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Optimization of the engine cold start is critical for gasoline direct injection (GDI) engines to meet increasingly stringent emission regulations, since the emissions during the first 20 seconds of the cold start constitute more than 80% of the hydrocarbon (HC) emissions for the entire EPA FTP75 drive cycle. However, Direct Injection Spark Ignition (DISI) engine cold start optimization is very challenging due to the rapidly changing engine speed, cold thermal environment and low cranking fuel pressure. One approach to reduce HC emissions for DISI engines is to adopt retarded spark so that engines generate high heat fluxes for faster catalyst light-off during the cold idle. This approach typically degrades the engine combustion stability and presents additional challenges to the engine cold start. This paper describes a CFD modeling based approach to address these challenges for the Ford 3.5L V6 EcoBoost engine cold start.

Ford Motor Company is introducing “EcoBoost” gasoline turbocharged direct injection (GTDI) engine technology in the 2010 Lincoln MKS. A logical enhancement of EcoBoost technology is the use of E85 for knock mitigation. The subject of this paper is the optimal use of E85 by using two fuel systems in the same EcoBoost engine: port fuel injection (PFI) of gasoline and direct injection (DI) of E85. Gasoline PFI is used for starting and light-medium load operation, while E85 DI is used only as required during high load operation to avoid knock. Direct injection of E85 (a commercially available blend of ∼85% ethanol and ∼15% gasoline) is extremely effective in suppressing knock, due to ethanol's high inherent octane and its high heat of vaporization, which results in substantial cooling of the charge. As a result, the compression ratio (CR) can be increased and higher boost levels can be used.

Exhaust Gas Recirculation (EGR) coolers are commonly used in on-road and off-road diesel engines to reduce the recirculated gas temperature in order to reduce NOx emissions. One of the common performance behaviors for EGR coolers in use on diesel engines is a reduction of the heat exchanger effectiveness, mainly due to particulate matter (PM) deposition and condensation of hydrocarbons (HC) from the diesel exhaust on the inside walls of the EGR cooler. According to previous studies, typically, the effectiveness decreases rapidly initially, then asymptotically stabilizes over time. Prior work has postulated a deposit removal mechanism to explain this stabilization phenomenon. In the present study, five field aged EGR cooler samples that were used on construction machines for over 10,000 hours were analyzed in order to understand the deposit structure as well as the deposit composition after long duration use.

This study experimentally investigates the impact of Miller cycle strategies on the combustion process, emissions, and thermal efficiency in heavy-duty diesel engines. The experiments were conducted at constant engine speed, load, and engine-out NOx (1160 rev/min, 1.76 MPa net IMEP, 4.5 g/kWh) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Early Intake Valve Closing (EIVC) and Late Intake Valve Closing (LIVC) timing strategies were compared to a conventional intake valve profile. While the decrease in effective compression ratio associated with the use of Miller valve profiles was symmetric around bottom dead center, the decrease in volumetric efficiency (VE) was not. EIVC profiles were more effective at reducing VE than LIVC profiles. Despite this difference, EIVC and LIVC profiles with comparable VE decrease resulted in similar changes in combustion and emissions characteristics.

Design of military vehicle needs to meet often conflicting requirements such as high mobility, excellent fuel efficiency and survivability, with acceptable cost. In order to reduce the development cost, time and associated risk, as many of the design questions as possible need to be addressed with advanced simulation tools. This paper describes a methodology to design a fuel efficient powerpack unit for a series hybrid electric military vehicle, with emphasis on the e-machine design. The proposed methodology builds on previously published Finite element based analysis to capture basic design features of the generator with three variables, and couples it with a model reduction technique to rapidly re-design the generator with desired fidelity. The generator is mated to an off the shelf engine to form a powerpack, which is subsequently evaluated over a representative military drive cycles.

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

The U.S. Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been refined and revalidated using newly acquired data from model year 2013-2016 engines and vehicles. The robustness of EPA’s vehicle and engine testing for the MTE coupled with further validation of the ALPHA model has highlighted some areas where additional data can be used to add fidelity to the engine model within ALPHA.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).

An analytic model of powertrain efficiency on a drive cycle was developed and evaluated using hundreds of cars and trucks from the US EPA ‘Test Car Lists’. The efficiency properties of naturally aspirated and downsized turbocharged engines were compared for vehicles with automatic transmissions on the US cycles. The resulting powertrain cycle efficiency model is proportional to the powertrain marginal energy conversion efficiency K, which is also its upper limit. It decreases as the powertrain matching parameters, the displacement-to-mass ratio (D/M) and the gearing ratio (n/V), increase. The inputs are the powertrain fuel consumption, the vehicle road load, and the cycle work requirement. They could be modeled simply with only minor approximations through the use of absolute inputs and outputs, and systematic use of scaling. On the Highway test, conventional automatic transmission vehicles of moderate performance achieve between 25% and 30% powertrain efficiency.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

Reducing the weight of the M1A2 tank by lightweighting hull, suspension, and track results in 5.1%, 1.3%, and 0.6% tank mass reductions, respectively. The impact of retrofitting with lightweight components is evaluated through primary energy demand (PED), cost, and fuel consumption (FC). Life cycle stages included are preproduction (design, prototype, and testing), material production, part fabrication, and operation. Metrics for lightweight components are expressed as ratios comparing lightweighted and unmodified tanks. Army-defined drive cycles were employed and an FC vs. mass elasticity of 0.55 was used. Depending on the distance traveled, cost to retrofit and operate a tank with a lightweighted hull is 3.5 to 19 times the cost for just operating an unmodified tank over the same distance. PED values for the lightweight hull are 1.1 to 2 times the unmodified tank. Cost and PED ratios decrease with increasing distance.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

A belt driven Front End Accessory Drive (FEAD) is used to efficiently supply power to accessory components on automotive engines. The total energy absorbed by the FEAD consists of the accessory component requirements, the belt deformation and friction losses as well as the bearing losses. The accessory component torque requirements provide accessory function such as air conditioning, fluid pumping and electrical power generation. Alternatively, belt related torque losses are a significant parasitic loss, since they do not contribute any useful work. This paper will explain the source of energy loss in FEADs and outline a comprehensive strategy to reduce it. Test results comparing the effect of reduced friction on fuel consumption will be presented as well.

This simulation study explores the potential synergy between the HCCI engine system and three hybrid electric vehicle (HEV) configurations, and proposes the supervisory control strategy that maximizes the benefits of combining these two technologies. HCCI operation significantly improves fuel efficiency at part load, while hybridization aims to reduce low load/low speed operation. Therefore, a key question arises: are the effects of these two technologies additive or overlapping? The HEV configurations include two parallel hybrids with varying degrees of electrification, e.g. with a 5kW integrated starter/motor (“Mild”) and with a 10 kW electric machine (“Medium”), and a power-split hybrid. The engine is a dual-mode, SI-HCCI system and the engine map reflects the impact of HCCI on brake specific fuel consumption.

Electrification and hybridization have great potential for improving fuel economy and reducing visual signature or soot emissions in military vehicles. Specific challenges related to military applications include severe duty cycles, large and uncertain energy flows through the system and high thermal loads. A novel supervisory control strategy is proposed to simultaneously mitigate severe engine transients and to reduce high electric current in the battery without oversizing the battery. The described objectives are accomplished by splitting the propulsion power demand through filtering in the frequency domain. The engine covers only low frequency power demand profile while the battery covers high frequency components. In the proposed strategy, the separation filter is systematically designed to identify different frequency components with the consideration of fuel consumption, aggressive engine transients, and battery electric loads.

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties.

Dimethyl ether (DME) is an alternative to diesel fuel for use in compression-ignition engines with modified fuel systems and offers potential advantages of efficiency improvements and emission reductions. DME can be produced from natural gas (NG) or from renewable feedstocks such as landfill gas (LFG) or renewable natural gas from manure waste streams (MANR) or any other biomass. This study investigates the well-to-wheels (WTW) energy use and emissions of five DME production pathways as compared with those of petroleum gasoline and diesel using the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET®) model developed at Argonne National Laboratory (ANL).

The drive cycle average powertrain efficiency of current US vehicles is studied by applying a first principles model to the EPA Test Car List database. The largest group of vehicles has naturally aspirated engines and six speed planetary automatic transmissions, and defines the base technology level. For this group the best cycle average powertrain efficiency is independent of vehicle size and is achieved by the lowest power-to-weight vehicles. For all segments of the EPA test, the fuel required per unit of vehicle work (the inverse of powertrain efficiency), is found to increase linearly with a basic powertrain matching parameter. The parameter is (D/M)(n/V), where D is engine displacement, M vehicle mass, and (n/V) the top gear engine speed over the vehicle speed. The fuel consumption penalties in the City segments due to powertrain warm-up, aftertreatment warm-up, stop-and-go operation, and power-off operation are estimated.

The fuel consumption of a vehicle is shown to be linearly proportional to (1) total vehicle work required to drive the cycle due to mass and acceleration, tire friction, and aerodynamic drag and (2) the powertrain (PT) mechanical losses, which are approximately proportional to the engine displaced volume per unit distance travelled (displacement time gearing). The fuel usage increases linearly with work and displacement over a wide range of applications, and the rate of increase is inversely proportional to the marginal efficiency of the engine. The theoretical basis for these predictions is reviewed. Examples from current applications are discussed, where a single PT is used across several vehicles. A full vehicle cycle simulation model also predicts a linear relationship between fuel consumption, vehicle work, and displacement time gearing and agrees well with the application data.