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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

This paper presents a combined numerical and experimental investigation of the characteristics of spark discharge in a spark-ignition engine. The main objective of this work is to gain insights into the spark discharge process and early flame kernel development. Experiments were conducted in an inert medium within an optically accessible constant-volume combustion vessel. The cross-flow motion in the vessel was generated using a previously developed shrouded fan. Numerical modeling was based on an existing discharge model in the literature developed by Kim and Anderson. However, this model is applicable to a limited range of gas pressures and flow fields. Therefore, the original model was evaluated and improved to predict the behavior of spark discharge at pressurized conditions up to 45 bar and high-speed cross-flows up to 32 m/s. To accomplish this goal, a parametric study on the spark channel resistance was conducted.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

The influence of spark plug orientation on early flame kernel development is investigated in an optically accessible gasoline direct injection homogeneous charged spark ignition engine. This investigation provides visual understanding and statistical characterization of how spark plug orientation impacts the early flame kernel and thus combustion phasing and engine performance. The projected images of flame kernel were captured through natural flame chemiluminescence with a high-speed camera at 10,000 frames per second, and the ignition secondary discharge voltage and current were measured with a 10 MHz DAQ system. The combustion metrics were determined using measurement from a piezo-electric in-cylinder pressure transducer and real-time engine combustion analyzer. Three spark plug orientations with two different electrode designs were studied. The captured images of the flame were processed to yield 2D and 1D probability distributions.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

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.

This paper analyzes the control of the series-parallel powersplit used in the 2001 Michigan Tech FutureTruck. An electronic throttle controller is implemented and a new control algorithm is proposed and tested. A vehicle simulation has been created in MATLAB and the control algorithm implemented within the simulation. A program written in C has also been created that implements the control algorithm in the test vehicle. The results from both the simulation and test vehicle are presented and discussed and show a 15% increase in fuel economy. With the increase in fuel economy, and through the use of the original exhaust after treatment, lower emissions are also expected.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

A 2-dimensional numerical model (MTU-FILTER) for a single channel of a honeycomb ceramic diesel particulate trap has been developed. The mathematical modeling of the filtration, flow, heat transfer and regeneration behavior of the particulate trap is described. Numerical results for the pressure drop and particulate mass were compared with existing experimental results. Parametric studies of the diesel particulate trap were carried out. The effects of trap size and inlet temperature on the trap performance are studied using the trap model. An approximate 2-dimensional analytical solution to the simplified Navier-Stokes equations was used to calculate the velocity field of the exhaust flow in the inlet and outlet channels. Assuming a similarity velocity profile in the channels, the 2-dimensional Navier-Stokes equations are approximated by 1-dimenisonal conservation equations, which is similar to those first developed by Bissett.

Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization.

Real-time control of Reactivity Controlled Compression Ignition (RCCI) during engine load and speed transient operation is challenging, since RCCI combustion phasing depends on nonlinear thermo-kinetic reactions that are controlled by dual-fuel reactivity gradients. This paper discusses the design and implementation of a real-time closed-loop combustion controller to maintain optimum combustion phasing during RCCI transient operations. New algorithms for real-time in-cylinder pressure analysis and combustion phasing calculations are developed and embedded on a Field Programmable Gate Array (FPGA) to compute RCCI combustion and performance metrics on cycle-by-cycle basis. This cycle-by-cycle data is then used as a feedback to the combustion controller, which is implemented on a real-time processor. A computationally efficient algorithm is introduced for detecting Start of Combustion (SOC) for the High Temperature Heat Release (HTHR) or main-stage heat release.

For spark-ignition gasoline engines operating under the wide speed and load conditions required for light duty vehicles, ignition quality limits the ability to minimize fuel consumption and NOx emissions via dilution under light and part load conditions. In addition, during transients including tip-outs, high levels of dilution can occur for multiple combustion events before either the external exhaust gas can be adjusted and cleared from the intake or cam phasing can be adjusted for correct internal dilution. Further improvement and a thorough understanding of the impact of the ignition system on combustion near the dilution limit will enable reduced fuel consumption and robust transient operation. To determine and isolate the effects of multiple parameters, a variable output ignition system (VOIS) was developed and tested on a 3.5L turbocharged V6 homogeneous charge direct-injection gasoline engine with two spark plug gaps and three ignition settings.

The increase in regulatory demand to reduce CO2 emissions resulted in a focus on the development of novel combustion modes such as gasoline compression ignition (GCI). It has been shown by others that GCI can improve the overall engine efficiency while achieving soot and NOx emissions targets. In comparison with diesel fuel, gasoline has a higher volatility and has more resistance to autoignition, therefore, it has a longer ignition delay time which facilitates better mixing of the air-fuel charge before ignition. In this study, a GCI combustion system has been tested using a 2.2L compression ignition engine as part of a US Department of Energy funded project. For this purpose, a multiple injection strategy was developed to improve the pressure rise rates and soot emission levels for the same engine out NOx emissions.

A variable displacement engine with the capability to vary stroke length and compression ratio independent of one another has been designed, prototyped, and successfully operated. Reasons for investigation of such an engine are the potential for improvement in fuel economy and/or performance. Literature has shown that engines with variable compression ratio can significantly decrease specific fuel consumption. Engines with variability in stroke length can maintain peak efficiency running conditions by adjusting power output through displacement change verses through the efficiency detriment of throttling. The project began with the synthesis of a planar 2-dimensional rigid body mechanism. Various synthesis techniques were employed and design took place with a collection of computer software. MATLAB code performed much of the synthesis, kinematic, and dynamic analysis.

The objective of this study is the development of a computationally efficient CFD-based tool with the capability of finding optimal engine operating conditions with respect to emissions and fuel consumption. The approach taken uses a conjugate gradient method, where the line search is performed with a backtracking algorithm. The initial backtracking step employs an adaptive step size mechanism which depends on the steepness of the search direction. The engine simulations are performed with a KIVA-3-based code which is equipped with well-established spray, combustion and emission models. The cost function is based on the idea of the penalty method and is minimized over the unit cube in n-dimensional space, which represents the set of normalized injection parameters under investigation. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine.

Monitoring of the wear rate of a diesel engine will yield valuable information regarding the wear mechanism within a diesel engine and ultimately will improve the predictions of failing engines and/or their components to allow preventive maintenance which will prolong the life of the engine. A mathematical model was developed that describes the wear particle concentration as a function of time in a diesel engine. This model contains engine and lubrication system parameters that determine the concentration of wear particles in the engine sump. These variables are the oil system volume, oil flow rate, particle generation rate, filtering efficiency and the initial particle concentration. The model has been employed to study the wear particle concentrations in the sump and the mass of particles in the filter for the Cummins VT-903 diesel engine.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.