths. The wide base truck tire was previously modeled and validated using Finite Element Analysis (FEA) technique in virtual performance software (Pam-Crash). The water is modeled using Smoothed-Particle Hydrodynamics (SPH) method and Murnaghan equation of state. A water layer is first built on top of an FEA rigid surface to represent a wet surface. The truck tire is then inflated to the desired pressure. A vertical load is then applied to the center of the tire. For rolling resistance tests variable constant longitudinal speeds are applied to the center of the tire. The forces in the vertical and longitudinal directions are computed, and the rolling resistance is calculated.