<div>The heavy duty transportation sector is projected to grow in the coming decades. Increasing the fuel economy of class 8 vehicles would simultaneously decrease CO2 emissions and decrease the annual fuel expenditures that account for nearly a quarter of cargo companies' annual budgets. Most technology that has aimed to do this has primarily been focused on either improvements in engine efficiency or reduction of aerodynamic drag. This thesis addresses a somewhat different approach: the optimization of vehicle dynamics in order to realize fuel savings. </div><div><br></div><div>Through partnerships with Peloton Technology and Cummins, tests and simulations were conducted on corridors with grades up to 5% that indicate fuel savings of up to 14.4% can be achieved through the combination of three strategies: two-truck platooning, long-horizon predictive cruise control (LHPCC), and simultaneous shifting. Two-truck platooning is the act of drafting a rear truck behind a front truck. It has been shown that this not only reduces the drag of the follow vehicle, but also that of the lead vehicle. LHPCC is an optimization of the lead truck's velocity over a given corridor to get "from point A to point B" in the most efficient way possible whilst doing so with a trip time constraint. Last is the use of simultaneous shifting, which allows the follow vehicle to maintain the proper platoon gap distance behind</div><div>the lead truck.</div>

<div>The United States Environmental Protection Agency (EPA) is charged with pro-tecting human health and the environment. Part of this mission involves regulating heavy-duty trucks that produce particulate matter (PM), unburned hydrocarbons (UHC), carbon dioxide (CO2), and nitrogen oxides (NOx). A byproduct of lean burn combustion in diesel engines is NOx. NOx output limits from commercial vehicles have been reduced significantly from 10 g/hp-hr in 1979 to 0.2 g/hp-hr in 2010. Ad-ditional reductions are expected in the near future.</div><div><br></div><div>One pathway to meet future NOx emissions regulations in a fuel efficient manner is with higher performing exhaust aftertreatment systems through improved engine air handling. As exhaust aftertreatment’s capability to convert harmful NOx into harmless N2 and H2O is a function of temperature, a key performance factor is how quickly does the exhaust aftertreatment system heat up (warm-up), and how well does the system stay at elevated temperatures (stay-warm).</div><div><br></div><div>When the warm-up strategy of iEGR was implemented over the heavy duty federal test procedure (HD-FTP) drive-cycle, it was able to get the SCR above the critical 250◦C peak NOx conversion threshold 100 seconds earlier than the TM baseline. While iEGR consumed 2.1% more fuel than the TM baseline, it reduced predicted tailpipe NOx by 7.9%.</div><div><br></div><div>CDA implemented as a stay-warm strategy over the idle portions of the HD-FTP successfully kept the SCR above the 250◦C threshold for as long as the TM baseline and consumed 3.0% less fuel. Implementing CDA both at idle and from 0 to 3 bar BMEP consumed an additional 0.4% less fuel, for a total fuel consumption reduction of 3.4%.</div><div><br></div><div>A method to predict and avoid compressor surge (which can destroy turbochargers and in fact did so during the HD-FTP experiments) instigated by CDA was devel-oped, as discussed later, and implemented with staged cylinder deactivation to avoid compressor surge.</div><div><br></div><div>The literature does not consider the fidelity of road grade data required to ad-equately predict vehicle fuel consumption and operational behavior. This work ad-dresses this issue for Class 8 trucks by comparing predicted fuel consumption and operation (shifting, engine torque/speed, and braking) of a single Class 8 truck simu-lated with grade data for the same corridor from different sources. The truth baseline road grade (best fidelity available with LiDAR) was obtained previously. This work compares road grade data to the truth baseline from four other typical methods i) utilizing GPS to record horizontal position and vertical elevation, ii) logging the pitch of a cost effective, commercially available IMU, iii) integrating the horizontal and ver-tical velocities of the same IMU, and iv) a commercially available dataset (Comm). Comm grade data (R2=0.992) best matches the LiDAR reference over a 5,432 m stretch of US 231 where high quality LiDAR data was available, followed in quality by the integrated IMU velocity road grade (R2=0.979). Limitations of the Comm dataset are shown, namely missing road grade (decreased point density) for up to 1 km spans on other sections of US 231, as well as for Interstate 69. Vehicle simulations show that both the Comm data (where available and accurate) and integrated IMU road grade data result in fuel consumption predictions within 2.5% of those simulated with the truth reference grade data.</div><div><br></div><div>The simulation framework described in Chapter 6 combines high fidelity vehicle and powertrain models (from Chapter 5) with a novel production-intent platooning controller. This controller commands propulsive engine torque, engine-braking, or friction-braking to a rear vehicle in a two-truck platoon to maintain a desired following distance. Additional unique features of the framework include high fidelity road grade and traffic speed data. A comparison to published experimental platooning results is performed through simulation with the platooning trucks traveling at a constant 28.6 m/s (64 MPH) on flat ground and separated by 11 m (36 ft). Simulations of platooning trucks separated by a 16.7 m (54.8 ft) gap are also performed in steady-state operation, at different speeds and on different grades (flat, uphill, and downhill), to demonstrate how platooning affects fuel consumption and torque demand (propulsive and braking) as speed and grade are varied. For instance, while platooning trucks with the same 16.7 m gap at 28.6 m/s save the same absolute quantity of fuel on a 1% grade as on flat ground (1.00 per-mile, normalized), the trucks consume more fuel overall as grade increases, such that relative savings for the platoon average decrease from 6.90% to 4.94% for flat vs. 1% grade, respectively. Furthermore, both absolute and relative fuel savings improve during platooning as speed increases, due to increase in aerodynamic drag force with speed. There are no fuel savings during the downhill operation, regardless of speed, as the trucks are engine braking to maintain reasonable speeds and thus not consuming fuel. Results for a two-truck platoon are also shown for moderately graded I-74 in Indiana, using traffic speed from INDOT for a typical Friday at 5PM. A 16.7 m (54.8 ft) gap two-truck platoon decreases fuel consumption by 6.18% over the baseline without degradation in trip time (average speed of 28.3 m/s (63.3 MPH)). The same platooning trucks operating on aggressively graded I-69 in Indiana shows a lower platoon-average 3.71% fuel savings over baseline at a slower average speed of 24.5 m/s (54.8 MPH). The impact of speed variation over, and grade difference between, these realistic routes (I-74 & I-69) on two-truck platooning is described in detail.<br></div><div><br></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>

<div>Class 8 trucks on average drive the most miles and consume the most fuel of any major vehicle category annually. Indiana specifically is the fifth busiest state for commercial freight traffic and moves $750 billion dollars of freight annually, and this number is expected to grow by 60% by 2040. Reducing fuel consumption for class 8 trucks would have a significant benefit on business and the proportional decrease in CO₂ would be exceptionally beneficial for the environment.</div><div><br></div><div>Platooning is one of the most important strategies for increasing class 8 truck fuel savings. Platooning alone can help trucks save upwards of 7% platoon average fuel savings on flat ground. However, it can be difficult for a platooning controller to maintain a desired truck separation during uncoordinated shifting events. Using a high-fidelity simulation model, it is shown that simultaneous shifting–having the follow truck shift whenever the lead truck shifts (unless shifting would cause its engine to overspeed or underspeed)–decreases maximum truck separation by 24% on a moderately challenging grade route and 40% on a heavy grade route.</div><div><br></div><div>Model Predictive Control (MPC) of the follow truck is considered as a means to reduce the distance the follow truck falls behind during uncoordinated shifting events. The result in simulation is a reduction in maximum truck separation of 1% on a moderately challenging grade route and 19% on a heavy grade route. However, simultaneous shifting largely alleviates the need for MPC for the sake of tracking for the follow truck.</div><div><br></div><div>A different MPC formulation is considered to dynamically change the desired set point for truck separation for routes through a strategy called Route Optimized Gap Growth (ROGG). The result in simulation is 1% greater fuel savings on a moderately challenging grade route and 7% greater fuel savings on a route with heavy grade for the follow truck.</div>

Thesis (Ph.D. in Cell Biology, Stem Cells, & Development) -- University of Colorado Denver, 2008. / Typescript. Includes bibliographical references (leaves 138-155). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;

No description available.

<div>The commercial vehicle sector is an important enabler of the economy and is heavily dependent on fossil fuels. In the fight against climate change, reduction of emissions by improving fuel economy is a key step for the commercial vehicle sector. Improving fuel economy deals with reducing energy losses from fuel to the wheels. This study aims to analyze efficiency improvements for two systems that are important in reducing CO2 emissions - hybrid powertrains and natural gas engines. At first, a prototype series hybrid powertrain was analyzed based on on-highway data collected from its powertrain components. Work done per mile by the electrical components of the powertrain showed inefficient battery operation. The net energy delivery of the battery was close to zero at the end of the runs. This indicated battery was majorly used as an energy storage device. Roughly 15% of losses were observed in the power electronics to supply power from battery and generator to the motor. Ability of the hybrid system to capture regenerative energy and utilize it to propel the vehicle is a primary cause for fuel savings. The ability of this system to capture the regenerative energy was studied by modeling the system. The vehicle model demonstrated that the system was capturing most of the theoretically available regenerative energy. The thesis also demonstrates the possibility of reduction of vehicular level losses for the prototype truck. Drag and rolling resistance coefficients were estimated based on two coast down tests conducted. The ratio of captured regenerative to the drive energy energy for estimated drag and rolling resistant coefficients showed that the current system utilizes 4%-9% of its drive energy from the captured regenerative energy. Whereas a low mileage Peterbilt 579 truck could increase the energy capture ratio to 8%-18% for the same drive profile and route. Decrease in the truck’s aerodynamic drag and rolling resistance can potentially improve the fuel benefits.</div><div>The second study aimed to reduce the engine level pumping losses for a natural gas spark ignition engine by cylinder deactivation (CDA). Spark ignited stoichiometric engines with an intake throttle valve encounter pumping/throttling losses at low speed, low loads due to the restriction of intake air by the throttle body. A simulation study for CDA on a six cylinder natural gas engine model was performed in GT- Power. The simulations were ran for steady state operating points with a torque range 25-560 ftlbs and 1600 rpm. Two , three and four cylinders were deactivated in the simulation study. CDA showed significant fuel benefits with increase in brake thermal efficiency and reduction in brake specific fuel consumption depending on the number of deactivated cylinders. The fuel benefits tend to decrease with increase in torque. Engine cycle efficiencies were analyzed to investigate the efficiency improvements. The open cycle efficiency is the main contributor to the overall increase in the brake thermal efficiency. The work done by the engine to overcome the gas exchange during the intake and exhaust stroke is referred to the pumping losses. The reduction in pumping losses cause an improvement in the open cycle efficiency. By deactivating cylinders, the engine meets its low torque requirements by increase in the intake manifold pressure. Increased intake manifold pressure also resulted in reduction of the pumping loop indicating reduced pumping losses. A major limitation of the CDA strategy was ability to meet EGR fraction requirements. The increase in intake manifold pressure also caused a reduction in the delta pressure across the EGR valve. At higher torques with high EGR requirements CDA strategy was unable to meet the required EGR fraction targets. This limited the benefits of CDA to a specific torque range based on the number of deactivated cylinders. Some variable valve actuation strategies were suggested to overcome this challenge and extend the benefits of CDA for a greater torque range.</div><div><br></div>

<p>Presently, internal combustion engines provide power to move the majority of vehicles on the roadway. While battery-powered electric vehicles provide an alternative, their widespread acceptance is hindered by range anxiety and longer charging/refueling times. Dynamic wireless power transfer (DWPT) has been proposed as a means to reduce both range anxiety and charging/refueling times. In DWPT, power is provided to a vehicle in motion using electromagnetic fields transmitted by a transmitter embedded within the roadway to a receiver at the underside of the vehicle. For commercial vehicles, DWPT often requires transferring hundreds of kW through a relatively large airgap (> 20 cm). This requires a high-power DC-AC converter at the transmitting end and a DC-AC converter within the vehicle. </p> In this research, a focus is on the development of models that can be used to support the design of DWPT systems. These include finite element-based models of the transmitter/receiver that are used to predict power transfer, coil loss, and core loss in DWPT systems. The transmitter/receiver models are coupled to behavioral models of power electronic converters to predict converter efficiency, mass, and volume based upon switching frequency, transmitter/receiver currents, and source voltage. To date, these models have been used to explore alternative designs for a DWPT intended to power Class 8-9 vehicles on IN interstates. Specifically, the models have been embedded within a genetic algorithm-based multi-objective optimization in which the objectives include minimizing system mass and minimizing loss. Several designs from the optimization are evaluated to consider practicality of the proposed designs.

<div> <div> <div> <p>Commercial vehicles in the United States account for a significant fraction of greenhouse gas emissions and NOx emissions. The objectives of this work are reduction in commercial vehicle NOx emissions through enhanced aftertreatment thermal management via diesel engine variable valve actuation and the reduction of commercial vehicle fuel consumption/GHG emissions by enabling more effective class 8 truck platooning. </p> <p><br></p><p>First, a novel diesel engine aftertreatment thermal management strategy is proposed which utilizes a 2-stroke breathing variable value actuation strategy to increase the mass flow rate of exhaust gas. Experiments showed that when allowed to operate with modestly higher engine-out emissions, temperatures comparable to baseline could be achieved with a 1.75x exhaust mass flow rate, which could be beneficial for heating the SCR catalyst in a cold-start scenario. </p> <p><br></p><p>Second, a methodology is presented for characterizing aerodynamic drag coefficients of platooning trucks using experimental track-test data, which allowed for the development of high-fidelity platoon simulations and thereby enabled rapid development of advanced platoon controllers. Single truck and platoon drag coefficients were calculated for late model year Peterbilt 579’s based on experimental data collected during J1321 fuel economy tests for a two-truck platoon at 65 mph with a 55’ truck gap. Results show drag coefficients of 0.53, 0.50, and 0.45 for a single truck, a platoon front truck, and a platoon rear truck, respectively. </p> <p><br></p><p>Finally, a PID-based platoon controller is presented for maximizing fuel savings and gap control on hilly terrain using a dynamically-variable platoon gap. The controller was vetted in simulation and demonstrated on a vehicle in closed-course functionality testing. Simulations show that the controller is capable of 6-9% rear truck fuel savings on a heavily-graded route compared to a production-intent platoon controller, while increasing control over the truck gap to discourage other vehicles from cutting in. </p></div></div></div>