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Under the competitive pressure of automotive industry the customer’s focus is on a vehicle’s quality perception. Side door closing efforts make a considerable share of the overall impression as the doors are the first physical and haptic interface to the customer. Customer’s subjective feeling of vehicle quality demands for detailed analysis of each contributor of door closing efforts. Most contributors come from kinematic influences. Beside the losses due to mechanical subsystems like the checkarm, latch or hinge friction one of the biggest impacts originates from the pressure spike that builds up due to air being pushed into the cabin. Subject of this publication is to discuss the dependencies of closing efforts on cabin pressure and air extraction. It demonstrates an approach to simulate the development of the air pressure during door closing motions and the validation of the simulation method with the “EZ-Slam” measurement device.

Pyrolysis processing of solid waste in space will inevitably lead to carbon formation as a primary pyrolysis product. The amount of carbon depends on the composition of the starting materials and the pyrolysis conditions (temperature, heating rate, residence time, pressure). Many paper and plastic materials produce almost no carbon residue upon pyrolysis, while most plant biomass materials or human wastes will yield up to 20-40 weight percent on a dry, as-received basis. In cases where carbon production is significant, it can be stored for later use to produce CO2 for plant growth. Alternatively it can be partly gasified by an oxidizing gas (e.g., CO2, H2O, O2) in order to produce activated carbon. Activated carbons have a unique capability of strongly absorbing a great variety of species, ranging from SO2 and NOx, trace organics, mercury, and other heavy metals.

This paper highlights and discusses the development and deployment of an enterprise-level tool infrastructure that fits into the production build environment of Ford Motor Company. A particular focus is on navigating bottlenecks and pitfalls that arise with the adoption of a model-based software development process. This includes provisions to support centralized data and architecture artifact management (including version control across the lifecycle of the software), support to integrate and manage legacy software artifacts, support to archive and bookshelf development milestones, and last but not least, built-in intelligence to spot potential sources of software defects early in the development stage.

Ford Motor Company (FMC) plans to adopt ISO 14229 - Unified Diagnostic Services (UDS) as the sole diagnostic vehicle protocol. All Ford brands, including Ford, Lincoln-Mercury, Jaguar, Land Rover, Volvo, and Mazda, will eventually use this protocol. Migrating from the many diagnostic protocols currently in use today to a single common protocol presents many challenges. However, the long-term benefits of using a single industry-standard protocol throughout the company far outweigh the short-term difficulties. This paper will present the issues facing Ford as it makes the transition to one common protocol as well as outline the potential benefits.

This study investigates the use of a characteristic reaction time as a possible method to speed up automotive knock calculations. In an earlier study of HCCI combustion it was found that for ignition at TDC, the ignition delay time at TDC conditions was required to be approximately 10 crank angle degrees (CAD), regardless of engine speed. In this study the analysis has been applied to knock in SI engines over a wide range of engine operating conditions including boosted operation and retarded combustion phasing, typical of high load operation of turbocharged engines. Representative pressure curves were used as input to a detailed kinetics calculation for a gasoline surrogate fuel mechanism with 312 species. The same detailed mechanism was used to compile a data set with traditional constant volume ignition delays evaluated at the peak pressure conditions in the end gas assuming adiabatic compression.

High frequency variations in crankcase pressure have been observed in Inline-four cylinder (I4) engines and an understanding of the causes, frequency and magnitude of these variations is helpful in the design and effective operation of various engine systems. This paper shows through a review and explanation of the physics related to engine operation followed by comparison to measured vehicle data, the relationship between crankcase volume throughout the engine cycle and the observed pressure fluctuations. It is demonstrated that for a known or proposed engine design, through knowledge of the key engine design parameters, the frequency and amplitude of the cyclic variation in crankcase pressure can be predicted and thus utilized in the design of other engine systems.

The ported shroud casing treatment for turbocharger compressors offers a wider operating flow range, elevated boost pressures at low compressor mass flow rates, and reduced broadband whoosh noise in spark-ignition internal combustion engine applications. However, the casing treatment elevates tonal noise at the blade-pass frequency (BPF). Typical rotational speeds of compressors employed in practice push BPF noise to high frequencies, which then promote multi-dimensional acoustic wave propagation within the compressor ducting. As a result, in-duct acoustic measurements become sensitive to the angular location of pressure transducers on the duct wall. The present work utilizes a steady-flow turbocharger gas stand featuring a unique rotating compressor inlet duct to quantify the variation of noise measured around the duct at different angular positions.

Using a fast-sampling valve, residual-fraction levels were determined in a 2.0L spark-ignited production engine, over varying engine operating conditions. Individual samples for each operating condition were analyzed by gas-chromatography which allowed for the determination of in-cylinder CO and CO2 levels. Through a comparison of in-cylinder measurement and exhaust data measurements, residual molar fraction (RMF) levels were determined and compared to analytical results. Analytical calculations were performed using the General Engine SIMulation (GESIM) which is a steady state quasi-dimensional engine combustion cycle simulation. Analytical RMF levels, for identical engine operating conditions, were compared to the experimental results as well as a sensitivity study on wave-dynamics and heat transfer on the analytically predicted RMF. Similarly, theoretical and experimental NOx emissions were compared and production sensitivity on RMF levels explored.

Many different chemical measurement methodologies of air to fuel (A/F) ratio have been documented in technical publications [1, 2, 3, 4, 5, 6, 7 and 8]. Each of these methods is derived from the same physical principles but they vary in simplifying assumptions and physical constants. All are well proven over time with test data, producing excellent results near stoichiometry. Few technical publications, however, include data at lean A/F ratios and none include data at the very lean A/F ratios at which new high-technology engines such as gasoline direct injection spark ignition engines may operate with stratified combustion. This paper presents a comparison of three A/F ratio measurement methods based on exhaust gas composition. The methods produce similar results when applied to feed-gas emissions, but results vary when applied to post-catalyst emissions measurements. Some theories that may explain this behavior are discussed.

Design, operations and maintenance activities in aviation involve analysis of variety of aviation data. This data is typically in disparate formats making it difficult to use with different software packages. Use of a self-describing and extensible standard called XML provides a solution to this interoperability problem. While self-describing nature of XML makes it easy to reuse, it also increases the size of data significantly. A natural solution to the problem is to compress the data using suitable algorithm and transfer it in the compressed form. We found that XML-specific compressors such as Xmill and XMLPPM generally outperform traditional compressors. However, optimal use of Xmill requires of discovery of optimal options to use while running Xmill. Manual discovery of optimal setting can require an engineer to experiment for weeks.

Connected vehicle data unlock compelling solutions for vehicle owners and fleet managers. In selecting machine learning algorithms for use in predicting a connected vehicle signal value, time series dependency is critical to understand. With little to no time series dependency, conventional machine learning models may be used with a feature set that has few or no lag variables. If there is a lot of time series dependency including long-term dependencies, deep learning architectures like variants of recurrent neural networks (RNN) may be a better approach. Further, at any time step, RNN features may be specified to use some number of past time steps to predict the latest value. This paper seeks to identify time series dependency of connected vehicle signals, and selection of the number of time steps to look back in the features set to minimize error.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

A typical automotive catalytic converter is constructed with a ceramic substrate and a steel shell. Due to a mismatch in coefficients of thermal expansion, the steel shell will expand away from the ceramic substrate at high temperatures. The gap between the substrate and shell is usually filled with a fiber composite material referred to as “mat.” Mat materials are compressed during assembly and must maintain an adequate pressure around the substrate under extreme temperature conditions. The container deformation measurement procedure is used to determine catalytic converter shell expansion during and after a period of hot catalytic converter operation. This procedure is useful in determining the potential physical durability of a catalytic converter system, and involves measuring converter shell expansion as a function of inlet temperature. A post-test dimensional measurement is used to determine permanent container deformation.

While not commonly in production today, Gasoline Particulate Filters (GPFs) are likely to see widespread deployment to meet stringent EU6.2 and China particulate number (PN) standards. In many ways the operating conditions for GPFs are orthogonal to those of their diesel counterparts, and this leads to different and interesting requirements for the control strategy. We will present some generic system architectures for exhaust systems containing a GPF and will lay out an architecture for the GPF control strategy components which include: regeneration assist feature, soot estimation algorithm, GPF protection. The regeneration assist feature uses spark retard to increase exhaust temperature. The soot estimation algorithm describes how we can estimate soot from an open loop model or from a normalized pressure metric. The GPF protection feature controls oxygen flow to limit the soot burn rate. We will show validation data of the control strategy under different operating conditions.

This paper describes and experimentally compares various strategies to control the gas flows of a high speed automotive diesel engine equipped with external exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT). The strategies differ in their choice of controlled variables and design approaches. Some of the strategies rely on standard measurements such as intake manifold pressure and compressor mass air flow. Others measure or estimate less readily available quantities such as exhaust manifold pressure and EGR flow fraction. The focus of this paper is on the experimental details of controller implementation. The control schemes are evaluated on their emissions and fuel economy over the extra urban part of the new European drive cycle. The implementation effort and chronometric load are also compared.

As systems necessarily become more integrated and increasingly complex through market demands for more features, technical risks and therefore business risks increase. It becomes correspondingly harder to show that the properties desired of these Systems of Systems (SoS) actually hold under normal or abnormal operation. In particular, it is hard to detect emergent properties of a SoS because properties of individual systems are not necessarily compositional, especially during failure. This paper describes the objectives of a project addressing the problem of Dependable System of Systems and other related research in the field of Automotive Electronics. The capability being developed is based upon the scalable ‘Assumption-Commitment’[1] paradigm so that it can be applied to large and complex systems of systems.

Computational Fluid Dynamic (CFD) analysis was performed using FLUENT to analyze the fluid thermal performance of a Battery cell container for the Ford Fusion Hybrid Electric Vehicle. The objective of the design was to maintain the cells in their desired operating temperature range with a near uniform temperature among the battery cells in the container, while minimizing energy losses associated with the pressure drop. Groupings of multiple such containers were assembled for bench test confirmation. Excellent agreement was obtained for air side pressure drop between the CFD and hardware physical properties. Multiple design iterations were made to improve the baseline design. Ultimately, the thermal gradient within the physical property was reduced to 1.8°C with a minimal increase in system pressure drop.

Lean Six Sigma is an approach that is gaining momentum both in manufacturing and service industries. Design for Lean Six Sigma (DFLSS) is an outgrowth of the DFSS and Lean Six Sigma approaches. The essence of DFLSS is to ensure design quality and predictability during the early design phases and the approach employs a structured integrated product development methodology and a comprehensive set of robust tools to drive product quality, innovation, faster time to market, and lower product costs. When it comes to automotive Product Development, applying lean principles and DFSS together becomes more of a challenge within the existing PD system. While the benefits of DFLSS present an attractive proposition in a fiercely competitive market it brings its own challenges as to how to deploy it for maximum benefits. This paper examines the challenges, potential and opportunities for DFLSS in the automotive industry and presents a vision for integrating it in to the Product Development System.

This paper discusses a Design For Six Sigma (DFSS) approach to a driveline system NVH design process as used on a RWD IRS (Independent Rear Suspension) vehicle program. It is shown how this approach helped understand the ways variability in the driveline system (mount properties and locations) affected output to the rest of the vehicle. A series of CAE and DFSS tools were used to deliver a distribution that described the vehicle Driveline Roughness performance, to identify the critical control factors that affected the performance, and to reduce the response sensitivity to variability of these critical factors. Other driveline issues were factored into the process indirectly.

Although great advances have been made over the last two decades in the automotive structural design process, tradition and experience guide many design choices even today. The need for innovative tools is stronger now more than ever before as the design engineer is confronted with more complex, often contradictory design requirements such as cost, weight, performance, safety, time to market, life cycle, aesthetics, environmental impact, changes in the industry's business models, etc. The ever-increasing use of optimization tools in engineering design generates solutions that are very close to the limits of the design constraints, hardly allowing for tolerances to compensate for uncontrollable factors such as manufacturing imperfections. Optimum designs developed without consideration of uncertainty can lead to non-robust designs.