Search Results

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

The peak efficiency of modern spark ignited engines varies from 36% to 40% depending on the exact technology utilized. Most engines can achieve this peak efficiency for a limited operating region. Multi-speed transmissions allow the engine to operate closer to its most efficient operating regions for more significant portions of operation. In the case of hybrid powertrains, electric machines help in improving engine efficiency by adjusting operating speed and load. Engine shutdown during idle events and low loads is another avenue for improving the overall efficiency. The choice of the ideal powertrain and component sizes depends on the engine characteristics, drive cycles and vehicle technical requirements. This study examines what type of powertrains will be suitable for more efficient engines that are likely to be available in the near future. Some of the new technologies achieve higher efficiency with a trade off on power or by accepting a more restrictive operating region.

On-road comparisons were made between a federal reference method mobile emissions laboratory (MEL) and a portable emissions measurement system (PEMS) to support validation of the engine “Not To Exceed” (NTE) emissions design and to evaluate the accuracy of PEMS. Three different brake specific emissions calculation equations (methods) were used as part of this research, with method one directly using engine speed and torque, and methods two and three including ECM fuel consumption and carbon balance fuel consumption. The brake specific NOx emissions for the particular PEMS unit utilized in this program were consistently higher than those for the MEL. The brake specific (bs) NOx NTE deltas were +0.63±0.31 g/kW-h (0.47±0.23 g/hp-h), +0.55±0.17 g/kW-h (0.41±0.13 g/hp-h), and +0.54±0.17g/kW-h (0.40±0.13g/hp-h) for methods one, two, and three respectively.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

Fuel is a significant portion of the operating cost for an on-highway diesel engine and fuel economy is important to the economics of shipping most goods in North America. Cat® ACERT™ engine technology is no exception. Ball bearings have been applied to the series turbochargers for the Caterpillar heavy-duty, on-highway diesel truck engines in order to reduce mechanical loss for improved efficiency and lower fuel consumption. Over many years of turbocharger development, much effort has been put into improving the aerodynamic efficiency of the compressor and turbine stages. Over the same span of time, the mechanical bearing losses of a turbocharger have not experienced a significant reduction in power consumption. Most turbochargers continue to use conventional hydrodynamic radial and thrust bearings to support the rotor. While these conventional bearings provide a low cost solution, they do create significant mechanical loss.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

Accurate individual cylinder Air Fuel Ratio (AFR) feedback provide opportunities for improved engine performance and reduced emissions in spark ignition engines. One potential measurement for individual cylinder AFR is in-cylinder ionization measured by employing the spark plug as a sensor. A number of previous investigations have studied correlations of the ionization signal with AFR and shown promising results. However the studies have typically been limited to single cylinders under restricted operating conditions. This investigation analyzes and characterizes the ionization signals in correlation to individual AFR values obtained from wide-band electrochemical oxygen sensors located in the exhaust runners of each cylinder. Experimental studies for this research were conducted on a 2.0L inline 4 cylinder spark ignited engine with dual independent variable cam phasing and an intake charge motion control valve.

Living in the era of rising environmental sensibility and increasing gasoline prices, the development of a new environmentally friendly generation of vehicles becomes a necessity. Hybrid electric vehicles are one means of increasing propulsion system efficiency and decreasing pollutant emissions. In this paper, the series-parallel power-split configuration for Michigan Technological University's FutureTruck is analyzed. Mathematical equations that describe the hybrid power-split transmission are derived. The vehicle's differential equations of motion are developed and the system's need for a controller is shown. The engine's brake power and brake specific fuel consumption, as a function of its speed and throttle position, are experimentally determined. A control strategy is proposed to achieve fuel efficient engine operation. The developed control strategy has been implemented in a vehicle simulation and in the test vehicle.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

Measurement of internal combustion engine air flow is challenging due to the required modification of the intake system and subsequent change in the air flow pattern. In this paper, various surge tank volumes were investigated to improve the accuracy of measuring air flow rate into a 674-cm3, four-stroke, liquid-cooled, internal combustion engine. According to the experimental results, when the venturi meter is used to measure the intake air flow rate, an air surge tank is required to be installed downstream of the venturi to smoothen the air flow. Moreover, test results revealed that increasing air surge tank volume beyond a limit could have a negative effect on the engine performance parameters especially in carbureted engines where controlling AFR is difficult. Although the air flow rate into the engine changed with increasing tank volume, the air-fuel ratio was leaner for smaller tank volumes.

Mandated pollutant emission levels are shifting light-duty vehicles towards hybrid and electric powertrains. Heavy-duty applications, on the other hand, will continue to rely on internal combustion engines for the foreseeable future. Hence there remain clear environmental and economic reasons to further decrease IC engine emissions. Turbocharged diesels are the mainstay prime mover for heavy-duty vehicles and industrial machines, and transient performance is integral to maximizing productivity, while minimizing work cycle fuel consumption and CO2 emissions. 1D engine simulation tools are commonplace for “virtual” performance development, saving time and cost, and enabling product and emissions legislation cycles to be met. A known limitation however, is the predictive capability of the turbocharger turbine sub-model in these tools.

Diesel engine downsizing aimed at reducing fuel consumption while meeting stringent exhaust emissions regulations is currently in high demand. The boost system architecture plays an essential role in providing adequate air flow rate for diesel fuel combustion while avoiding impaired transient response of the downsized engine. Electric Turbocharger Assist (ETA) technology integrates an electric motor/generator with the turbocharger to provide electrical power to assist compressor work or to electrically recover excess turbine power. Additionally, a variable geometry turbine (VGT) is able to bring an extra degree of freedom for the boost system optimization. The electrically-assisted turbocharger, coupled with VGT, provides an illuminating opportunity to increase the diesel engine power density and enhance the downsized engine transient response. This paper assesses the potential benefits of the electrically-assisted turbocharger with VGT to enable heavy-duty diesel engine downsizing.

Greenhouse gas regulations and global economic growth are expected to drive a future demand shift towards diesel fuel in the transportation sector. This may create a market opportunity for cost-effective fuels in the light distillate range if they can be burned as efficiently and cleanly as diesel fuel. In this study, the emission performance of a low cetane number, low research octane number naphtha (CN 34, RON 56) was examined on a production 6-cylinder heavy-duty on-highway truck engine and aftertreatment system. Using only production hardware, both the engine-out and tailpipe emissions were examined during the heavy-duty emission testing cycles using naphtha and ultra-low-sulfur diesel (ULSD) fuels. Without any modifications to the hardware and software, the tailpipe emissions were comparable when using either naphtha or ULSD on the heavy duty test cycles.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.