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A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

The road profile has been shown to have significant effects on various vehicle conditions including ride, handling, fatigue or even energy efficiency; as a result it has become a variable of interest in the design and control of numerous vehicle parts. In this study, an integrated state estimation algorithm is proposed that can provide continuous information on road elevation and profile variations, primarily to be used in active suspension controls. A novel tire instrumentation technology (smart tire) is adopted together with a sensor couple of wheel attached accelerometer and suspension deflection sensor as observer inputs. The algorithm utilizes an adaptive Kalman filter (AKF) structure that provides the sprung and unsprung mass displacements to a sliding-mode differentiator, which then yields to the estimation of road elevations and the corresponding road profile along with the quarter car states.

Models for off-road vehicles, such as farm equipment and military vehicles, require an off-road tire model in order to properly understand their dynamic behavior on off-road driving surfaces. Extensive literature can be found for on-road tire modeling, but not much can be found for off-road tire modeling. This paper presents an off-road tire model that was developed for use in vehicle handling studies. An on-road, dry asphalt tire model was first developed by performing rolling road force and moment testing. Off-road testing was then performed on dirt and gravel driving surfaces to develop scaling factors that explain how the lateral force behavior of the tire will scale from an on-road to an off-road situation. The tire models were used in vehicle simulation software to simulate vehicle behavior on various driving surfaces. The simulated vehicle response was compared to actual maximum speed before sliding vs. turning radius data for the studied vehicle to assess the tire model.

Dynamic game theory brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion.

Human expert drivers have the unique ability to combine correlated sensory inputs with repetitive learning to build complex perceptive models of the vehicle dynamics as well as certain key aspects of the tire-ground interface. This ability offers significant advantages for navigating a vehicle through the spatial and temporal uncertainties in a given environment. Conventional traction control algorithms utilize measurements of wheel slip to help insure that the wheels do not enter into an excessive slip condition such as burnout. This approach sacrifices peak performance to ensure that the slip limits are generic enough suck that burnout is avoided on a variety of surfaces: dry pavement, wet pavement, snow, gravel, etc. In this paper, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy.

In this study, the preliminary validation method of the steering system is constructed and the objective is to satisfy the target performance in the conceptual design stage for minimizing the problems after the detailed design. The first consideration about steering system is how to extract the reliable steering effort for parking. The tire model commonly used in MBD(Multi-Body Dynamics) has limited ability to represent deformations under heavy loads. Therefore, it is necessary to study adequate tire model to simulate the behavior due to the large deformation and friction between the ground and the tire. The two approaches related with F tire model and mathematical model are used. The second is how to extract each link’s load in the conceptual design stage. Until now, each link’s load could be derived only by actual vehicle test, and a durability analysis was performed using only pre-settled RIG test conditions.

Numerous machine elements are operated in mixed lubrication regime where is governed by a combination of boundary and fluid film effects. The direct contact between two surfaces reduces a machines life by increasing local pressure. In order to estimate machine's life exactly, the effect of asperity contact should be considered in the lubrication model. In this study, new 3-dimensional partial elasto-hydrodynamic lubrication (PEHL) algorithm is developed. The algorithm contains the procedures to find out solid contact regions within the lubricated regime and to calculate both the pressure by fluid film and the contact pressure between the asperities of the solids. Using the algorithm, we conducted the PEHL analysis for the contact between the rotating shaft and the inside of pinion gear. To investigate the effect of surface topology two different surfaces with sinusoidal profile are used. Both film thickness and pressure are calculated successfully through the PEHL algorithm.

The Rollover CAE model is developed for Rollover sensing algorithm in this paper. By using suggested CAE model, it is possible to make sensing data of rollover test matrix and these data can be used for calibration of rollover sensing algorithm. Developed vehicle model consists of three parts: a vehicle parts, an occupant parts and a ground boundary conditions. The vehicle parts include detailed suspension model and FE structure model. The occupant parts include ATD (anthropomorphic test device) male dummy and restraint systems: Curtain Airbag and Seat-Belt. We find analytical value of the suspension model through correlation with vehicle drop test, simulate this model under the conditions of untripped (Embankment, Corkscrew) and tripped (Curb-Trip, Soil-Trip) rollover scenarios. Comparison of the simulation and experimental data shows that the simulation results of suggested CAE model can be substituted for the experimental ones in calibration of rollover sensing algorithm.

This study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

A detailed study of the effects of shim stack assemblies on performance of hydraulic mono-tube vehicle shock absorbers is presented. Currently, shim stacks are modeled as blow-off valves in hydraulic models of shock absorbers. Using this simplification, important material and geometrical properties of shim stacks cannot be studied and their effects cannot be understood on overall damper performance. In this paper, shim stack deflection is investigated and a mathematical model is presented for shim stack deflection. This model is then incorporated into the mathematical model of a hydraulic damper and various properties of shim stack and their effects on damper characteristics are studied. Energy and variational methods were used to develop the mathematical model of the shim stack. The mathematical model also takes into account the sliding effects of the shims on each other when the shim stack is deflected.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is currently developing a control strategy for a parallel plug-in hybrid electric vehicle (PHEV). The hybrid powertrain is being implemented in a 2016 Chevrolet Camaro for the EcoCAR 3 competition. Fuzzy rule sets determine the torque split between the motor and the engine using the accelerator pedal position, vehicle speed and state of charge (SOC) as the input variables. The torque producing components are a 280 kW V8 L83 engine with active fuel management (AFM) and a post-transmission (P3) 100 kW custom motor. The vehicle operates in charge depleting (CD) and charge sustaining (CS) modes. In CD mode, the model drives as an electric vehicle (EV) and depletes the battery pack till a lower state of charge threshold is reached. Then CS operation begins, and driver demand is supplied by the engine operating in V8 or AFM modes with supplemental or loading torque from the P3 motor.

This paper proposes a reference steering wheel torque map and a torque tracking algorithm via steer-by-wire to achieve the targeted steering feel. The reference steering wheel torque map is designed using the measurement data of rack force and steering characteristic of a target performance of the vehicle at transition steering test. Since the target performance of the vehicle is only tested in nominal road condition, various road conditions such as disturbances and tire-road friction are not considered. Hence, the measurement data of the rack force that reflects the road conditions in the reference steering wheel torque map have been used. The rack force is the net force which consists of tire aligning moment, road friction force and normal force on the tire kingpin axis. A motor and a magnetorheological damper are used as actuators to generate the desired steering feel using the torque tracking algorithm.

Magneto-rheological fluid squeeze mode investigations at CVeSS have shown that MR fluids show large force capabilities in squeeze mode. A novel MR squeeze mount was designed and built at CVeSS, and a dynamic mathematical model was developed, which considered the inertial effect and was validated by the test data. A variant engine mount that will be used for isolating vibration, based on the MR squeeze mode is proposed in the paper. The mathematical governing equations of the mount are derived to account for its operation with MR squeeze mode. The design method of a robust H✓ controller is addressed for the squeeze mount subject to parameter uncertainties in the damping and stiffness. The controller parameter can be derived from the solution of bilinear matrix inequalities (BMIs). The displacement transmissibility is constrained to be no more than 1.05 with this robust H✓ controller. The MR squeeze mount has a very large range of force used to isolate the vibration.

This paper describes a reference steering feel tracking algorithm for Electric-Power-Steering (EPS) system. Development of the EPS system with intended steering feel has been time-consuming procedure, because the feedforward map-based method has been applied to the conventional EPS system. However, in this study, a three-dimensional reference steering feel surface, which is determined from current vehicle states, is proposed. In order to track the proposed reference steering feel surface, sliding mode approach is applied to second-order steering dynamics model considering a coulomb friction model. An adaptive technique is utilized for robustness against uncertainties. In order to validate the proposed EPS control algorithm, hardware-in-the-loop simulation (HILS) has been conducted with respect to a typical steering test. It is shown that the reference steering feel is realized well by the proposed EPS control algorithm.

Road friction estimation algorithms had been studied for many years because it is very important factor for safety control and fuel efficiency of vehicle. But traditional solutions are hard to adapt in automotive industry because their performance is not sufficient enough and expensive to implement. Therefore, this paper proposes a road friction estimation algorithm based on a trained artificial neural-network which is low cost and robust. The suggested method doesn’t need expensive additional sensors such as optical or lidar sensor, also it shows better performance in real car environment compared to other algorithms based on vehicle dynamics. In this paper, we would describe this algorithm in detail and analyze the test results evaluated in real road conditions.

In this study, a co-operative regenerative braking control algorithm was developed for an electric vehicle (EV) equipped with an electronic wedge brake (EWB) for its front wheels and an electronic mechanical brake (EMB) for its rear wheels. The co-operative regenerative braking control algorithm was designed considering the road friction characteristic to increase the recuperation energy while avoiding wheel lock. A powertrain model of an EV composed of a motor, and batteries and a MATLAB model of the control algorithm were also developed. They were linked to the CarSim model of the vehicle under study to develop an EV simulator. The EMB and EWB were modeled with an actuator, screw, and wedge to develop an EMB and EWB simulator. A co-simulator for an EV equipped with an EWB for the front wheels and an EMB for the rear wheels was fabricated, composed of the EV and the EMB and EWB simulator.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech has achieved the Year 2 goal of producing a 65% functional mule vehicle suitable for testing and refinement, while maintaining the series-parallel plug-in hybrid architecture developed during Year 1. Even so, further design and expert consultations necessitated an extensive redesign of the rear powertrain and front auxiliary systems packaging. The revised rear powertrain consists of the planned Rear Traction Motor (RTM), coupled to a single-speed transmission. New information, such as the dimensions of the high voltage (HV) air conditioning compressor and the P2 motor inverter, required the repackaging of the hybrid components in the engine bay. The P2 motor/generator was incorporated into the vehicle after spreading the engine and transmission to allow for the required space.

Predicting driver response to road departure and attempted recovery is a challenging but essential need for estimating the benefits of active safety systems. One promising approach has been to mathematically model the driver steering and braking inputs during departure and recovery. The objective of this paper is to compare a model developed by Volvo, Ford, and UMRTI (VFU) through the Advanced Crash Avoidance Technologies (ACAT) Program against a set of real-world departure events. These departure events, collected by Hutchinson and Kennedy, include the vehicle's off road trajectory in 256 road departure events involving passenger vehicles. The VFU-ACAT model was exercised for left side road departures onto the median of a divided highway with a speed limit of 113 kph (70 mph). At low departure angles, the VFU-ACAT model underpredicted the maximum lateral and longitudinal distances when compared to the departure events measured by Hutchinson and Kennedy.

This paper proposes a torque tracking algorithm via steer by wire to achieve the target steering feel and proposed a modified friction model to obtain return-ability. A three dimensional reference steering wheel torque map is designed using the measurement data of the steering characteristics of the target vehicle at a transition test and a weave test. In order to track the reference steering wheel torque, a sliding mode control is used in the tracking algorithm. In addition, to achieve return-ability, the modified friction model for steer by wire is used instead of the friction model defined in the reference steering wheel torque map. The modified friction model is composed of various models according to the angular velocity. The angular velocity and the angular acceleration used in the control algorithm are estimated using a kalman filter.