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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.

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.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

The automotive and aerospace industries are increasingly using the light-weight material to improve the vehicle performance. However, using light-weight material can increase the airborne and structure-borne noise. A special attention needs to be paid in designing the structures and measuring their dynamics. Conventionally, the structure is excited using an impulse hammer or a mechanical shaker and the response is measured using uniaxial or multi-axial accelerometers to obtain the dynamics of the structure. However, using contact-based transducers can mass load the structure and provide data at a few discrete points. Hence, obtaining the true dynamics of the structure conventionally can be challenging. In the past few years, stereo-photogrammetry and three-dimensional digital image correlation have received special attention in collecting operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure.

With many advantages of GDI technology, one major disadvantage is high HC emissions. The primary goal of this study is to determine the optimum values of engine parameters that would result in maximum power output from a GDI engine, with complete combustion, minimum hydrocarbon (HC) emissions, and minimum specific fuel consumption. A two-dimensional engine geometry with a piston-bowl was selected for faster engine CFD simulations. The first part involves a study of the affect of engine parameters on performance and HC emissions. The parameters considered were, equivalence ratio (mass of injected fuel), injection timing, ignition timing, engine RPM, spray cone angle, and velocity of fuel injection. The second part of the study involves determining the optimum values of fuel mass injected, injection timing, and ignition timing in order to maximize power output while limiting the amount of fuel left unburned after the end of the expansion process.

A numerical and experimental study of the use of air motion control, piston bowl shape, and injector configuration on combustion and emissions in diesel engines has been conducted. The objective of this study is to investigate the use of flow control within the piston bowl during compression to enhance fuel air mixing to achieve a uniform air-fuel mixture to reduce soot and NO emissions. In addition to flow control different piston bowl geometries and injector spray angles have been considered and simulated using three-dimensional computational fluid dynamics and experiments. The results include cylinder pressure and emissions measurements and contour plots of fuel mass fraction, soot, and NO. The results show that soot and NO emissions can be reduced by proper flow control and piston bowl design.

The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems alert the distracted drivers by raising alarms. Most of the image based driver drowsiness detection systems face the challenge of failure proof performance in real time applications. Failure in face detection and other important part (eyes, nose and mouth) detections in real time cause the system to skip detections of blinking and yawning in few frames. In this paper, a real time robust and failure proof driver drowsiness detection system is proposed. The proposed system deploys a set of detection systems to detect face, blinking and yawning sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment is used for face detection. This system attained 97% recall in the real time driving dataset collected. The detected face is passed on to ensemble of regression trees to detect the 68 facial landmarks.

The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.

In a competitive engineering business world, there is a constant demand to meet stringent emissions and on board diagnostic (OBD) regulations in a cost-effective manner. Engineers are tasked with the responsibility to innovate and design solutions around cost-cutting measures that involve reducing bill of material costs on the printed circuit board (PCB). Varied features in commercial application specific integrated circuits (ASIC) devices makes it more challenging to create consistent engineering design methods to provide critical inputs for controls and diagnostic strategies. In addition, continuous evolution of the emissions and OBD regulations in the different markets make it challenging for ASIC design manufacturers to evolve their hardware designs quickly. One such input is soak time. Soak time is typically defined as the amount of time the engine has been turned off. Emission controls and OBD algorithms use soak time to enable cold and hot start processing strategies.

Close-coupled or manifold catalysts have been extensively employed to reduce emissions during cold start by achieving quick catalyst light-off. These catalysts must have good thermal durability, high intrinsic light-off activity and high HC/CO/NOx conversions at high temperature and flow conditions. A number of studies have been dedicated to engine control, manifold design and converter optimization to reduce cold start emissions. The current paper focuses on the effect of catalyst design parameters and their performance response to different engine operating conditions. Key design parameters such as catalyst formulation (CeO2 vs. non CeO2), precious metal loading and composition (Pd vs. Pd/Rh), washcoat loading, catalyst thermal mass, substrate properties and key application (in use) parameters such as catalyst aging, exhaust A/F ratio, A/F ratio modulation, exhaust temperature, temperature rise rate and exhaust flow rate were studied on engine dynamometers in a systematic manner.

In recent years, gasoline direct injection (GDi) engines have been popular due to their inherent potential for reduction of exhaust emissions and fuel consumption to meet stringent EPA standards. These engines require high-pressure fuel injection in order to improve the atomization process and accelerate mixture preparation. The high-pressure fuel pump is an essential component in the GDi system. Therefore, understanding the flow characteristics of this device and its associated behavior is critical for improving the performance of this category of engines. In this paper, the fluid flow characteristics in a high-pressure single-piston pump for use in GDi engines are analyzed using 1-D LMS Imagine.Lab AMESim system and 3-D Ansys Fluent computational fluid dynamics (CFD) models. The flow rate of the fuel pump under various cam speeds has been examined along with characteristics of the pump's control valve.

A hardware and software architecture suitable for a safety-critical steer-by-wire systems is presented. The architecture supports three major failure modes and features several safety protocols and mechanisms. Failures due to component failures, software errors, and human errors are handled by the architecture and safety protocols. A test implementation using replicated communication channels, controllers, sensors, and actuators has been performed. The test implementation uses the CAN protocol, Motorola S12 microcontrollers, and Microchip MCP250XX components with a steering wheel and road wheel simulator. The focus of the paper is on the application level, using system engineering principles which incorporate a holistic approach to achieve safety at various levels.

An integrated optimization system has been developed to combine optimization algorithms with Finite Element Analysis for airbag design. A number of industry standard software packages are employed to work in coherence to complete the optimization procedure automatically with minimal user intervention. The system can be easily tailored to fit multiple performance requirements and various design constraints for different airbag systems. Compared with the commonly used Design of Experiment (DOE) method, time and computer resources requirements are greatly curtailed. The integrated optimization system was successfully used in single-chamber and dual-chamber airbag optimizations. The results proved the effectiveness of the system and demonstrated its capability in product design.

An HPLC (High Performance Liquid Chromatography) method to measure the concentration of six organic acids in E-85 fuel has been developed. A three point calibration curve is established using standard solutions of the following organic acids: formic acid, acetic acid, propionic acid, butyric acid, glycolic acid and citric acid. An internal standard (maleic acid) is used to monitor HPLC system suitability and peak retention time stability. The method utilizes UV detection at 210 nm to detect and quantify the levels of each acid in E-85 fuel. Test results from nine commercially available E-85 fuel samples are reported. Analytical method validation was achieved by performing and confirming system suitability or injection repeatability (percent relative standard deviation ≤ 3%), calibration curve linearity (R2 ≥ 0.999), analysis repeatability (standard deviation < 1 mg/L) and recovery (percent recovery 91 - 102%).

Bond graph modeling is a powerful technique to study the complex interactions occurring between various components in a system. A few investigations were carried out to study vehicle dynamics using Bondgraphs, but are limited to 2 degree of freedom systems [1,2&3]. In this work, a 4-DOF-vehicle model was developed using bond graphs. A frequency response analysis was also carried out to study the natural frequencies. This model was later validated using Lagrangian principles. The results correlated well for a typical passenger car using the manufacturer supplied information available in the public domain.

This paper reviews the state of the art on analytical design of cockpit modules in two most crucial performance categories: safety and comfort. On safety, applications of finite element analysis (FEA) for achieving robust designs that meet FMVSS 201, 208 and 214 requirements and score top frontal and side NCAP star-ratings are presented. On comfort, focus is placed on Noise, Vibration and Harshness (NVH) performance. Cutting-edge analytical tools for Buzz, Squeak and Rattle (BSR) avoidance and passenger compartment noise reduction are demonstrated. Most of the analytical results shown in this paper are based on the development work of a real-life application program. Correlations between the analytical results and physical test results are included. Examples of Computational Fluid Dynamics (CFD) analysis for climate control are also included. At the end, the road map toward 100 percent virtual prototyping and validation is presented.

The perceived interior noise has been one of the major driving factors in the design of automotive interior assemblies. Buzz, Squeak and Rattle (BSR) issues are one of the major contributors toward the perceived quality in a vehicle. Traditionally BSR issues have been identified and rectified through extensive hardware testing. In order to reduce the product development cycle and minimize the number of costly hardware builds, however, one must rely on engineering analysis and simulation upfront in the design cycle. In this paper, an analytical and experimental study to identify potential BSR locations in a cockpit assembly is presented. The analytical investigation utilizes a novel and practical methodology, implemented in the software tool Nhance.BSR, for identification and ranking of potential BSR issues. The emphasis here is to evaluate the software for the BSR predictions and the identification of modeling issues, rather than to evaluate the cockpit design itself for BSR issues.

Vehicle Dynamics play an important role in responsiveness of a vehicle. The performance of a vehicle depends on its ride and handling characteristics [1]. Handling is a measure of the directional response of a vehicle and one of the important characteristics from the vehicle dynamics point of view. The directional response of a vehicle depends on the dynamics of the steering system. A good steering control provides an accurate feedback about how the vehicle reacts to the road. In this paper, the powerful techniques of Bond graphs and state equations [2] are used to design and analyze the dynamics of a manual rack and pinion steering system. The author obtains the transfer function between the Angle of rotation of front tire and the Angle of rotation of steering wheel. The overall steering ratio of the bond graph modeled steering system is compared with the overall ratio of a similar vehicle to validate the model.

Robust Engineering techniques developed by Taguchi have traditionally applied to the optimization of engineering designs. Robust Engineering methods also may be applied to software testing of ECU algorithms. The net result is an approach capable of improving the software algorithm in one of two ways. First the approach can identify the range of areas which prove problematic to the software such that a robust solution may be developed. Conversely, the approach can be used as a general strategy to verify that the software is robust over the range of inputs tested. The robust engineering methods applied to software testing utilize orthogonal array experiments to test software over a range of inputs. The actual software trials are best performed in the simulation environment and also via automated test hardware in the loop configurations in realtime. This paper outlines a process for applying Robust Engineering methods to software testing.