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Vehicle operation during cold-start powertrain conditions can have a significant impact on drivability, fuel economy and tailpipe emissions in modern passenger vehicles. As efforts continue to maximize fuel economy in passenger vehicles, considerable engineering resources are being spent in order to reduce the consumption penalties incurred shortly after engine start and during powertrain warmup while maintaining suitably low levels of tailpipe emissions. Engine downsizing, advanced transmissions and hybrid-electric architecture can each have an appreciable effect on cold-start strategy and its impact on fuel economy. This work seeks to explore the cold-start strategy of several passenger vehicles with different powertrain architectures and to understand the resulting fuel economy impact relative to warm powertrain operation. To this end, four vehicles were chosen with different powertrain architectures.

One-dimensional simulation methods for unsteady (transient) engine operations have been developed and published in previous studies. These 1-D methods utilize heat release and emissions results obtained from 3-D CFD simulations which are stored in a data library. The goal of this study is to improve the 1-D methodology by optimizing the control strategies. Also, additional independent parameters are introduced to extend the 3-D data library, while, as in the previous studies, the number of interpolation points for each parameter remains small. The data points for the 3-D simulations are selected in the vicinity of the expected trajectories obtained from the independent parameter changes, as predicted by the transient 1-D simulations. By this approach, the number of time-consuming 3-D simulations is limited to a reasonable amount.

Exhaust gas recirculation (EGR) has been employed in a diesel engine to reduce NOx emissions by diluting the fresh air charge with gases composed of primarily N2, CO2, H2O, and O2 from the engines exhaust stream. The addition of EGR reduces the production of NOx by lowering the peak cylinder gas temperature and reducing the concentration of O2 molecules, both of which contribute to the NOx formation mechanism. The amount of EGR has been typically controlled using an open loop control strategy where the flow of EGR was calibrated to the engine speed and load and controlled by the combination of an EGR valve and the ratio of the boost and exhaust back pressures. When oxygenated biofuels with lower specific energy are used, the engine control unit (ECU) will demand a higher fuel rate to maintain power output, which can alter the volumetric flow rate of EGR. In addition, oxygenated biofuels affect the oxygen concentration in the intake manifold gas stream.

Exergy or availability is the potential of a system to do work. In this paper, an innovative exergy-based control approach is presented for Internal Combustion Engines (ICEs). An exergy model is developed for a Homogeneous Charge Compression Ignition (HCCI) engine. The exergy model is based on quantification of the Second Law of Thermodynamic (SLT) and irreversibilities which are not identified in commonly used First Law of Thermodynamics (FLT) analysis. An experimental data set for 175 different ICE operating conditions is used to construct the SLT efficiency maps. Depending on the application, two different SLT efficiency maps are generated including the applications in which work is the desired output, and the applications where Combined Power and Exhaust Exergy (CPEX) is the desired output. The sources of irreversibility and exergy loss are identified for a single cylinder Ricardo HCCI engine.

Precise cycle-to-cycle control of combustion is the major challenge to reduce fuel consumption in Homogenous Charge Compression Ignition (HCCI) engines, while maintaining low emission levels. This paper outlines a framework for simultaneous control of HCCI combustion phasing and Indicated Mean Effective Pressure (IMEP) on a cycle-to-cycle basis. A dynamic control model is extended to predict behavior of HCCI engine by capturing main physical processes through an HCCI engine cycle. Performance of the model is validated by comparison with the experimental data from a single cylinder Ricardo engine. For 60 different steady state and transient HCCI conditions, the model predicts the combustion phasing and IMEP with average errors less than 1.4 CAD and 0.2 bar respectively. A two-input two-output controller is designed to control combustion phasing and IMEP by adjusting fuel equivalence ratio and blending ratio of two Primary Reference Fuels (PRFs).

This paper discusses the evaluation and application of a portable parked-vehicle tailpipe emissions measurement apparatus (EMA). The EMA consists of an exhaust dilution system and a portable instrument package. The EMA instantaneously dilutes and cools a sample of exhaust with compressed nitrogen or air at a known dilution ratio, thereby presenting it to instruments as it is presented to personnel in the surrounding environment. The operating principles and governing equations of the EMA are presented. A computational method is presented to determine the engine operating and performance parameters from the exhaust CO2 concentrations along with an assumed engine overall volumetric efficiency and brake specific fuel consumption. The parameters determined are fuel/air ratio, mass flow rates of fuel, air and exhaust emissions, and engine brake torque and horsepower.

Experimental Brake Specific Fuel Consumption (BSFC) data are presented for two engines as a function of engine speed, load, outlet coolant temperature and inlet oil temperature. The engines used in the study were the Cummins VT-903 (turbocharged) and the Caterpillar 3208, both being direct-injection and four-cycle. The data were taken for the Cat 3208 engine using a fractional factorial statistical method which reduced the total test matrix from 256 to 64 data points. The experimental data are used in the development of BSFC regression equations as a function of load, speed, outlet coolant temperature and inlet oil temperatures. A mathematical parameter for expressing quantitatively the change of BSFC per 10°F change in coolant and oil temperature is presented. It was found that an increase in the coolant and/or oil temperatures had the effect of reducing BSFC in both engines.

This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of power-split HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods.

Vehicles with power exporting capability are microgrids since they possess electrical power generation, onboard loads, energy storage, and the ability to interconnect. The unique load and silent watch requirements of some military vehicles make them particularly well-suited to augment stationary power grids to increase power resiliency and capability. Connecting multiple vehicles in a peer-to-peer arrangement or to a stationary grid requires scalable power management strategies to accommodate the possibly large numbers of assets. This paper describes a military ground vehicle power management scheme for vehicle-to-grid applications. The particular focus is overall fuel consumption reduction of the mixed asset inventory of military vehicles with diesel generators typically used in small unit outposts.

The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time.

This investigation focuses on conventional powertrain technologies that provide operational synergy based on customer utilization to reduce fuel consumption for a heavy-duty, nonroad (off-road) material handler. The vehicle of interest is a Pettibone Cary-Lift 204i, with a base weight of 50,000 lbs. and a lift capacity of 20,000 lbs. The conventional powertrain consists of a US Tier 4 Final diesel engine, a non-lockup torque converter, a four-speed powershift automatic transmission, and all-wheel drive. The paper will present a base vehicle energy/fuel consumption breakdown of propulsion, hydraulic and idle distribution based on a representative end-user drive cycle. The baseline vehicle test data was then used to develop a correlated lumped parameter model of the vehicle-powertrain-hydraulic system that can be used to explore technology integration that can reduce fuel consumption.