Search Results

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.

Impending emissions regulations for diesel engines, specifically exhaust particulate emissions have caused engine manufacturers to once again examine the potential of alternative fuels. Much interest has centered around compressed natural gas (CNG) due to its potential for low particulate and NOx emissions. Natural gas engine development projects have tended toward the use of current gasoline engine technology (stoichiometric mixtures, closed-loop fuel control, exhaust catalysts) or have applied the results of previous research in lean-burn gasoline engines (high-turbulence combustion chambers). These technologies may be inappropriate for foreseeable emissions targets in heavy-duty natural gas engines.

Alternative combustion crossing stoichiometric air fuel ratio was investigated to utilize a 4-way catalyst system with LNT (lean NOx trap). The chemical mechanism of restricting soot formation reactions with low combustion temperature was combined with the physical mechanism of reducing smoke by lowering local equivalence ratio to enable low smoke rich and near rich combustion. A new combustion chamber for spatially and timely mixture formation phasing was developed to combine the two mechanisms and allow smooth EGR changing over a wide load range. Through this investigation, rich and near rich combustion to effectively utilize a 4-way catalyst system was realized. In addition, conditions suitable for LNT sulfur regeneration were realized from light to medium load.

The goal of this project was to demonstrate a low emissions, high efficiency heavy-duty on-highway natural gas engine. The emissions targets for this project are to demonstrate US 2010 emissions standards on the 13-mode steady state test. To meet this goal, a chemically correct combustion (stoichiometric) natural gas engine with exhaust gas recirculation (EGR) and a three way catalyst (TWC) was developed. In addition, a Sturman Industries, Inc. camless Hydraulic Valve Actuation (HVA) system was used to improve efficiency. A Volvo 11 liter diesel engine was converted to operate as a stoichiometric natural gas engine. Operating a natural gas engine with stoichiometric combustion allows for the effective use of a TWC, which can simultaneously oxidize hydrocarbons and carbon monoxide and reduce NOx. High conversion efficiencies are possible through proper control of air-fuel ratio.

An airflow-dominant control system was developed to provide precise engine and exhaust treatment control with low air fuel ratio alternative combustion. The main elements of the control logic include a real-time state observer for in-cylinder oxygen mass estimation, a simplified packaging scheme for all air-handling and fueling parameters, a finite state machine for control mode switching, combustion control models to maintain robust alternative combustion during transients, and smooth rich/lean switching during lean NOx trap (LNT) regeneration without post injection. The control logic was evaluated on a passenger car equipped with a 4-way catalyst system with LNT and was instrumental in achieving US Tier II Bin 5 emission targets with good drivability and low NVH.

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.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

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.

The objective of this effort was to develop an understanding of how different converter substrate cell structures impact tailpipe emissions and pressure drop from a total systems perspective. The cell structures studied were the following: The catalyst technologies utilized were a new technology palladium only catalyst in combination with a palladium/rhodium catalyst. A 4.0-liter, 1997 Jeep Cherokee with a modified calibration was chosen as the test platform for performing the FTP test. The experimental design focused on quantifying emissions performance as a function of converter volume for the different cell structures. The results from this study demonstrate that the 93 square cell/cm2 structure has superior performance versus the 62 square cell/cm2 structure and the 46 triangle cell/cm2 structure when the converter volumes were relatively small. However, as converter volume increases the emissions differences diminish.

This paper describes the design, development and validation of a muffler for reducing exhaust noise from a commercial tele-handler. It also describes the procedure for modeling and optimizing the exhaust muffler along with experimental measurement for correlating the sound transmission loss (STL). The design and tuning of the tele-handler muffler was based on several factors including overall performance, cost, weight, available space, and ease of manufacturing. The analysis for predicting the STL was conducted using the commercial software LMS Virtual Lab (LMS-VL), while the experimental validation was carried out in the laboratory using the two load setup. First, in order to gain confidence in the applicability of LMS-VL, the STL of some simple expansion mufflers with and without extended inlet/outlet and perforations was considered. The STL of these mufflers were predicted using the traditional plane wave transfer matrix approach.

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.

Typically, an engine/dynamometer thermal aging cycle contains combinations of elevated catalyst inlet temperatures, chemical reaction-induced thermal excursions (simulating misfire events), and average air/fuel ratio's (AFR's) to create a condition that accelerates the aging of the test part. In theory, thermal aging is predominantly a function of the time at an exposure temperature. Therefore, if a burner system can be used to simulate the exhaust AFR and catalyst inlet and bed temperature profile generated by an engine running an accelerated aging cycle, then a catalyst should thermally age the same when exposed to either exhaust stream. This paper describes the results of a study that examined the aging difference between six like catalysts aged using the Rapid Aging Test (RAT) cycle (an accelerated thermal aging cycle). Three catalysts were aged using a gasoline-fueled engine aging stand; the other three were aged using a computer controlled burner system.

The influence of catalyst volume, cell density and precious metal loading on the catalyst efficiency were investigated to design a low cost catalyst system. In a first experiment the specific loading was kept constant for a 500cpsi and a 900cpsi substrate. In a second experiment the palladium loading was reduced on the 900cpsi substrate and the same PM loading was applied to a 1200cpsi substrate with lower volume. Finally the loading was further reduced for the 1200cpsi substrate. The following parameters were studied after aging: Catalyst performance of standard cell density compared to high cell density technology Light-off performance and catalyst efficiency as a function of Palladium loading and substrate cell density Catalyst efficiency as a function of AFR biasing The performance of the aged catalysts was investigated in a lambda sweep test and in light-off tests at an engine bench.

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.

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

It is projected that even when the entire on-road fleet of heavy-duty vehicles operating in California is compliant with 2010 emission standards of 0.20 g/bhp-hr, the National Ambient Air Quality Standards (NAAQS) requirements for ambient ozone will not be met. It is expected that further reductions in NOX emissions from the heavy-duty fleet will be required to achieve compliance with the ambient ozone requirement. To study the feasibility of further reductions, the California Air Resources Board (CARB) funded a research program to demonstrate the potential to reach 0.02 g/bhp-hr NOX emissions. This paper details the work executed to achieve this goal on the heavy-duty Federal Test Procedure (FTP) with a heavy-duty natural gas engine equipped with a three-way catalyst. A Cummins ISX-12G natural gas engine was modified and coupled with an advanced catalyst system.

A simulation framework with a validated system model capable of estimating fuel consumption is a valuable tool in analysis and design of the hybrid vehicles. In particular, the framework can be used for (1) benchmarking the fuel economy achievable from alternate hybrid powertrain technologies, (2) investigating sensitivity of fuel savings with respect to design parameters (for example, component sizing), and (3) evaluating the performance of various supervisory control algorithms for energy management. This paper describes such a simulation framework that can be used to predict fuel economy of series hydraulic hybrid vehicle for any specified driver demand schedule (drive cycle), developed in MATLAB/Simulink. The key components of the series hydraulic hybrid vehicle are modeled using a combination of first principles and empirical data. A simplified driver model is included to follow the specified drive cycle.