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The most recent 2010 emissions standards for heavy-duty engines have established a tailpipe limit of oxides of nitrogen (NOX) emissions of 0.20 g/bhp-hr. However, it is projected that even when the entire on-road fleet of heavy-duty vehicles operating in California is compliant with 2010 emission standards, the National Ambient Air Quality Standards (NAAQS) requirement for ambient particulate matter and Ozone will not be achieved without further reduction in NOX emissions. The California Air Resources Board (CARB) funded a research program to explore the feasibility of achieving 0.02 g/bhp-hr NOX emissions.

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.

The key words in the marketplace for off-highway vehicles are durability, performance, and efficiency. A manufacturer of these vehicles recognizes that one way to successfully address these needs is by a well thought through electronics design. With the computer sophistication now being incorporated into off-highway vehicles, engineers must work closely to assure electromagnetic compatibility (EMC) of the entire system. A properly established EMC program extending from concept to final design will support each of a product's specified operations and still function as an integrated whole. This paper describes the process for designing the EMC for an off-highway vehicle.

The optimal design of hybrid-electric vehicle power systems poses a challenge to the system analyst, who is presented with a host of parameters to fine-tune, along with stringent performance criteria and multiple design objectives to meet. Herein, a methodology is presented to transform such a design task into a constrained multi-objective optimization problem, which is solved using a distributed evolutionary algorithm. A power system model representative of a series hybrid-electric vehicle is considered as a paradigm to support the illustration of the proposed methodology, with particular emphasis on the power system's time-domain performance.

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.

Researchers desire to evaluate system reliability uniquely and efficiently. Despite its strong technical demand, little progress has been made on system reliability analysis in the last two decades. Up to now, bound methods for system reliability prediction have been dominant. For system reliability bounds, the second order bound method gives fairly accurate prediction for system reliability assuming that the probabilities of second-order joint events are accurately obtained. Two primary challenges in system reliability analysis are evaluation of the probabilities of second-order joint events and no unique system reliability for design optimization. Firstly, the greatest technical demand is found in an accurate and efficient method to numerically evaluate the probability of a second-order joint event.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

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.

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.

Elastomers are traditionally designed for use in applications that require specific mechanical properties. Unfortunately, these properties change with respect to many different variables including heat, light, fatigue, oxygen, ozone, and the catalytic effects of trace elements. When elastomeric mounts are designed for NVH use in vehicles, they are designed to isolate specific unwanted frequencies. As the elastomers age however, the desired elastomeric properties may have changed with time. This study looks at the variability seen in new vehicle engine mounts and how the dynamic properties change with respect to miles accumulated on fleet and durability test vehicles.

Automotive catalytic converters must provide a very high level of mechanical and thermal durability to maintain performance during their 100,000 to 150,000 mile life expectancy. The work reported herein characterizes the converter as a base (can) excited spring (mat material) supported mass (substrate). A mat material coupon test apparatus was developed for the purpose of providing parameter data for the converter model in the form of stiffness and material loss factor data as a function of shear deflection across the mat. An intumescent mat material was chosen and its dynamic properties evaluated for a range of converter operating parameters. The mat material response properties were placed into a mat material database as a function of gap bulk density, substrate temperature, and temperature gradient across the mat.

Mat material durability data in the form of fragility curves were generated in a critical temperature region for three intumescent mat materials considered for low temperature converter applications. The mat materials were tested in a tourniquet wrap converter configuration employing a cylindrical ceramic substrate. Prior to developing durability data for these mat materials, the test items were subjected to various environmental thermal and/or vibration aging conditions. Mat material fragility data were generated in terms of the dynamic force required to impose prescribed differential motion between the can and substrate, thereby, subjecting the mat material to a dynamic shearing like that expected during resonant excitation. As expected, it was found that the mat material capacity to resist shearing deformation decreased when the test samples were subjected to 36 hours of low temperature thermal cyclic aging.

An on-line oil consumption measurement system using the SO2 tracer method has characterized automotive gasoline engine oil consumption under various engine operating conditions, including a 200-hour durability test. An oil consumption map of total engine, individual cylinder, and valve train was produced for various speed and load ranges under both steady-state and step-transient operating conditions. The effect of spark timing as an additional engine parameter on the oil consumption was also investigated. Oil consumption maps have enlightened the conventional understanding of oil consumption characteristics and broadened the areas of concern for control technologies. This paper reports the benefit of the on-line oil consumption measurement system, the result of oil consumption history over the durability test, discrete measurement of oil consumption contribution within the engine, and various oil consumption characteristics affected by engine operating conditions.

Emissions from existing diesel-powered urban buses are increasingly scrutinized as local, state, and federal governments require enforcement of more stringent emission regulations and expectations. Currently, visual observation of high smoke levels from diesel-powered equipment is a popular indicator of potential emission problems requiring tune-up or engine maintenance. It is important that bus inspection and maintenance (I/M) operations have a quality control “test” to check engine emissions or diagnose the engine state-of-tune before or after maintenance. Ideally, the “emission test” would be correlated to EPA transient emissions standards, be of short duration, and be compatible with garage procedures and equipment. In support of developing a useful “short-test,” equipment was designed to collect samples of raw exhaust over a short time period for gaseous and particulate emissions.

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.

This paper reviews the technologies available for Bharat Stage 3 and 4 Heavy Duty on-highway emissions standards. Benchmarking data from several existing engines is used to explore the trade-offs between engine/vehicle cost and fuel consumption. Other implications of the available technologies, such as durability / reliability requirements, are also addressed. The paper provides recommendations for low cost approaches to meeting Bharat Stage 3 and 4 standards with good fuel consumption and reliability/ durability characteristics. A brief look ahead to future Bharat Stage 5 requirements is also provided.