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This paper presents part of the analytical work performed for the development and optimization of the 3.5L EcoBoost combustion system from Ford Motor Company. The 3.5L EcoBoost combustion system is a direct injected twin turbocharged combustion system employing side-mounted multi-hole injectors. Upfront 3D CFD, employing a Ford proprietary KIVA-based code, was extensively used in the combustion system development and optimization phases. This paper presents the effect of intake port design with various levels of tumble motion on the combustion system characteristics. A high tumble intake port design enforces a well-organized stable motion that results in higher turbulence intensity in the cylinder that in turn leads to faster burn rates, a more stable combustion and less fuel enrichment requirement at full load.

A CAE SEA-based optimization process for the enhancement of vehicle high frequency NVH applications is developed and validated. The CAE simulation, based on statistical energy analysis (SEA) theory [1], has been used to analyze high frequency NVH responses for the vehicle sound package development. However, engineers have always faced two challenges during the vehicle SEA model development. One is to create a reliable SEA model, which is correlated well with hardware test data. The other is to have a systematic approach by using the correlated model to design effective and cost efficient sound package to improve vehicle interior quietness. The optimization process presented in this paper, which integrates analysis, design sensitivity, and optimization solver, has been developed to address the challenges and to serve the needs. A non-correlated Sport Utility Vehicle (SUV) and a correlated midsize car models were used to demonstrate the capability of the proposed optimization process.

Software development for automotive application requires several iterations in order to tune parameters and strategy logic to operate accordantly with optimal performance. Thus, in this paper we present an optimizer method and tool used to tune calibration parameters related to torque estimation for a hybrid automatic transmission application. This optimizer aims to minimize the time invested during the software calibration and software development phases that could take significant time in order to cover the different driving conditions under which a hybrid automatic transmission can operate. For this reason, an optimization function based on the Nelder-Mead simplex algorithm using Matlab software helps to find optimized calibration values based on a cost function (square sum error minimization).

Near-Field Acoustical Holography (NAH) is an inverse process in which sound pressure measurements made in the near-field of an unknown sound source are used to reconstruct the sound field so that source distributions can be clearly identified. NAH was originally based on performing spatial transforms of arrays of measured pressures and then processing the data in the wavenumber domain, a procedure that entailed the use of very large microphone arrays to avoid spatial truncation effects. Over the last twenty years, a number of different NAH methods have been proposed that can reduce or avoid spatial truncation issues: for example, Statistically Optimized Near-Field Acoustical Holography (SONAH), various Equivalent Source Methods (ESM), etc.

Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

When performing trajectory planning for robotic applications, there are many aspects to consider, such as the reach conditions, joint and end-effector velocities, accelerations and jerk conditions, etc. The reach conditions are dependent on the end-effector orientations and the robot kinematic structure. The reach condition feasibility is the first consideration to be addressed prior to optimizing a solution. The ‘functional’ work space or work window represents a region of feasible reach conditions, and is a sub-set of the work envelope. It is not intuitive to define. Consequently, 2D solution approaches are proposed. The 3D travel paths are decomposed to a 2D representation via radial projections. Forward kinematic representations are employed to define a 2D boundary curve for each desired end effector orientation.

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).

Designing a durability test for an automatic transmission that appropriately reflects customer usage during the lifetime of the vehicle is a formidable task; while the transmission and its components must survive severe usage, overdesigning components leads to unnecessary weight, increased fuel consumption and increased emissions. Damage to transmission components is a function of many parameters including customer driving habits and vehicle and transmission characteristics such as weight, powertrain calibration, and gear ratios. Additionally, in some cases durability tests are required to verify only a subset of the total parameter space, for example, verifying only component modifications. Lastly, the ideal durability test is designed to impose the worst case loading conditions for the maximum number of internal components, be as short as practicable to reduce testing time, with minimal variability between tests in order to optimize test equipment and personnel resources.

With an emerging need for gasoline particulate filters (GPFs) to lower particle emissions from gasoline direct injection (GDI) engines, studies are being conducted to optimize GPF designs in order to balance filtration efficiency, backpressure penalty, filter size, cost and other factors. Metal fiber filters could offer additional designs to the GPF portfolio, which is currently dominated by ceramic wall-flow filters. However, knowledge on their performance as GPFs is still limited. In this study, modeling on backpressure and filtration efficiency of fibrous media was carried out to determine the basic design criteria (filtration area, filter thickness and size) for different target efficiencies and backpressures at given gas flow conditions. Filter media with different fiber sizes (8 - 17 μm) and porosities (80% - 95%) were evaluated using modeling to determine the influence of fiber size and porosity.

A passenger airbag is an important part of a vehicle restraint system which provides supplemental protection to an occupant in a crash event. New Federal Motor Vehicle Safety Standards No. 208 requires considering multiple crash scenarios at different speeds with various sizes of occupants both belted and unbelted. The increased complexity of the new requirements makes the selection of an optimal airbag shape a new challenge. The aim of this research is to present an automated optimization framework to facilitate the airbag shape design process by integrating advanced tools and technologies, including system integration, numerical optimization, robust assessment, and occupant simulation. A real-world frontal impact application is used to demonstrate the methodology.

Capacity planning is one of the major factors in saving capital and avoiding unnecessary costs in any manufacturing system particularly large original equipment manufacturers (OEMs). However, many manufacturing systems still suffer from huge costs incurred due to a lack of applying a robust capacity planning optimization model. Most of the developed models in literature do not consider real-life situations in manufacturing systems and, hence, are not easy to implement. In this paper, a novel capacity planning optimization model considers various important features of a manufacturing system. The objective function of the model is to minimize the weighted sum of the total number of assets and changeovers. A unique feature of the developed model is the capability of providing the number of additional required assets of each type in case the existing assets are not capable of covering the entire demand.

Excessive vibration and poor controllability occur in many mobile fluid power applications, with negative consequences as concerns operators' health and comfort as well as machine safety and productivity. This paper addresses the problem of reducing oscillations in fluid power machines presenting a novel control technique of general applicability. Strong nonlinearities of hydraulic systems and the unpredictable operating conditions of the specific application (e.g. uneven ground, varying loads, etc.) are the main challenges to the development of satisfactory general vibration damping methods. The state of the art methods are typically designed as a function of the specific application, and in many cases they introduce energy dissipation and/or system slowdown. This paper contributes to this research by introducing an energy efficient active damping method based on feedback signals from pressure sensors mounted on the flow control valve block.

There are many challenges in implementing grid aware plug-in electric vehicle (PEV) charging systems with local load control. New opportunities for innovative load control were created as a result of changes to the 2014 National Electric Code (NEC) about automatic load control definitions for EV charging infrastructure. Stakeholders in optimized dispatch of EV charging assets include the end users (EV drivers), site owner/operators, facility managers and utilities. NEC definition changes allow for ‘over subscription’ of more potential EV charging station load than can be continuously supported if the total load at any time is within the supply system safety limit. Local load control can be implemented via compact submeter(s) with locally hosted control algorithms using direct communication to the managed electric vehicle supply equipment (EVSE).

The capability to drive NVH quality into vehicle frame design is often compromised by the lack of available predictive tools that can be developed and applied within the timeframe during which key architectural design decisions are required. To address this need, a new parametric frame modeling approach was developed and is presented in this paper. This fully parameterized model is capable of fast modal, static stiffness & weight assessments, as well as DSA/optimization for frame design changes. This tool has been proven to be effective in improving speed, quality and impact of NVH hardware decisions.

Transmission system models require a high level of fidelity and details in order to capture the transient behaviors in drivability and fuel economy simulations. Due to model fidelity, manufacturing tolerances, frictional losses and other noise sources, parametrization and tuning of a large number of parameters in the plant model is very challenging and time consuming. In this paper, we used particle swarm optimization as the key algorithm to fast correlate the open-loop performance of an automatic transmission system plant model to vehicle launch and coast down test data using vehicle control inputs. During normal operations, the model correlated well with test data. For error states, due to the lack of model fidelity, the model cannot reproduce the same error state quantitatively, but provided a valuable methodology for qualitatively identifying error states at the early stages.

The objective of this paper is to develop a test capable of ranking lift-gates based on strain concentration levels reflected in fringe characteristics in the known stress/strain concentration and fracture vicinity. First, the system (lift gate glass, adhesive and appliqué) is chosen as test sample since the subsystem (local appliqué) does not exhibit the failure mode observed in the field test. Subsequently, it has been identified that the thermal component (rather than mechanical) is the predominant load by laser scanning vibrometry and confirmed via field test data. Next, digital shearography has been selected as the measurement and visualization tool of strain distribution due to its various advantages such as full field view and non-contact advantages. Finally, the test method has been applied to rank and optimize the structural configuration around appliqués' to reduce / eliminate failure.

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements.

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.

This Paper introduces a modular, flexible and user-friendly dynamic powertrain model of the US Army's High Mobility Multi-Wheeled Vehicle (HMMWV). It includes the DDC 6.5L diesel engine, Hydra-matic 4L80-E automatic transmission, Torsen differentials, transfer case, and flexible drive and axle shafts. This model is used in a case study on transmission optimization design to demonstrate an application of the model. This study shows how combined optimization of the transmission hardware (clutch capacity) and control strategy (shift time) can be explored, and how the models can help the designer understand dynamic interactions as well as provide useful design guidance early in the system design phase.