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2-step variable valve actuation using early-intake valve closing is a strategy for high fuel economy on spark-ignited gasoline engines. Two discrete valve-lift profiles are used with continuously variable cam phasing. 2-step VVA systems are attractive because of their low cost/benefit, relative simplicity, and ease-of-packaging on new and existing engines. A 2-step VVA system was designed and integrated on a 4-valve-per-cylinder 4.2L line-6 engine. Simulation tools were used to develop valve lift profiles for high fuel economy and low NOx emissions. The intake lift profiles had equal lift for both valves and were designed for high airflow & residual capacity in order to minimize valvetrain switching during the EPA drive cycle. It was determined that an enhanced combustion system was needed to maximize fuel economy benefit with the selected valve lift profiles. A flow-efficient chamber mask was developed to increase in-cylinder tumble motion and combustion rates.

During the production controller and software development process, one critical step is the controller and software verification. There are various ways to perform this verification. One of the commonly used methods is to utilize an HIL (hardware-in-the-loop) test bench to emulate powertrain hardware for development and validation of powertrain controllers and software. A key piece of an HIL bench is the plant dynamics model used to emulate the external environment of a modern controller, such as engine (ECM), transmission (TCM) or powertrain controller (PCM), so that the algorithms and their software implementation can be exercised to confirm the desired results. This paper presents a 6-speed automatic transmission plant dynamics model development for hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The modeling method, model validation, and application in an HIL test environment are described in details.

This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software, with a focus on a closed-loop vehicle drive-train model incorporating a detailed automatic transmission plant dynamics model developed for certain applications. Specifically, this paper presents the closed-loop integration of a 6-speed automatic transmission model developed for our HIL transmission controller and algorithm test bench (Opal-RT TestDrive based). The model validation, integration and its application in an HIL test environment are described in details.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

In this paper, a comparative study of the production applications of hybrid electric powertrains is presented. Vehicles studied include the Toyota Prius, Honda Insight, Toyota Estima, Toyota Crown, Honda Civic Hybrid, and Nissan Tino. The upcoming Ford Escape Hybrid and General Motors Parallel Hybrid Truck (PHT) will also be included, although advance information is limited. The goal of this paper is to look at what hybrid drivetrain architectures have actually been selected for production and what are the underlying details of these drivetrains. Since hybridizing a powertrain involves significant changes, the powertrain architectures are presented in diagram form, with analysis as to the similarities and advantages represented in these architectures. The specific hybrid functions used to save fuel are discussed. Peak power-to-weight ratio and degree of hybridization are plotted for the vehicles. System voltage versus electric power level are also plotted and analyzed.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

In this work, the effects of ozone, hydrogen, carbon monoxide, and exhaust gas recirculation (EGR) addition to Haltermann gasoline combustion were investigated. For these additives, laminar and turbulent flame speeds were experimentally determined using spherically propagating premixed flames in a constant volume combustion vessel. Two initial mixture pressures of Po = 1 and 5 bar, two initial mixture temperatures of 358 and 373 K and a range of equivalence ratios (Ф) from 0.5 to 1 were investigated. The additives were added as single, binary and ternary mixtures to Haltermann gasoline over a wide range of concentrations. For the stoichiometric mixture, the addition of 10% H2, 5% CO and 1000 ppm O3 shows remarkable enhancement (80%) in SL0compared to neat Haltermann gasoline. In addition, for this same blend, increasing the mixture initial temperature and pressure results in a significant increase in SL0compared to the neat gasoline.

A two-stage PPCI-diffusion combustion process recently showed good potential to enable clean and fuel-efficient gasoline compression ignition (GCI) combustion at medium-to-high loads. By conducting closed-cycle 3-D CFD combustion analysis, a further step was undertaken in this work to evaluate and optimize the PPCI-diffusion combustion strategy at a full load operating point (2000rpm-23.5 bar IMEPcc) while keeping engine-out NOx below 1 g/kWh. The light-duty GCI engine used in this investigation featured a custom-designed piston bowl geometry at a 17.0 compression ratio (CR), a high pressure diesel fuel injection system, and advanced single-stage turbocharging. A split fuel injection strategy was used to enable the two-stage PPCI-diffusion combustion process. First, the injector spray pattern and swirl ratio effects were evaluated. In-cylinder air utilization and the PPCI-diffusion combustion process were notably influenced by the closed-cycle combustion system design.

Steer by Wire systems provide many benefits in terms of functionality, and at the same time present significant challenges too. Chief among them is to make sure that an acceptable steering feel is achieved. Various aspects of this subjective attribute will be defined mathematically. A control system that is architected specifically to meet these challenges is presented. Furthermore, the design is made such that it would be robust to tire and loading variations. Supporting vehicle data and model results are shown as needed.

Production software validation is critical during software development, allowing potential quality issues that could occur in the field to be minimized. By developing automated and repeatable software test methods, test cases can be created to validate targeted areas of the control software for confirmation of the expected results from software release to release. This is especially important when algorithm/software development timing is aggressive and the management of development activities in a global work environment requires high quality, and timely test results. This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The powertrain model used within the HIL consists of an engine model and a detailed automatic transmission dynamics model. The model runs in an OPAL-RT TestDrive based HIL system.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

A methodology has been developed for identifying the combination of battery characteristics which lead to least-cost electric vehicles. Battery interrelationships include specific power vs, specific energy, peak power vs. specific energy and DOD, cycle life vs. DOD, cost vs. specific energy and peak power, and volumetric and battery size effects. The method is illustrated for the “second car” mission assuming lead/acid batteries. Reductions in life-cycle costs associated with future battery research breakthroughs are estimated using a sensitivity technique. A research prioritization system is described.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

A key quantity for use in engine control is the exhaust manifold pressure. For production applications it is an important component in the calculation of the engine volumetric efficiency, as well as EGR flow and residual fraction. For cost reasons, however, it is preferable to not have to measure the exhaust manifold pressure for production applications. For that reason, it is advantageous to develop a model for estimating the exhaust manifold pressure in production application software that is small, accurate, and simple to calibrate. In this paper, a mean-value model for calculating the exhaust manifold pressure is derived from the compressible flow equation, treating the exhaust system as a fixed-geometry restriction between the exhaust manifold and the outlet of the tailpipe. Validation data from production applications is presented.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

This paper presents an alternative brake that uses two floating discs, with four rubbing surfaces, to provide a step change improvement in performance over existing products. The paper details the development of this product highlighting the test data, which demonstrates the significant improvements in specific torque, fluid consumption and cooling rates. The design retains conventional materials, existing processes and fits within current package constraints. The sliding discs, which compensate for wear, allow opportunities to simplify the caliper to a fixed design and allow integration with the steering knuckle. Performance, refinement and durability test results indicate the current status of the design as implemented on a small passenger car and an SUV, and show its compatibility with existing vehicle brake control systems. Design options to implement this technology within current and future vehicle systems are also described.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.