<div> In recent decades as environmental concerns and the cost and availability of fossil fuels have become more pressing issues, the need to extract more work from each drop of fuel has increased accordingly. Electrification has been identified as a way to address these issues in vehicles powered by internal combustion engines, as it allows existing engines to be operated more efficiently, reducing overall fuel consumption. Two applications of electrification are discussed in the work presented: a series-electric hybrid powertrain from an on-road class 8 truck, and an electrically supercharged diesel engine for use in the series hybrid power system of a wheel loader.</div><div> </div><div> The first application is an experimental powertrain developed by a small start-up company for use in highway trucks. The work presented in this thesis shows test results from routes along (1) Interstate 75 between Florence, KY, and Lexington, KY, and (2) Interstates 74 and 70 east of Indianapolis, during which tests the startup collected power flow data from the vehicle's motor, generator, and battery, and three-dimensional position data from a GPS system. Based on these data, it was determined that the engine-driven generator provided an average of 15% more propulsive energy than required due to electrical losses in the drivetrain. Some of these losses occured in the power electronics, which are shown to be 82% - 92% efficient depending on power flow direction, but the battery showed significant signs of wear, accounting for the remainder of these electrical losses. Overall, most of the system's fuel savings came from its regenerative braking capability, which recaptured between 3% and 12% of the total drive energy output. Routes with significant grade changes maximize this energy recapture percentage, but it is shown minimizing drag and rolling resistance with a more modern truck and trailer could further increase this energy capture to between 8% and 18%.</div><div> </div><div> In the second application, an electrified air handling system is added to a 4.5L engine, allowing it to replace the 6.8L engine in John Deere's 644K hybrid wheel loader. Most of the fuel savings arise from downsizing the engine, so in this case an electrically driven supercharger (eBooster) allows the engine to meet the peak torque requirements of the larger, original engine. In this thesis, a control-oriented nonlinear state space model of the modified 4.5L engine is presented and linearized for use in designing a robust, multi-input multi-output (MIMO) controller which commands the engine's fueling rate, eBooster, eBooster bypass valve, exhaust gas recirculation (EGR) valve, and exhaust throttle. This integrated control strategy will ultimately allow superior tracking of engine speed, EGR fraction, and air-fuel ratio (AFR) targets, but these performance gains over independent single-input single-output control loops for each component demand linear models that accurately represent the engine's gas exchange dynamics. To address this, a physics-based model is presented and linearized to simulate pressures, temperatures, and shaft speeds based on sub-models for exhaust temperature, cylinder charge flow, valve flow, compressor flow, turbine flow, compressor power, and turbine power. The nonlinear model matches the truth reference engine model over the 1200 rpm - 2000 rpm and 100 Nm - 500 Nm speed and torque envelope of interest within 10% in steady state and 20% in transient conditions. Two linear models represent the full engine's dynamics over this speed and torque range, and these models match the truth reference model within 20% in the middle of the operating envelope. However, specifically at (1) low load for any speed and (2) high load at high speed, the linear models diverge from the nonlinear and truth reference models due to nonlinear engine dynamics lost in linearization. Nevertheless, these discrepancies at the edges of the engine's operating envelope are acceptable for control design, and if greater accuracy is needed, additional linear models can be generated to capture the engine's dynamics in this region.</div>

<div>With the upsurge of greenhouse gas emissions and rapid depletion of fossil fuels, the pressure on the transportation industry to develop new vehicles with improved fuel economy without sacrificing performance is on the rise. Hybrid Electric Vehicles (HEVs), which employ an internal combustion engine as well as an electric motor as power sources, are becoming increasingly popular alternatives to traditional engine only vehicles. However, the presence of multiple power sources makes HEVs more complex. A significant task in developing an HEV is designing a power management strategy, defined as a control system tasked with the responsibility of efficiently splitting the power/torque demand from the separate energy sources. Five different types of power management strategies, which were developed previously, are reviewed in this work, including dynamic programming, equivalent consumption minimization strategy, proportional state-of-charge algorithm, regression modeling and long short term memory modeling. The effects of these power management strategies on the vehicle performance are studied using a simplified model of the vehicle. This work also proposes an original power management strategy development using a genetic algorithm. This power management strategy is compared to dynamic programming and several similarities and differences are observed in the results of dynamic programming and genetic algorithm. For a particular drive cycle, the implementation of the genetic algorithm strategy on the vehicle model leads to a vehicle speed profile that almost matches the original speed profile of that drive cycle.</div>

<p>Alternate power sources in automotive class-8 trucking industry is a major focus of research in recent days. Green house gasses, oxides of Nitrogen(NOx), Oxides of Sulphur(SOx), hydrocarbons and particulate matter are major concerns contributing to the shift in alternate fuel strategies. Another direct relation to move to an alternate power strategy is the reduction in net fuel consumption which in turn implicitly improves the emission components.</p> <p>A holistic approach is needed while designing a modern class-8 vehicle. A variety of system architecture, control algorithms, diagnostic levers are needed to be manipulated to achieve the best of blends amongst Total Cost of Ownership (TCO), Drivability, Fuel</p> <p>Economy, Emissions Compliant, Hauling Capacity, etc. The control and system levers are not mutually exclusive and there is a strong correlation amongst all these control and system components. In order to achieve a consensus amongst all these levers to achieve a common objective, is a challenging and complex problem to solve. It is often required to shift the algorithm strategy to predictive information based rather than reactive logic. Predictively modulating and manipulating control logic can help with better fuel efficient solution along with emissions improvement. A further addition to the above challenge is when we add a fleet of vehicle to the problem. So, the problem now is to optimize a control action for a fleet</p> <p>of vehicles and design/select the correct component size. A lot of research has been done and is still underway to use a 48V hybrid system with a small battery using a simple charge sustaining SOC control strategy. This will make the system light enough not to compromise on the freight carrying capacity as well as give some extra boost during the high torque requirement sections in the route for a better fuel and emissions efficient solution. In this work a P2 type 48V hybrid system is used which is side mounted to the transmission via a gear system. The selection of the system and components enables the usage of different control strategies such as neutral coasting and Engine off coasting. This architecture with a traditional 12-15L Internal combustion engine along with a mild 48V hybrid system provides the most viable selection for a long haul class-8 application and is used in this work. It is also possible to identify other component sizes along with architectures for new configurations. The framework in this research work can help develop the study for different component sizing. While this research work is focused towards building a framework for achieving predictive control in a 3 truck platooning system using multi-agent based control, the other supporting work done also helps understand the optimal behavior of the interacting multiple controls when the corridor information such as road grade and route speed limit are known a-priori, in a single vehicle. The build up of this work analyzes an offline simulation of a 4 control optimal solution for a single hybrid truck and then extend the optimal controls to a 3 truck platoon. In the single truck, this research will help identify the interacting zones in the route where the various control actions will provide the best cost benefits which is fuel economy. These benefits are associated as a function of exogenous look ahead information such as grade and speed limit. Further it is also possible to identify the optimal behavior and the look ahead horizon required for achieving that. In other words the optimal behavior and benefits associated with the global solution can be accomplished by implementing rule based control system with a look ahead horizon of 2-5 km. If this would not have been the case then it is almost impossible to design a predictive controller based on the entire route information which can stretch up to hundreds of kilometers. Optimal algorithms of such prediction horizon are not feasible to be implemented in real time controllers. This research work will also help understand the interaction between different active control actions such as predictive speed modulation, gear shift, coasting and power split with passive control levers such as slow down due to hybrid regeneration, hybrid boost during coasting, etc. This will help in architecting a system involving component specifications, active optimal control, look ahead information, hybrid system strength, etc, working in close interaction with each other. Though we analyze these predictive behavior for a single vehicle as a supporting work the prime objective is to include these predictive levers in a platooning system using an agent based method. This multi-agent based technique will help analyze the behavior of multiple trucks in a platoon in terms of fuel efficient safe operation. The focus of this research work is to not directly come up with a controller or strategy but rather to understand the optimality of this control levers for a multi-vehicle platoon system given a look ahead information is available. The research shows that predictive information will help in gaining fuel economy for a platoon of class-8 mild hybrid trucks. It also highlights the challenges in doing so and what needs to be traded off in order to achieve the net fuel benefit.</p>

<p dir="ltr">Engine downsizing with the assistance of turbomachinery and/or energy storage system has been realized to be one of the most promising and cost-effective solutions in pursuit of cleaner and more efficient engine products. Fundamental challenges however, exist in terms of control and energy management of advanced powertrain featuring turbocharged engines due to their complex dynamics, inherent coupling nature, and strict emission regulations concerning environmental preservation. For the purpose of addressing those challenges, this dissertation develops an integrated framework for modeling, robust coordinated control, and power management of advanced powertrains featuring turbocharged engines.</p><p dir="ltr">This dissertation first studies an advanced turbocharged lean-burn SI natural gas engine manufactured by Caterpillar, and develops an intuitive physics-based, control-oriented model. The obtained control-oriented model is validated against a high-fidelity truth-reference model and serves as the basis on which a robust coordinated control system is developed. The dissertation then proposes a comprehensive procedure for synthesizing a robust coordinated control system applying optimization-based H_infinity control theory. Specifically, this framework outlines a methodology of modeling uncertainties to account for system robustness, and providing valuable insights into the tuning of general coordinated control system design. For performance testing, the synthesized robust coordinated control system is implemented on the high-fidelity truth-reference model. A parallel closed-loop simulation strategy is adopted so that direct comparison between the robust coordinated control system and benchmark production control system (composed of multiple fine-tuned PID controllers) developed by Caterpillar can be carried out. Simulation results manage to demonstrate the merit of utilizing the robust coordinated control system, with better performances observed in terms of steady-state tracking, transient response, and disturbance attenuation.</p><p dir="ltr">The second part of this dissertation focuses on the development of a proposed novel hybrid electric wheel loader which features a downsized engine assisted by turbocharger and an energy storage system. Research efforts documented in this dissertation involve system configuration, controller design (both component-level and supervisory-level), simulation development (both software-in-the-loop and hardware-in-the-loop) and simulated validation for the proposed novel wheel loader. Inspired by the successful simulation results, John Deere assembled a real demo vehicle with the proposed powertrain and conducted some in-field testing, from which encouraging experimental results are observed.</p>

<p>Binder jet printing (BJP) is an additive manufacturing (AM) method which has the potential to be applied to high annual volumes in the automotive industry. Binder jet printing provides an excellent opportunity to innovate transmission valve body components. The three-layer design and complex hydraulic control system channels of valve body housing formulated a new electro-hydraulic system with the brand-new features inherited from BJP. For the valve body, the features of BJP brought a revolutionary new idea for both the valves and hydraulic channel design. The spool valve was housed with a sleeve that integrates orifices and port controls. The hydraulic channel layout of the valve body assembly was greatly simplified and space-saving. The support components had also been replaced with a lightweight design while maintaining the same functionality. Integrated design of Binder jet print produced hydraulic automatic valve system presented an entirely new design, whose static performance was compared to that of the conventional 948TE ZF9HP48 transmission valve body. Similar performance indicated that a valve body design featuring BJP would have great potential for various industrial applications.</p>

The electrification of transportation is increasingly of interest to governments around the world as a means of contributing to the achievement of climate change goals. Transportation is a significant source of greenhouse gas emissions, but it is also the backbone of the global economy and local mobility. Electrification is widely seen as a promising pathway to reducing greenhouse gas emissions from transportation while continuing to support economic growth. Multiphase machines have distinctive features that draw attention in the transportation electrification domain due to their features. Recently, powertrains based on the current-source inverter (CSI) are getting more attention to be a more reliable structure for Electric Vehicles (EVs) by replacing the dc-link capacitor with a choke inductor. This thesis combines these two technologies to develop a more reliable, compact powertrain for heavy-duty electric vehicles. First, a survey covers the recent advances in several aspects such as topology, control, and performance to evaluate the possibility and the future of exploiting them more in EV applications. The six-phase drives are extensively covered here because of their inherent structure as a dual three-phase system which eases the production process. The survey presents the different topologies used in dual three-phase drives, the modulation techniques used to operate them, the status of using multiphase drives in traction applications industrially, and the upcoming trends toward promoting this technology. New powertrain configurations for heavy-duty electric vehicles (HDEV) are proposed based on current-source inverters (CSI) and asymmetrical six-phase electric machines. Since the six-phase CSI comprises two three-phase CSIs, multiple configurations can arise based on the connection between the two CSIs. In this context, the proposed powertrain configurations are based on parallel, cascaded, and standalone six-phase CSIs. The standalone topology is based on separating the two three-phase converters by supplying each converter with a dedicated dc-dc converter. A new and straightforward method is proposed to extend the six-phase standalone CSI. The proposed technique employs the vector space decomposition (VSD) to mitigate the inverter current harmonics and extend the linear modulation region by about 8%. For motor drive applications, increasing the fundamental output component can reflect higher torque production capability for the same drive size, given that thermal limits are not exceeded. Moreover, to increase the drive's reliability, space vector modulation (SVM) techniques are developed to operate the six-phase CSI while reducing the common-mode voltage (CMV) content associated with the switching of semiconductors. The SVM techniques select the switching states associated with the minimum CMV value offline to eliminate the need for measurements. Experimental validation of the proposed algorithms is presented to operate a scaled-down six-phase PMSM fed by the proposed powertrain configuration. These proposed techniques make the CSI- based powertrain a promising solution for future HDEVs in terms of cost, performance, and reliability. / Thesis / Candidate in Philosophy

No description available.

<p dir="ltr">This motorsport thesis explores the complete electrification of an IndyCar by simulations. Initial research was conducted on stock IndyCar specifications, and concurrently, a sequential approach was developed for MATLAB-based simulations to generate comprehensive results. The study aims to integrate extensive insights gained from courses such as Vehicle Dynamics, Aerodynamics, Data Acquisition, and Electric Powertrains, alongside practical experience from racing internships. The goal is to comprehend the impact of this conversion on engineering parameters. The analysis specifically emphasizes the engineering aspects, with a particular focus on the longitudinal dynamics of the vehicle through quarter-mile simulations.</p>

The transportation sector consumes around 70% of all petroleum in the US. In recent years, there have been improvements in the efficiency of the vehicles, and hybrid techniques that have been used to improve efficiency for conventional combustion vehicles. Hydraulic systems have been used as an alternative to conventional electric regenerative systems with good results. It has been proven that hydraulic systems can improve energy consumption in conventional combustion vehicles and in refuse collection vehicles. The control strategy has a large impact on the performance of the system and studies have shown the control strategy selection should be optimized and selected based on application. The performance of a hydraulic accumulator was compared with the performance of a set of ultracapacitors with the same energy storage capacity. The energy efficiency for the ultracapacitor was around 79% and the energy efficiency of the hydraulic accumulator was 87.7%. The power/mass ratio in the set of ultracapacitors was 2.21 kW/kg and 2.69 kW/kg in the hydraulic accumulator. The cost/power ratio is 217 US$/kW in the ultracapacitors and 75 US$/kW in the hydraulic accumulator. Based on these results, the hydraulic accumulator was selected as the energy storage device for the system. A testbench was designed, modeled, implemented to test the energy storage system in different conditions of operation. The experimental results of the testbench show how system can be actively controlled for different operating conditions. The operating conditions in the system can be adjusted by changing the number of rheostats connected to the electric generator. Different variables in the system were measured such as the angular shaft speed in the hydraulic pump, the torque and speed in the hydraulic motor, the pressure in the system, the flow rate, and the current and voltage in the electric generator. The control algorithm was successfully implemented, the results for the pressure in the system and the angular speed in the electric generator show how the control system can follow a desired reference value. Two different controllers were implemented: one controller for the pressure in the system, and one controller for the speed.

A complete chain of analysis and synthesis system identification tools for detectability assessment and adaptive identification of parameters with physical interpretation that can be found commonly in control-oriented powertrain models is presented. This research is motivated from the fact that future powertrain control and monitoring systems will depend increasingly on physically oriented system models to reduce the complexity of existing control strategies and open the road to new environmentally friendly technologies. At the outset of this study a physics-based control-oriented dynamic model of a complete transient engine testing facility, consisting of a single cylinder engine, an alternating current dynamometer and a coupling shaft unit, is developed to investigate the functional relationships of the inputs, outputs and parameters of the system. Having understood these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended algorithms is illustrated with three novel practical applications. These are, the development of an on-line health monitoring system for engine dynamometer coupling shafts based on recursive estimation of shaft’s physical parameters, the sensitivity analysis and adaptive identification of engine friction parameters, and the non-linear recursive parameter estimation with parameter estimability analysis of physical and semi-physical cyclic engine torque model parameters. The findings of this research suggest that the combination of physics-based control oriented models with adaptive identification algorithms can lead to the development of component-based diagnosis and control strategies. Ultimately, this work contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control for vehicular systems.