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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).

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 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

Industrial robotic arms and manipulators are systems that offer technological advances in automation, production, and logistical processes. Therefore, it is vital to understand and analyze the reachability and dexterity of such manipulators. This paper presents a reconfigurable algorithm for evaluation and 3D visual representation of the total workspace and singularity space of two and three degrees of freedom open-ended kinematic chains. A manipulator's performance is greatly depreciated at or near singular regions which may occur as subset(s) in its complete workspace. It is therefore crucial to understand the functional workspace of a manipulator for an enhanced performance in an industrial setting. The implementation of this algorithm requires two inputs namely; the joint type(s), rotational (R) or translational (T), and the Denavit-Hartenberg (D-H) parameters of the manipulator.

A vehicle undergoing longitudinal or lateral accelerations experiences load transfer, dynamically changing the normal load carried by each tire. Conventional braking systems are designed only to work adequately over a large range of conditions, but often ignore the dynamic state of the tire's normal load. Fortunately, new developments in braking system hardware give designers more control over the application of braking pressures. By identifying the tires that carry increased normal load, and biasing the braking system toward those tires, total braking force can be increased. The purpose of this research is to investigate advantages of open-loop load transfer based active brake pressure distribution. By estimating the tractive ability of the tires as a function of measurable vehicle conditions, brake pressure can be applied in proportions appropriate for the current dynamic state of the vehicle, referred to as Active Brake Proportioning (ABP).

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

Building a vehicle model with sufficient accuracy for fuel economy analysis is a time-consuming process, even with the modern-day simulation tools. Obtaining the right kind of data for modeling a vehicle can itself be challenging, given that while OEMs advertise the power and torque capability of their engines, the efficiency data for the components or the control algorithms are not usually made available for independent verification. The U.S. Department of Energy (DOE) funds the testing of vehicles at Argonne National Laboratory, and the test data are publicly available. Argonne is also the premier DOE laboratory for the modeling and simulation of vehicles. By combining the resources and expertise with available data, a process has been created to automatically develop a model for any conventional vehicle that is tested at Argonne. This paper explains the process of analyzing the publicly available test data and computing the parameters of various components from the analysis.

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

Two limited-production hybrid electric vehicles (HEVs) - a 1988 Japanese model Toyota Prius and a 2000 Honda Insight - were tested at Argonne National Laboratory to collect data from vehicle component and systems operation. The test data are used to analyze operation and efficiency and to help validate computer simulation models. Both HEVs have FTP fuel economy greater than 45 miles per gallon and also have attributes very similar to those of conventional gasoline vehicles, even though each HEV has a unique powertrain configuration and operation control strategy. The designs and characteristics of these vehicles are of interest because they represent production technology with all the compromises for production included. This paper will explore both designs, their control strategies, and under what conditions high fuel economy was achieved.

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory's (Argonne's) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

Electric power steering (EPS) systems have been used to replace hydraulic power steering systems in vehicles. How to enhance the safety and reliability while reducing the manufacturing cost of EPS systems is still of strong interest to the automotive industry. In this paper, modeling analysis is conducted for advanced control of electric power steering system. Specifically, a mathematical model is proposed for a column-mounted EPS system and then a simplified model for control design purpose is proposed. Issues that need to be addressed, such as noise/ disturbance attenuation as well as potential fault detection/tolerance are analyzed. Simulation using CarSim™ is also presented for an optimal control design using the simplified model as an example to validate the proposed ideas.

Amid all nondestructive testing (NDT) methods Ultrasound is considered the most practically feasible modality for quality assessment and detection of defects in automobile industry. Pattern recognition of the ultrasonic signals gives us important information about the interrogated object. This information includes size, geometric shape and location of the defect zone. However, this would not be straightforward to extract this information from the backscattered echoes due to the overlapping signals and also the presence of noise. Here in this study, we suggest a new method for classification of different defects in inspection of adhesively bonded joint. At the first step of this method, the problem of parameter estimation of the reflected echoes is defined in a Maximum Likelihood Estimation (MLE) framework. Then a space alternating generalized Expectation Maximization (SAGE) algorithm is implemented to solve the MLE problem.

Expanding upon the authors’ previous work which utilized a GT-Power model of the Cooperative Fuels Research (CFR) engine under Research Octane Number (RON) conditions, this work defines the boundary conditions of the CFR engine under Motored Octane Number (MON) test conditions. The GT-Power model was validated against experimental CFR engine data for primary reference fuel (PRF) blends between 60 and 100 under standard MON conditions, defining the full range of interest of MON for gasoline-type fuels. The CFR engine model utilizes a predictive turbulent flame propagation sub-model, and a chemical kinetic solver for the end-gas chemistry. The validation was performed simultaneously for thermodynamic and chemical kinetic parameters to match in-cylinder pressure conditions, burn rate, and knock point prediction with experimental data, requiring only minor modifications to the flame propagation model from previous model iterations.

In an earlier paper, the authors described how Model-Based System Engineering could be utilized to provide a virtual Hardware-in-the-Loop simulation capability, which creates a framework for the development of virtual ECU software by providing a platform upon which embedded control algorithms may be developed, tested, updated, and validated. The development of virtual ECU software is increasingly valuable in automotive control system engineering because vehicle systems are becoming more complex and tightly integrated, which requires that interactions between subsystems be evaluated during the design process. Variational analysis and robustness studies are also important and become more difficult to perform with real hardware as system complexity increases. The methodology described in this paper permits algorithm development to be performed prior to the availability of vehicle and control system hardware by providing what is essentially a virtual integration vehicle.

The effects of engine noise in plastic manifolds has been a subject of study in the automotive Industry. Several SAE papers have been published on the subject. Most testing described requires access to engine dynamometers and other elaborate equipment. As part of a general study of plastic intake manifold noise characteristics, this study was undertaken to develop a synthesis bench for enabling low cost noise testing of plastic induction systems including plastic manifolds. Computer simulation of engine intake noise was used as part of a correlation between the plastic manifold synthesis bench and actual engine measurements. The Fast Fourier Transform (FFT) analysis provided analogous results between the predicted theoretical and two measured signals with a fundamental frequency at approximately 80 Hz. Qualitative and statistical comparisons of the time domain signals also proved equally favourable. Recommendations are included for further development of this approach.

The paper describes a study conducted by the University of Windsor Vehicle Dynamics and Control Research Group into the stability of coupled vehicles, e.g., truck-trailer combinations. Several instabilities associated with truck-trailer combinations have been well documented, and have been predicted using mathematical models. Despite having relatively low complexity the classic truck-trailer model, a simple two body, three degree of freedom, linear model has been used extensively in coupled vehicle stability analyses. The aim of the presented work was to extend the conventional coupled vehicle analysis with a set of more elaborate mathematical models evaluating various vehicle configurations. Using in-house multibody dynamics software the linearized equations of motion of three dimensional models were automatically generated for various coupled vehicle configurations with general and military applications. Stability analyses were conducted over a range of expected operating speeds.

The performance of Gasoline Direct Injection (GDI) engines is governed by multiple physical processes such as the internal nozzle flow and the mixing of the liquid stream with the gaseous ambient environment. A detailed knowledge of these processes even for complex injectors is very important for improving the design and performance of combustion engines all the way to pollutant formation and emissions. However, many processes are still not completely understood, which is partly caused by their restricted experimental accessibility. Thus, high-fidelity simulations can be helpful to obtain further understanding of GDI injectors. In this work, advanced simulation and experimental methods are combined in order to study the spray characteristics of two different 3-hole GDI injectors.

Knock integrals and corresponding ignition delay (τ) correlations are often used in model-based control algorithms in order to predict ignition timing for kinetically modulated combustion regimes such as HCCI and PCCI. They can also be used to estimate knock-inception during conventional SI operation. The purpose of this study is to investigate the performance of various τ correlations proposed in the literature, including those developed based on fundamental data from shock tubes and rapid compression machines, those based on predictions from isochoric simulations using detailed chemical kinetic mechanisms, and those deduced from data of operating engines. A 0D engine simulation framework is used to compare the correlation performance where evaluations are based on the temperatures required at intake valve closure (TIVC) in order to achieve a fixed CA50 point over a range of conditions.