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This paper presents a robust active trailer steering (ATS) controller for car-trailer combinations. ATS systems have been proposed and explored for improving the lateral stability and enhancing the path-following performance of car-trailer combinations. Most of the ATS controllers were designed using the linear quadratic regulator (LQR) technique. In the design of the LQR-based ATS controllers, it was assumed that all vehicle and operating parameters were constant. In reality, vehicle and operating parameters may vary, which may have an impact on the stability of the combination. For example, varied vehicle forward speed and trailer payload may impose negative impacts on the directional performance of the car-trailer combination. Thus, the robustness of the conventional LQR-based ATS controllers is questionable. To address this problem, we propose a gain-scheduling LQR-based ATS controller.

In this research, active aerodynamic wings are investigated using numerical simulation in order to improve vehicle handling performance under emergency scenarios, such as tight cornering maneuvers at high speeds. Air foils are selected and analyzed to determine the basic geometric features of aerodynamic wings. Built upon the airfoil analysis, the 3-D aerodynamic wing model is developed. Then, the virtual aerodynamic wings are assembled with the 3-D vehicle model. The resulting 3-D geometry model is used for aerodynamic analysis based on numerical simulation using a computational fluid dynamics (CFD) software package. The CFD-based simulation data and the vehicle dynamic model generated are combined to study the effects of active aerodynamic wings on handling performance of high-speed vehicles. The systematic numerical simulation method and achieved results may provide design guidance for the development of active aerodynamic wings for high-speed road vehicles.

Ensuring the lateral stability and handling of a car-and-trailer combination remains one of the challenges in safety system design and development for articulated vehicles. This paper reviews the state-of-the-art approaches for car-trailer lateral stability control. A literature review covering the effects of external factors, such as aerodynamic forces, tire forces, and road & climatic conditions, is presented. To address the effects of these factors, researchers have previously investigated numerous passive and active safety control techniques. This paper intends to identify the inadequacies of the passive safety approaches and analyzes promising active-control schemes, such as active trailer steering control (ATSC), active trailer braking (ATB) and model reference adaptive controller (MRAC). A comparative study of these control strategies in terms of applicability and cost effectiveness is performed.

Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.

The optimum driving dynamics can be achieved only when the tire forces on all four wheels and in all three coordinate directions are monitored and controlled precisely. This advanced level of control is possible only when a vehicle is equipped with several active chassis control systems that are networked together in an integrated fashion. To investigate such capabilities, an electric vehicle model has been developed with four direct-drive in-wheel motors and an active steering system. Using this vehicle model, an advanced slip control system, an advanced torque vectoring controller, and a genetic fuzzy active steering controller have been developed previously. This paper investigates whether the integration of these stability control systems enhances the performance of the vehicle in terms of handling, stability, path-following, and longitudinal dynamics.

A two-passenger, all-wheel-drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors has been designed and developed at the University of Waterloo. An advanced genetic-fuzzy active steering controller is developed based on this vehicle platform. The rule base of the fuzzy controller is developed from expert knowledge, and a multi-criteria genetic algorithm is used to optimize the parameters of the fuzzy active steering controller. To evaluate the performance of this controller, a computational model of the AUTO21EV is driven through several standard test maneuvers using an advanced path-following driver model. As the final step in the evaluation process, the genetic-fuzzy active steering controller is implemented in a hardware- and operator-in-the-loop driving simulator to confirm its performance and effectiveness.

The paper examines typical vehicle dynamics models used for the design of car-trailer active safety systems, including active trailer braking and steering. A linear 3 degree-of-freedom (DOF), a nonlinear 4 DOF and a nonlinear 6 DOF car-trailer model are generated. Then, these models are compared with a car-trailer model developed with the commercial software package, CarSim. The benchmark investigation of the car-trailer models is carried out through examining numerical simulation results obtained in two emulated tests, i.e., a single lane-change and a Fishhook maneuver. In the vehicle modeling, a mathematical model of a tire with flexible sidewalls is included to account for transient tire forces. Steady-state aerodynamic forces are included in these models. The deviation of the model dynamic responses, e.g., the variation of the articulation angle between the car and trailer, is discussed.

A two-passenger, all-wheel-drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors has been designed and developed at the University of Waterloo. A 14-degree-of-freedom model of this vehicle has been used to develop a genetic fuzzy yaw moment controller. The genetic fuzzy yaw moment controller determines the corrective yaw moment that is required to stabilize the vehicle, and applies a virtual yaw moment around the vertical axis of the vehicle. In this work, an advanced torque vectoring controller is developed, the objective of which is to generate the required corrective yaw moment through the torque intervention of the individual in-wheel motors, stabilizing the vehicle during both normal and emergency driving maneuvers. Novel algorithms are developed for the left-to-right torque vectoring control on each axle and for the front-to-rear torque vectoring distribution action.

This paper presents the design of airfoil and briefly introduces a real physical prototype for an actively controlled wing to improve high speed vehicle safety. Conventionally, active safety systems of road vehicles, including active steering and differential braking, mainly manipulate the tire/road forces to enhance the lateral stability of vehicles. However, this active safety technology is hindered by the saturation of tire/road forces at high lateral accelerations and on icy slippery roads. In contrast, the use of controlled aerodynamic forces has received little attention. In this paper, the actively controlled wing is proposed to manipulate the negative lift force (downforce) to enhance handling capabilities of vehicles at high speeds.

Vehicle simulators are often used for vehicle system development and driver behaviour study. The target of this study is to design and evaluate an Active Trailer Steering (ATS) system for a Long Combination Vehicle (LCV) with mutiple trailers using a real-time vehicle simulator. A linear yaw-plane LCV model is generated to derive an optimal ATS controller. Then, the controller is reconstructed in LabVIEW and integrated with a vehicle model for a B-train double from TruckSim. The Driver-Software-in-the-Loop (DSIL) real-time simulations are conducted with the vehicle simulator. The DSIL real-time simulations indicate that the ATS controller can effectively improve the LCV's low-speed maneuverability and high-speed stability under the test maneuvers of a low-speed 90-degree turn and a high-speed single-lane change, respectively.

The recent increase in oil prices and environmental concerns have attracted various research efforts on hybrid electric vehicles (HEVs) which provide promising alternatives to conventional engine-powered vehicles with better fuel economy and fewer emissions. To speed up the design and prototyping processes of new HEVs, a method that automatically generates mathematics equations governing the vehicle system response in an optimized symbolic form is desirable. To achieve this goal, we employed MapleSimTM, a new physical modeling tool developed by Maplesoft Inc., to develop the multi-domain model of a series-HEV, utilizing the symbolic computing algorithms of Maple software package to generate an optimized set of governing equations. The HEV model consists of a mean-value internal combustion engine (ICE), a chemistry-based Ni-MH battery pack, and a multibody vehicle model. Simulations are then used to demonstrate the performance of the developed HEV system.

Because of its widespread use in almost all the current electric and hybrid electric vehicles on the market, nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. To help understand these complex phenomena, a mathematical model of a Ni-MH battery will be presented in this paper. A parametric importance analysis is performed on this model to assess the contribution of individual model parameters to the battery performance. In this paper the efficiency of the battery is chosen as the performance measure. Efficiency is defined by the ratio of the energy output from the battery and the energy input to the battery while charging. By evaluating the sensitivity of the efficiency with respect to various model parameters, the order of importance of those parameters is obtained.

A method to improve the computational efficiency of analyzing wheeled vehicle systems on three-dimensional (3-D) roads has been developed. This was accomplished by creating a technique to incorporate the tire on a 3-D road in a multibody dynamics model of the vehicle with an approach that formulates the governing equations using symbolic formulation. For general handling analysis performed on the vehicle, the tire forces and moments are determined using a tire model that represents the tire as a set of mathematical expressions. Since these expressions need numerical values to determine the forces and moments, a symbolic solution does not exist. Therefore, the evaluation of the tire forces and moments needs to be done during simulation. However, symbolic operations can be used when the governing equations are formulated to develop an efficient method to evaluate these forces.

An electric vehicle model has been developed with four direct-drive in-wheel motors. A high-level vehicle stability controller is proposed, which uses the principles of fuzzy logic to determine the corrective yaw moment required to minimize the vehicle sideslip and yaw rate errors. A genetic algorithm has been used to optimize the parameters of the fuzzy controller. The performance of the controller is evaluated as the vehicle is driven through a double-lane-change maneuver. Preliminary results indicate that the proposed control system has the ability to improve the performance of the vehicle considerably.

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

This paper outlines the state-of-the-art of approaches for automated design synthesis of ground vehicle suspensions. Conventionally, design synthesis of suspensions has been based on trial and error approaches, where designers iteratively change the values of design variables and reanalyze until acceptable performance criteria are achieved. This is time-consuming and tedious. With stringent requirements for vehicles, design synthesis undergoes fundamental changes. This puts much attention on the potentials of an automated process. This process is based on the following techniques: effective modelling and simulation methods, realistic formulation approaches, and appropriately selected optimization algorithms. These techniques are reviewed and an automated design synthesis methodology is briefly introduced.