<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>

<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>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>

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.