An essential tool in the truck development process is the ability to quantify and grade vehicle dynamic behavior. Today this is performed either through subjective or objective tests. Subjective tests have the disadvantage that numerous factors influence test drivers’ opinions while objective measures have the advantage of repeatability. However, objective methods of today are often only able to provide a rough grading of vehicles. The main objective with this thesis is to develop more sensitive objective methods for ride comfort evaluation. An effective test procedure to measure driver perception sensitivity to small differences in vehicle ride is suggested and utilized. The driver sensitivity is tested on dynamic behavior that is typically graded in vehicle development. Cab motions from a truck are first measured and then recreated in a simulator where a test driver is seated. The perception threshold for small changes in typical vehicle motion is established in this way for each test person. The perception sensitivity tests indicate that humans are quite sensitive to transients in vehicle motion. One problem with many common vehicle ride measures is that the impact of transient behavior is small due to the averaging used to condense the measurement data into scalar measures. A new evaluation method for ride comfort, with influences from the well known handling diagram, is suggested. This method has four main advantages: it is fairly simple to interpret, it shows the absolute vibration level, it considers transient events separately and it shows changes in vehicle character with increasing excitation. Promising results from both measurements and simulations are derived. New technology has made it possible to vary vehicle suspension parameters during vehicle ride. In order to prescribe different damping for different vehicle modes, modal motion estimates are needed. A system identification approach is suggested. It yields improved estimates of vehicle modal motion compared to previous work. / QC 20101221

In motorsport, simulating road vehicles driving at the limit of handling is a valuable tool to study and optimize their overall performance during the design and set-up phases. Along with Quasi-Steady-State optimization, optimal control (OC) is the most utilized technique to simulate the control and states of a vehicle during minimum-time maneuvers and has been used for offline lap-time optimization for more than twenty years now. Since the first applications of optimal control in this field, it has been clear that the solution of the minimum-time optimization does not represent a model of the human driver but instead substitutes him/her. However, the common points or divergences between the minimum-time strategy of human race drivers and the OC one are still unclear. Moreover, it seems that in the literature there is no agreement about what vehicle models must be used, and in general the choice of one model or the other is not clearly justified. Finally, thanks to the rise in popularity of autonomous driving and racing, optimal control has been used as path planner for automated vehicles: %nonetheless, the application of free-trajectory real-time nonlinear optimal control in Model Predictive Control (MPC) schemes, where the optimal controls are directly fed to the vehicle, is still an unexplored topic. nonetheless, the application of free-trajectory real-time nonlinear optimal control in Model Predictive Control (MPC) schemes, where the optimal controls are computed from a single optimization and directly fed to the vehicle, is a topic still open for exploration. The first aim of this thesis is to provide an objective comparison of several vehicle, tire, powertrain and road models to be used in minimum-time OC. In the first part of this work we thus detail several models of the vehicle and its subsystems. We then solve minimum-time OC problems on a series of test tracks adopting most of the model combinations and discuss the differences in the solutions. We then draw conclusions on the best model combinations to obtain realistic and reliable minimum-time maneuvers. The second part of the thesis aims to prove that the solutions of minimum-time OC problems are indeed different from the driving behavior of professional drivers, but they can be employed to coach the human driver and improve his/her racing performance. After modeling a high-performance vehicle manufactured by Ferrari, we again use optimal control to compute minimum-time maneuvers on two different tracks. A professional racer driving is then coached in following the OC strategy on the Ferrari driving simulator, and we objectively prove that the driver can outperform his previous lap times. In the third and last part of the thesis, we aim to prove that free-trajectory real-time optimal control is a valid alternative to hierarchical MPC frameworks based on high-level path planning and low-level path tracing. We first develop a novel kineto-dynamic vehicle model able to satisfy the trade-off between computational lightness and accuracy in representing the vehicle's pure and combined dynamics. Then, by solving a minimum-time OC in real-time, we are able to autonomously drive a real scaled vehicle around a track at the limits of tire adherence.

The goals of this thesis are to modify a full-scale personal hovercraft to perform autonomous maneuvers on flat ground, develop a first principles of the craft, and present data on the vehicle behavior in field trials. The hovercraft, initially designed for manual control by a rider, was modified both physically and with software to allow for remote and autonomous operation. The design leverages the actuator control solutions that are already implemented on the hovercraft for ease of installation and control. A key modification made in the design is the addition of auxiliary fans to increase overall thrust. Controller designs are presented to manage the rotation rate of the added fans. The purpose of the dynamic model is to assist in the design and evaluation of model-based controllers for the vehicle speed and heading. A first principles model was developed to give an approximate understanding of the vehicle's behavior. Data collected during field trials was used to challenge the assumptions made in the first principles model. Based on the field data, the model was updated to provide a better basis to evaluate model based controllers. Additionally, several key observations about the hovercraft performance were noted during the field trials. Controlling the vehicle heading is a nontrivial task and will require a responsive and authoritative controller / Master of Science / Hovercraft are useful vehicles because they can travel over many terrains, including water and land, without being impacted severely by friction. However, they also have several drawbacks including being difficult to steer and having insufficient thrust to scale a steep incline. To address these concerns, we present a design for a modified hovercraft that is capable of being steered with a remote control or with autonomy software. Additionally, eight fans were added to increase the overall thrust of the vehicle to allow it to drive uphill. A model of the hovercraft dynamics was made to allow us to study its behavior. Field trials were conducted to collect data on the hovercraft's performance from the onboard sensors. This data was used to improve the dynamic model so that it can be used in the future to decide the best control design for the hovercraft steering.

As the vehicle's autonomy level increases, new security systems are added to its functionality so accidents can be avoided. Those security systems can only be reliable and work effectively if an accurate estimation of the vehicle's velocity is available.  Given the importance of the estimation of velocity in vehicles, in this thesis, we used the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) to improve the velocity estimation of a heavy-duty dumper vehicle. Those methods were used to fuse the wheels' speed information and the Inertial Measurement Unit (IMU) readings available from the vehicle. A simulation model of the vehicle was created using Simulink which outputted the ground truth velocities that were used as a reference for comparison with the estimators when the vehicle went through different path patterns that included combinations of going straight, steering, and experiencing excessive wheel slip. Moreover, the sensors were simulated in Simulink as well and they provided the data that was used by the MATLAB scripts that coded the EKF and the UKF. The performance of the estimators was compared with the ground truth velocities by calculating the Root Mean Squared Error (RMSE) in each case. The results from the experiments showed that both the EKF and the UKF performed the same for the used simulation model, however, both improved the velocity estimation by decreasing the RMSE values from 0.46 (estimation using only IMU information) and 0.226 (estimation based only on wheels information) to 0.20. This is evidence that the Kalman Filter variations are a good option to test when the task is estimating the velocity of a vehicle.

A significant number of studies address various aspects related to tire modeling; most are dedicated to the development of tire models for on-road conditions. Such models cover a wide range of resolutions and approaches, as required for specific applications. At one end of the spectrum are the very simple tire models, such as those employed in real-time vehicle dynamic simulations. At the other end of the spectrum are the very complex finite element models, such as those used in tire design. In between these extremes, various other models have been developed, at different levels of compromise between accuracy and computational efficiency. Existing tire models for off-road applications lag behind the on-road models. The main reason is the complexity added to the modeling due to the interaction with the soft soil. In such situations, one must account for the soil dynamics and its impact on the tire forces, in addition to those aspects considered for an on-road tire. The goal of this project is to develop an accurate and comprehensive, while also efficient, off-road tire model for soft soil applications. The types of applications we target are traction, handling, and vehicle durability, as needed to support current army mobility goals. Thus, the proposed approach is to develop a detailed semi-analytical tire model for soft soil that utilizes the tire construction details and parallels existing commercially available on-road tire models. The novelty of this project relies in developing a three-dimensional three-layer tire model employing discrete lumped masses and in improving the tire-soil interface model. This will be achieved by enhancing the resolution of the tire model at the contact patch and by accounting for effects and phenomena not considered in existing models. / Master of Science

A racing vehicle suspension system is a kinematic linkage that supports the vehicle under complex loading scenarios. The suspension also defines the handling characteristics of the vehicle. Understanding the loads that the suspension carries in a variety of loading scenarios is necessary in order to properly design a safe and effective suspension system. In the past, the Formula SAE team at Virginia Tech has used simplified calculations to determine the loads expected in the suspension members. This approach involves several large assumptions. These assumptions have been used for years and the justification for them has been lost. The goal of this research is to determine the validity of each of the assumptions made in the method used for calculating the vehicle suspension loads by hand. These assumptions include modeling the suspension as pinned-pinned truss members to prevent bending, neglecting any steering angle input to the suspension, and neglecting vertical articulation of the system. This thesis presents an approach to modeling the suspension member loads by creating a finite element (FE) model of the entire suspension system. The first stage of this research covers the validation of the current calculation methods. The FE model will replicate the suspension with all of the current assumptions and the member loads will be compared to the hand calculations. This truss-element-based FE model resulted in member loads identical to the hand calculations. The next stage of the FE model development converts the truss model to beam elements. This step is performed to determine if the assumption that bending loads are insignificant is a valid approach to calculating member loads. In addition to changing the elements used from truss to beam element, the suspension linkage was adapted to more accurately model the methods by which each member is attached to the others. This involves welding the members of each control arm together at the outboard point as well as creating a simplified version of the pull rod mounting bracket on the upper control arm. The pull rod is the member that connects the ride spring, damper, and anti-roll bar to the wheel assembly and had previously been mounted on the upright. This model reveals reduced axial components of load but increases in bending moments sizable enough to reduce the resistance to buckling of any member in compression. The third stage of model development incorporates the steer angle that must be present in loading scenarios that involve some level of cornering. An analysis of the vehicle trajectory that includes the effects of slip angle is presented and used to determine the most likely steer angle the vehicle will experience under cornering. The FE model was adapted to include the movement of the steering linkage caused by driver input. This movement changes the angle of the upright and steering linkage as well as the angle at which wheel loads are applied to the suspension. This model results in a dramatic change in member loads for loading cases that involve a component of steering input. Finally, the FE model was further enhanced to account for vertical movement of the suspension as allowed by the spring and damper assembly. The quasi-static loading scenarios are used to determine any member loading change due to vertical movement. The FE model is also used to predict the amount of vertical movement expected at the wheel center. This data can be used by the suspension designer to determine if changes to the spring rate or anti-roll bar stiffness will result in a more desirable amount of wheel movement for a given loading condition. This model shows that there is no change in the member loads due to the vertical movement of the wheel. This thesis concludes by presenting the most important changes that must occur in member load calculations to determine the proper suspension loading under a variety of loading scenarios. Finally, a discussion of future research is offered including the importance of each area in determining suspension loads and recommendations on how to perform this research. / Master of Science

This thesis aims at estimating vital vehicle states and developing control algorithms for automotive suspensions and vehicle stability. A parametric model of an automotive monotube damper is developed and several control algorithms for semi-active suspensions have been developed. An extensive comparison of different control algorithms has been done. Skyhook, Groundhook, Hybrid, Acceleration-driven, Power-driven, Groundhook-linear, Linear Quadratic Regulator (LQR) optimal, Genetic algorithm optimized Linear Quadratic Regulator optimal, Model-reference adaptive, H∞ robust, µ-synthesis, fuzzy-logic based, and Deep Reinforcement learning based control algorithms have been developed and simulated. A shock dyno is instrumented and skyhook and groundhook control algorithms have been implemented as well. In addition to this, a semi-active suspension switching based control algorithm is developed for reducing the effort of a direct moment yaw rate controller, and improve stability of a vehicle when turning. Accelerometer based intelligent tires have been used to estimate vehicle states like vertical load on tire, velocity of the vehicle, unsprung mass acceleration, and forces on a tire. All these estimations would be helpful in observing various parameters of a vehicle using data from only a tri-axis accelerometer inside the tire. Data was collected in an instrumented Volkswagen Jetta and a Trailer setup as well. The test vehicle was instrumented with a tri-axis accelerometer inside the tire, encoder, Inertial Measurement Unit (IMU), and VBOX Racelogic Global Positioning System (GPS) based velocity measurement unit. For payload estimation, the data collected by the in-tire accelerometer was converted into frequency domain using Welch's method of averaging, followed by feature extraction. The extracted features were fed to a trained bagged trees model. Root mean squared error of 11% was observed on the test dataset. For velocity estimation, the data collected by the accelerometer was fed to a variational mode decomposition process. The extracted mode was converted to time-frequency domain using Hilbert transform and features for machine learning were extracted. A root mean squared error of 1.02kmph was observed on the trained dataset. A Gaussian process model was trained for this application. For unsprung mass acceleration estimation, the test vehicle was instrumented with an accelerometer near the wheel spindle as well. For this estimation problem, Convolutional neural networks (CNN) were used. The time-frequency spectrogram of x, y, and z axis data of the in-tire accelerometer were considered as the three color channels of an image. With this, an image of 224 x 224 x 3 dimensions was generated, which represented the time and frequency variation of data. These images were used for training the CNN and a 96.8% coefficient of correlation was obtained for this regression task. For the last wheel force estimation problem, the concept of training the images generated by overlapping time-frequency matrices was used and an accuracy of 90.1% was achieved. With these estimation of vehicle states, better control algorithms can be developed and deployed for better handling, safety and comfort of vehicles using data from only tri-axis accelerometer in the tire. / Master of Science / The main objective of this thesis is to aid in the development of better control systems for vehicles, using data from accelerometer-based intelligent tire. Payload on the vehicle's tire, vehicle velocity, wheel acceleration, and wheel forces are vital parameters, which if estimated correctly can be instrumental in having better understanding of the vehicle's condition. A tri-axis accelerometer is mounted inside the tire, and is used for estimating these vehicle parameters. Statistical models are developed based on features extracted from the accelerometer data. The main challenge was to use the data collected by only intelligent tire to estimate vehicle states. This makes the developed algorithms independent of other sensors and hence economic. Tires are the only component which serve as a link between the vehicle and road. Hence, these parameter estimations can be accurately observed simultaneously using the in-tire accelerometer data. Testing is done on an instrumented trailer-test setup and a Volkswagen Jetta. The vehicle is instrumented with the intelligent tire, a Global positioning system (GPS) based velocity measuring unit, Inertial measurement unit (IMU), and encoder. Testing is done for different loading conditions, road surfaces, inflation pressures, and vehicle velocities. In this way, it has been attempted to make the developed statistical models robust and expose them to a multitude of test conditions. In addition to this, several suspension semi-active control algorithms have been developed for improving vehicle ride comfort and road holding. A parametric damper model has been developed, and several control algorithms have been simulated. A shock dyno experimental setup has been instrumented and some of the control algorithms have been implemented. With this, several suspension semi-active control algorithms have been developed, and statistical models have been developed for estimation of various vehicle parameters. This research can be helpful for developing accurate control algorithms for active safety systems in a vehicle.

This Bachelors Thesis, conducted together with Öhlins racing AB, aims to develop a deeper understanding of the vehicle dynamics of snowmobiles, focusing particularly on the behavior of the rear suspension under various motions and applied forces. The rear suspension called the bogie, consists of several linkages, springs, and dampers whose geometry and parameters influence the movement of the bogie. The study aims to attain industry-standard knowledge of snowmobile dynamics by developing a simulation model in Matlab to further understand and examine the behavior of the bogie during heave and pitch, as well as consulting with professionals in snowmobile simulation to understand industry practices.The simulation model is built on two main components, the kinematic- and the dynamic calculations. The kinematics is determined by measuring existing snowmobiles to get data on how the points in the bogie are distanced. Subsequently, the motion of the bogie during heave is calculated by iteratively lifting the rail a small distance upwards from the initial points determined by the measurements. The motion will be dictated by the center arm since the rail is not allowed to rotate and the center arm can't be compressed. The dynamics of the bogie are then modeled by integrating springs to examine how the application of force varies throughout the motion. This, together with the forces exerted on the rail by the arms will result in a net heave force which is the force that is needed to initiate the heave motion.The heave simulation reveals that the application point of the heave force shifts forward during the compression of the bogie, a behavior that positively impacts the vehicle's turning ability by effectively shortening the wheelbase. Additionally, the motion ratios for the center and rear springs were analyzed, showing distinct variations. The motion ratio analysis for the center spring revealed only slight variations in the front, whereas the rear motion ratio exhibited substantial changes due to the rear arm and spring moving in opposite directions.Limitations of the study include the absence of empirical validation as well as simplifications of the suspension linkage, specifically the exclusion of coupled mechanisms. Future work should involve simultaneous pitch and heave movements and incorporate feedback from professional snowmobile drivers to refine the suspension settings. The insights gained from these simulations can guide the design of more efficient and responsive snowmobile bogies, ultimately enhancing the vehicle's performance and safety.

A comprehensive evaluation of roll and yaw stability of tractor/semitrailers with single and dual trailers in city and highway conditions is conducted. Commercial vehicles fundamentally behave differently in city driving conditions than at high speeds during highway driving conditions. In order to closely examine each, this study offers two distinct evaluations of commercial vehicles: 1) low-speed driving in tight turns, representative of city driving; and 2) high-speed lane change and evasive maneuvers, typical of highway driving. Specifically, for city driving, the geometric parameters of the roadway in places where tight turns occur—such as in roundabouts—are closely examined in a simulation study in order to evaluate the elements that could cause large vehicle body lean (or high rollover index), besides the truck elements that have most often been studied. Two roundabout geometries, 140-ft single-lane and a 180-ft double-lane, are examined for various truck load conditions and configurations. The vehicle configurations that are considered are a straight 4x2 truck, a tractor with a 53-ft semi-trailer (commonly known as WB-67), and two trucks in double-trailer configurations. Five potential factors are identified and thoroughly studied: circulatory roadway cross-section, roundabout tilt, truck configurations, truck apron geometry, and truck load condition. The results of the study indicate that when the rear axles of the trailer encounter the truck apron in the roundabout, the climbing and disembarking action can cause wheel unloading on the opposite side, therefore significantly increasing the risk of rollover. Interestingly, in contrast to most high-speed rollovers that happen with fully-loaded trailers, at low speeds, the highest risks are associated with lightly loaded or unloaded trucks. For high-speed driving conditions, typical of highway driving, a semi-truck with a double 28-ft trailer configuration is considered, mainly due to its increasing use on U.S. roads. The effect of active safety systems for commercial vehicles, namely Roll Stability Control (RSC) for trailers and Electronic Stability Control (ESC) for the tractor, is closely examined in a test study. Various trailer loading possibilities are evaluated for different combinations of ESC/RSC on the tractor and trailer, respectively. The results of the study indicate that 1) RSC systems reduce the risk of high-speed rollovers in both front and rear trailers, 2) the combination of ESC (on tractor) and RSC (on trailer) reduce the risk of rollover and jackknifing, and 3) RSC systems perform less effectively when the rear trailer is empty. / PHD / Traffic accidents involved with heavy trucks are more likely to result in fatality, excessive property damage, and traffic congestion. Unfortunately, heavy trucks commonly have lower stability than passenger cars due to heavy axle load and high center of gravity, which means they are easier to roll over or lose control. Therefore, it is necessary for us to understand the dynamics of heavy trucks in order to improve their stability and reduce the likelihood of severe accidents. Because heavy trucks are commonly operated for freight transport, they are subjected to two different driving conditions. When a truck is used within an urban area, it will be driven at low speeds and will need to negotiate tight turns, such as those normally seen at city traffic intersections and roundabouts. In this condition, the tight turns and roadway geometry (i.e. curb, truck apron, etc.) can considerably increase the likelihood of truck rollovers. On the contrary, non-collision accidents like rollovers that happen to heavy trucks during highway driving, where there are no tight turns or significant roadway input, are commonly due to the unstable dynamics of trucks rather than external excitation. This is because heavy trucks are more prone to exhibiting unstable dynamics at high speeds, especially when performing quick and aggressive maneuvers, such as those applied when changing lanes or avoiding an obstacle on the road. In this dissertation, the dynamic stability of heavy trucks in both driving conditions are evaluated. For low-speed conditions, a simulation study is conducted to learn how roadway geometry and truck elements affect the likelihood of rollovers during city driving. For high-speed conditions, a test study is performed to investigate how active safety systems reduce the likelihood of heavy truck rollovers and other non-collision accidents during highway driving. This dissertation provides valuable information for researchers or engineers who are interested in urban traffic design, heavy truck dynamics, and active safety systems for commercial vehicles.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Location-Aware Adaptive Vehicle Dynamics (LAAVD) System is being developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive, not reactive. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin (PM), to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the PM in a manner that is minimally intrusive to the driver's control authority. This system depends heavily on an understanding of the interplay between the vehicle's longitudinal, lateral, and vertical forces, as well as their resulting moments. These vehicle dynamics impact the PM metric and ultimately the point at which the Intervention Strategy will modulate the throttle and brake controls. Real-time implementation requires the development of computationally efficient predictive models of the vehicle dynamics. In this work, a method for predicting future vehicle states, based on current states and upcoming terrain, is developed using perturbation theory. An analytical relationship between the change in the spindle forces and the resulting change in the PM is derived, and the inverse relationship, between change in PM and resulting changes in longitudinal forces, is modeled. This model is implemented in the predictor-corrector algorithm of the Intervention Strategy. Corrections to the predicted states are made at each time step using a detailed, full, non-linear vehicle model. This model is run in real-time and is intended to be replaced with a drive-by-wire vehicle. Finally, the impact of this work on the automotive industry is discussed and recommendations for future work are given. / Master of Science