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In searching for more efficient vehicles with lower carbon emissions, researchers have invested enormous time and resources in designing new materials, components, systems, and control methods. The result is not only an immense volume of publications and patents but also a true electrification revolution in the transportation sector. Although the advancements are remarkable, much is still to be developed. Energy management systems are often designed to fulfil drive cycles that represent just a fraction of the actual use of the vehicles, disregarding essential factors such as driving conditions that may vary in real life. Furthermore, control algorithms should not ignore one of the most relevant driving aspects, comfort. Driving should be a pleasant activity since we spend much time of our lives performing this task. This research proposes a novel algorithm for managing energy consumption in electrified vehicles, the regen-based equivalent consumption minimization strategy (R-ECMS). Its suitability for solving the power-split problem is evaluated. Experiments emulating labelling schedules are conducted considering an example application. Robustness to different drive cycles and flexibility of the algorithm to various modes of operation are assessed. Furthermore, the method is integrated into an autonomous longitudinal control. The function leverages vehicle dynamics and journey mapping to assure energy efficiency and adequate drivability. Finally, the tests are conducted using human-driven cycles leveraging driving simulation technology. That allows for including driver subjective feelings in the design and assessing the algorithm's performance in realistic driving conditions. / Thesis / Doctor of Philosophy (PhD)

The aim of this thesis work is to investigate the possibility of applying a sensor fusion algorithm with a focus on estimating vehicle dynamic states, mainly the vehicle body accelerations. Modern passenger vehicles have several mechatronic systems such as active safety, comfort, driver assistance etc., which are highly dependant on accurate knowledge of such states. This work focuses on the mechatronic suspension system, which makes use of the body accelerations measurements to control the dynamics of the vehicle body in order to provide an improved driving experience. This work can be split up into two major parts, the first being the identification of available onboard sensors for measuring the vehicle body accelerations. Five different sensor combinations are considered and compared with each other. The next part is to develop a sensor fusion algorithm, in this case, a Kalman Filter (KF) based algorithm, which uses vehicle dynamic modelling knowledge to obtain accurate, reliable and less uncertain estimates of the states. Specifically, an Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) were built and compared with each other. Two different vehicle dynamic models, a vehicle planar dynamic model and a full car suspension model, were implemented to capture both the effects of road disturbances and drivingmanoeuvres on the vehicle body dynamics. Both these fusion algorithms were tested using simulation data and logged data and validated by comparing with an ideal sensing method to measure the body accelerations used currently at Volvo Car Corporation. / Syftet med detta examensarbete är att undersöka möjligheten att tillämpa en sensorfusionsalgoritm med fokus på att uppskatta fordonets dynamiska tillstånd, främst karossens acceleration. Moderna personbilar har flera mekatroniska system som aktiv säkerhet, komfort, förarassistans etc., som är mycket beroende av exakt kunskap om sådana tillstånd. Detta arbete fokuserar på det mekatroniska fjädringssystemet, som använder karossens accelerationsmätningar för att styra fordonets dynamik och för att ge en förbättrad körupplevelse. Detta arbete kan delas upp i två huvuddelar, den första är identifiering av tillgängliga inbyggda sensorer för mätning av fordonets accelerationer. Fem olika sensorkombinationer övervägs och jämförs med varandra. Nästa del är att utveckla en sensorfusionsalgoritm, i detta fall en kalmanfilter baserad algoritm, som använder kunskap om fordonets dynamik för att få exakta, pålitliga och mindre osäkra uppskattningar av tillstånden. Specifikt byggdes en UKF och CKF som jämfördes med varandra. Två olika fordonsdynamiska modeller, en plan dynamisk modell och en full hjulupphängningsmodell, implementerades för att fånga både effekterna av vägstörningar och körmanövrer på fordonets karossdynamik. Båda dessa fusionsalgoritmer testades med hjälp av simuleringsdata och loggade data och validerades genom att jämföra med en idealisk avkänningsmetod för att mäta karossaccelerationerna som används för närvarande på Volvo Car Corporation.

This thesis presents the development of a six degree of freedom nonlinear dynamic model for a single-mast fully submerged hydrofoil vehicle. The aim of the model is to aid in evaluating various model-based controllers for autonomous operation by simulating their performance before implementation in the field. Initially, first principles approach is employed to develop an approximate dynamic model of the vehicle. Prediction of the vehicle motion using the first principles model is then compared with the data from the tow tank experiments to assess the accuracy of the assumptions made in estimating the hydrofoil performance. Additionally, the dynamic model is adjusted to reflect the measured hydrodynamic forces in the tow tank tests. Utilizing the modified dynamic model to simulate the vehicle motion, an initial height controller is designed and tuned in field trials until stable foiling state was achieved. We evaluate the field results and discuss the limitation of employing steady-state tow tank data in establishing the vehicle dynamic model. / Master of Science / This thesis presents the development of a model describing the motion of a hydrofoil vehicle. The craft uses hydrofoils which act like conventional airplane wings that work in water instead of air to lift the hull fully out of the water. In order to maintain a set height above the water and direction of travel, the vehicle needs some form of a controller for autonomous operation. The purpose of the vehicle model is to aid in development of these controllers by simulating and evaluating their performance before implementation in the field. Initially, forces acting on the vehicle are approximated using fundamental hydrodynamic theory. The theoretical model is then compared with experimental data to assist in characterization of the hydrofoils. Building upon the measured test data, we create a preliminary height controller in simulation and conduct field trials to achieve stable foiling state.

Human expert drivers have the unique ability to build complex perceptive models using correlated sensory inputs and outputs. In the case of longitudinal vehicle traction, this work will show a direct correlation in longitudinal acceleration to throttle input in a controlled laboratory environment. In fact, human experts have the ability to control a vehicle at or near the performance limits, with respect to vehicle traction, without direct knowledge of the vehicle states; speed, slip or tractive force. Traditional algorithms such as PID, full state feedback, and even sliding mode control have been very successful at handling low level tasks where the physics of the dynamic system are known and stationary. The ability to learn and adapt to changing environmental conditions, as well as develop perceptive models based on stimulus-response data, provides expert human drivers with significant advantages. When it comes to bandwidth, accuracy, and repeatability, automatic control systems have clear advantages over humans; however, most high performance control systems lack many of the unique abilities of a human expert. The underlying motivation for this work is that there are advantages to framing the traction control problem in a manner that more closely resembles how a human expert drives a vehicle. The fundamental idea is the belief that humans have a unique ability to adapt to uncertain environments that are both temporal and spatially varying. In this work, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy. The proposed anthropomimetic traction control algorithm operates on the same correlated input signals that a human expert driver would in order to maximize traction. A gradient ascent approach is at the heart of the proposed anthropomimetic control algorithm, and a real-time implementation is described using linear operator techniques, even though the tire-ground interface is highly non-linear. Performance of the proposed anthropomimetic traction control algorithm is demonstrated using both a longitudinal traction case study and a combined mode traction case study, in which longitudinal and lateral accelerations are maximized simultaneously. The approach presented in this research should be considered as a first step in the development of a truly anthropomimetic solution, where an advanced control algorithm has been designed to be responsive to the same limited input signals that a human expert would rely on, with the objective of maximizing traction. This work establishes the foundation for a general framework for an anthropomimetic control algorithm that is capable of learning and adapting to an uncertain, time varying environment. The algorithms developed in this work are well suited for efficient real time control in ground vehicles in a variety of applications from a driver assist technology to fully autonomous applications. / Ph. D.

Successful race teams rely heavily on track testing to search for the ideal suspension setup. As more restrictions are placed on the amount of on-track testing by major racing sanctioning bodies, such as NASCAR, teams have increased their attention to alternate testing methods to augment their track data and better understand the dynamics of their racecars. One popular alternate to track testing is 8-post dynamic shaker rig testing. Eight-post rig testing gives the team a better understanding of the vehicle's dynamics before they arrive at the race track, allowing them to use their limited track testing time more efficiently. While 8-post rig testing certainly is an attractive option, an extensive test matrix is often required to find the best suspension setups. To take full advantage of 8-post rig tests, more efficient experimental methods are needed. Since investigating shock absorber selection is often the most time-consuming task, this study focuses on developing more efficient methods to select the best shock absorber setups. This study develops a novel method that applies dynamic substructuring and system identification to generate a mathematical model that predicts the results of future tests as both command inputs and components are changed. This method is used to predict the results of 8-post rig tests as actuator commands and shock absorber forces are varied. The resulting model can then be coupled with shock absorber models to simulate how the vehicle response changes with shock absorber selection. This model can then be applied to experimental design. First, a physically-motivated nonlinear dynamic shock absorber model is developed, suitable for quickly fitting experimental data and implementing in simulation studies. Next, a system identification method to identify a vehicle model using experimental data is developed. The vehicle model is then used to predict response trends as shock absorber selection is varied. Comparison of simulation and experimental results show that this model can be used to predict the response levels for 8-post rig tests and aid in streamlining 8-post rig testing experimental designs. / Ph. D.

The design of nonlinear adaptive control systems for reducing vibration transmission in applications such as transportation systems is discussed. The systems studied include suspension systems, such as those used in vehicles, employing nonlinear magneto-rheological (MR) dampers that are controlled to provide improved vibration isolation. Magneto-rheological dampers use a novel class of smart fluid whose apparent viscosity changes as it is exposed to a magnetic field. The developed adaptive control scheme is designed to deal with the nonlinearities and uncertainties that commonly arise in most suspension applications. Some of the nonlinearities that are considered include time-varying characteristics, displacement-dependent effects, and hysterisis damping of magneto-rheological dampers. The uncertainties include mass and stiffness variations that can commonly occur in a suspension system. A number of nonlinear analytical models are developed and used in numerical simulation to evaluate the validity and effectiveness of the developed adaptive controllers. Further, the results of the numerical study are used in an experimental evaluation of the controllers on a seat suspension for heavy vehicles. The analytical and experimental evaluation both indicate the effectiveness of the proposed adaptive control technique in controlling vibration transmission in the presence of both system nonlinearities and uncertainties. The manuscript will provide a detail account of the modeling, dynamic analysis, adaptive control development, and testing that was performed throughout this study. / Ph. D.

The effect of center of gravity (CG) height and lateral and longitudinal off-centering on high-speed roll stability of A-double tractor trailers with 28-ft and 33-ft straight-rail and drop-frame trailers is evaluated through simulation and track testing. The changes in CG position due to the type of trailer (straight-rail vs. drop-frame) and laterally and longitudinally off-centered loads are considered. The simulation results show that imbalanced trailer loading induces roll instability and increases the likelihood of trailer rollover. Additionally, for equal loading conditions, the drop-frame trailers exhibit better roll stability than straight-rail trailers because of the lower CG. The simulation evaluation of 28-ft A-doubles is complemented with track testing of 33-ft trailers in alike (Drop-Drop and Straight-Straight) and mixed (Drop-Straight and Straight-Drop) arrangements of front and rear trailers, for various steering maneuvers that represent highway driving, such as exit ramp, obstacle avoidance, etc. The test trailers include specially designed load frames for emulating a loaded trailer in various loading conditions, outriggers for preventing trailer rollover, and durability structures for withstanding the torsional and bending moments resulting from the tests. Various sensors, including GPS, LiDAR units, accelerometers, string pots, and pressure transducers, are used, along with an onboard data acquisition (DAQ) system, for collecting the necessary data for post-analysis. Analysis of the test data indicates that the Drop-Drop configuration exhibits higher roll stability than the Straight-Straight configuration. For mixed trailers, the Drop-Straight configuration exhibits higher roll stability in exit ramps, but lower obstacle avoidance stability. Equipping the trailers with a roll stability control (RSC) system improves roll stability in terms of increasing the rollover threshold speed and tolerating more aggressive lane change steering maneuvers for A-doubles in various conditions. The RSC performance increases further when the brake application is synchronized between the two trailers to account for any lateral dynamic delay that naturally occurs. A novel interconnected RSC system is proposed to eliminate the lag between the RSC modules with a new control logarithm. The proposed RSC system increases the trailers' roll stability by 16% when compared with independent RSC systems that are commonly used for A-doubles. / Doctor of Philosophy / Commercial trucks play an indispensable role in transporting goods in society. A large percentage of the goods that we use daily or are delivered to our homes are transported on the nation's highways. Most often, the average automobile driver notices the presence of trucks on highways, at times with a bit of disdain. The public's perception appears to be formed by the fact that accidents involving commercial trucks are more publicized because they can cause more property damage, injuries, or even fatalities. The primary thrust of this research is to make the nation's highways safer by offering a better understanding of the dynamics of trucks with double trailers that are operated with a higher frequency on public highways. The double trailer configuration is often favored because of its larger cargo capacity and high modularity. However, their roll dynamics are not as well understood as the conventional tractor-semitrailers. Understanding the dynamics of double-trailer trucks is undoubtedly the very first step toward preventing or reducing the traffic accidents caused by rollovers. This study provides detailed analysis of roll dynamics for double trailers with imbalanced payloads. It also evaluates the effect of different types of trailers, such as drop-frame trailers (those with a "belly" in the mid-section of the trailer) and straight-rail trailers (those without a "belly") on their rollover propensity. The commercialized RSC system is evaluated for its effectiveness on the double-trailer truck. The evaluations are based on over 1,000 sets of tests in highly controlled conditions at the Transportation Research Center (TRC), a special facility for vehicle dynamic assessment in East Liberty, Ohio. It is found that the rollover dynamics of trucks with double trailers can be improved by having an awareness of the most favorable trailer arrangements according to their types of trailers and type of steering (exit-ramp or obstacle avoidance). In addition, this study provides the analysis of the commercialized RSC system for its effectiveness on the double-trailer truck. Lastly, a novel RSC system is proposed to further improve the effectiveness of the original RSC system.