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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.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

Increasing regulatory demand for efficiency has led to development of novel combustion modes such as HCCI, GCI and RCCI for gasoline light duty engines. In order to realize HCCI as a compression ignition combustion mode system, in-cylinder compression temperatures must be elevated to reach the autoignition point of the premixed fuel/air mixture. This should be co-optimized with appropriate fuel formulations that can autoignite at such temperatures. CFD combustion modeling is used to model the auto ignition of gasoline fuel under compression ignition conditions. Using the fully detailed fuel mechanism consisting of thousands of components in the CFD simulations is computationally expensive. To overcome this challenge, the real fuel is represented by few major components of create a surrogate fuel mechanism. In this study, 9 variations of gasoline fuel sets were chosen as candidates to run in HCCI combustion mode.

Gasoline compression ignition (GCI) technology shows the potential to obtain high thermal efficiencies while maintaining low soot and NOx emissions in light-duty engine applications. Recent experimental studies and numerical simulations have indicated that high reactivity gasoline-like fuels can further enable the benefits of GCI combustion. However, there is limited empirical data in the literature studying the gasoline compression ignition process at relevant in-cylinder conditions, which are required for further optimizing combustion system designs. This study investigates the temporal and spatial evolution of the compression ignition process of various high reactivity gasoline fuels with research octane numbers (RON) of 71, 74 and 82, as well as a conventional RON 97 E10 gasoline fuel. A ten-hole prototype gasoline injector specifically designed for GCI applications capable of injection pressures up to 450 bar was used.

The compression ratio is a strong lever to increase the efficiency of an internal combustion engine. However, among others, it is limited by the knock resistance of the fuel used. Natural gas shows a higher knock resistance compared to gasoline, which makes it very attractive for use in internal combustion engines. The current paper describes the knock behavior of two gasoline fuels, and specific incylinder blend ratios with one of the gasoline fuels and natural gas. The engine used for these investigations is a single cylinder research engine for light duty application which is equipped with two separate fuel systems. Both fuels can be used simultaneously which allows for gasoline to be injected into the intake port and natural gas to be injected directly into the cylinder to overcome the power density loss usually connected with port fuel injection of natural gas.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

This paper evaluates the potential of adding higher alcohols to gasoline blendstock in an attempt to improve overall fuel performance. The alcohols considered include ethanol, normal- and iso-structures of propanol, butanol and pentanol as well as normal-hexanol (C2-C6). Fuel performance is quantified based on energy content, knock resistance as well as petroleum displacement and promising multi-component blends are systematically identified based on property prediction methods. These promising multi-component blends, as well as their respective reference fuels, are subsequently tested for efficiency and emissions performance utilizing a gasoline direct injection, spark ignition engine. The engine test results confirm that combustion and efficiency of tailored multi-component blends closely match those of the reference fuels. Regulated emissions stemming from combustion of these blends are equal or lower compared to the reference fuels across the tested engine speed and load regime.

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.

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.

The high octane rating and more plentiful domestic supply of natural gas make it an excellent alternative to gasoline. Recent studies have shown that using natural gas in dual fuel engines provides one possible strategy for leveraging the advantages of both natural gas and gasoline. In particular, such engines been able to improve overall engine efficiencies and load capacity when they leverage direct injection of the natural gas fuel. While the benefits of these engine concepts are still being explored, differences in fuel composition, combustion process and in-cylinder mixing could lead to dramatically different emissions which can substantially impact the effectiveness of the engine’s exhaust aftertreatment system. In order to explore this topic, this study examined the variations in speciated hydrocarbon emissions which occur for different fuel blends of E10 and compressed natural gas and for different fuel injection strategies on a spark-ignition engine.

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.

Natural Gas (NG) is an alternative fuel which has attracted a lot of attention recently, in particular in the US due to shale gas availability. The higher hydrogen-to-carbon (H/C) ratio, compared to gasoline, allows for decreasing carbon dioxide emissions throughout the entire engine map. Furthermore, the high knock resistance of NG allows increasing the efficiency at high engine loads compared to fuels with lower knock resistance. NG direct injection (DI) allows for fuel to be added after intake valve closing (IVC) resulting in an increase in power density compared to an injection before IVC. Steady-state engine tests were performed on a single-cylinder research engine equipped with gasoline (E10) port-fuel injection (PFI) and NG DI to allow for in-cylinder blending of both fuels. Knock investigations were performed at two discrete compression ratios (CR), 10.5 and 12.5.

In this study, the impact of the intake valve timing on knock propensity is investigated on a dual-fuel engine which leverages a low octane fuel and a high octane fuel to adjust the fuel mixture’s research octane rating (RON) based on operating point. Variations in the intake valve timing have a direct impact on residual gas concentrations due to valve overlap, and also affect the compression pressure and temperature by altering the effective compression ratio (eCR). In this study, it is shown that the fuel RON requirement for a non-knocking condition at a fixed operating point can vary significantly solely due to variations of the intake valve timing. At 2000 rpm and 6 bar IMEP, the fuel RON requirement ranges from 80 to 90 as a function of the intake valve timing, and the valve timing can change the RON requirement from 98 to 104 at 2000 rpm and 14 bar IMEP.

The characteristics of combustion knock metrics over a number of engine cycles can be an essential reference for knock detection and control in internal combustion engines. In a Spark-Ignition (SI) engine, the stochastic nature of combustion knock has been shown to follow a log-normal distribution. However, this has been derived from experiments done with gasoline only and applicability of log-normal distribution to dual-fuel combustion knock has not been explored. To evaluate the effectiveness and accuracy of log-normal distributed knock model for methane-gasoline blended fuel, a sweep of methane-gasoline blend ratio was conducted at two different engine speeds. Experimental investigation was conducted on a single cylinder prototype SI engine equipped with two fuel systems: a direct injection (DI) system for gasoline and a port fuel injection (PFI) system for methane.

Traditionally, internal combustion engines follow thermodynamic cycles comprising a fixed number of crank revolutions, in order to accommodate compression of the incoming air as well as expansion of the combustion products. With the advent of computer-controlled valve trains, we now have the possibility of detaching compression from expansion events, thus achieving an “adaptive cycle” molded to the performance required of the engine at any given time. The adaptive cycle engine differs from split-cycle engines in that all phases of the cycle take place within the same cylinder, so that in an extreme case the gas contained in all cylinders can be undergoing expansion events, resulting in a large increase in power density over the conventional four-stroke and two-stroke cycles. Key to the adaptive cycle is the addition of a variable-timing “transfer” valve to each cylinder, plus a space for air storage between compression and expansion events.

The objective of this study is the development of a computationally efficient CFD-based tool for finding 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, the duration of each pulse, the duration of the dwell, the exhaust gas recirculation rate and the boost pressure.