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Under various real-world scenarios, vehicles can become disabled and require towing. OEMs allow a few options for vehicle wrecker towing that include wheel lift tow using a stinger or towing on a flatbed. These methods entail multiple loading events that need to be assessed for damage to the towed vehicle. OEMs have several testing and evaluation methods in place for those scenarios with majority requiring physical vehicle prototypes. Recent focus to reduce product development time and cost has replaced the need for prototype testing with analytical verification methods. In this paper, the CAE method involving multibody dynamic simulation (MBDS) as well as finite element analysis (FEA) of vehicle flatbed operation, winching onto a flatbed, and stinger-pull towing are discussed.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

This paper describes an indoor electromagnetic compatibility (EMC) testing facility designed for automotive testing over the 60 Hz to 18 GHz frequency range. The facility includes a large TEM cell, covering the 60 Hz to 20 MHz frequency range, and a state-of-the-art anechoic chamber, covering 20 MHz to 18 GHz. In addition to describing the test cells, this paper discusses testing methodology, automatic testing software and calibration. Data is presented depicting the electromagnetic field distribution in each test cell with and without the test vehicle in place. Data is also presented showing a typical field distribution near a high power shortwave transmitter site for correlation purposes.

A new methodology to predict vehicle interior wind noise using CFD results has been developed. The CFD simulation replaces wind tunnel testing for providing flow field information around vehicle greenhouse. A loadcase model based on the CFD results is used to excite an SEA vehicle model. This new approach has been demonstrated on a production vehicle with success for the frequency range of 250-10K Hz. The CAE prediction of interior wind noise agrees within 0.2 sones from wind tunnel testing. The model has been used to evaluate wind noise performance with different door glass design parameters. A glass thickness change from 3.8 mm to 4.8 mm results in 1.1 sones improvement, which agrees well to 1.4 sones improvement from testing. Laminated glass with about 3 times higher damping results in 2.5 sones improvement. This methodology using CFD results can be used in the early stage of product development to impact designs.

A key ingredient in designing products that are more robust is a thorough knowledge of the physics of the ideal function of those products and the physics of the failure modes of those products. We refer to the mathematical functions describing this physics as the transfer functions for that product. Dimensional analysis (DA) is a well known, but often overlooked, tool for reducing the number of experiments needed to characterize a physical system. In this paper, we demonstrate how the application of DA can be used to reduce the size of a DOE needed to estimate transfer functions experimentally. Furthermore, the transfer function generated using DOEs with DA tend to be more general than those generated using larger DOEs directly on the design parameters. With ever-increasing competitive pressure and reduced product development time, a tool such as DA, which can dramatically reduce experimental cost, is an incredibly valuable addition to an engineers toolbox.

Accurate prediction of engine combustion characteristics, especially burn rates, can eliminate a number of hardware iterations, thus resulting in a significant reduction in design and developmental time and cost. An analytical methodology has been developed which allows the determination of part-load MBT spark timing to within 2 crank-angle degrees. The design methodology employs the in-house-developed steady-state quasi-dimensional engine simulation model (GESIM), coupled with full-field measurement of the in-cylinder fluid motion at bottom dead center (BDC) in the computer-controlled water analog system (AquaDyne). The in-cylinder flow-field measurements are obtained using 3-D Particle Tracking Velocimetry (3-D PTV), also developed in-house. In this methodology, the in-cylinder flow measurement data are used to calibrate both the tumble and swirl models in GESIM.

Successful application of turbocharger technology to the Ford 2.3L OHC engine requires management of thermal loading. The 1979/1980 2.3L draw-thru carbureted engine was octane and spark advance limited, requiring calibration to worse case 91 RON conditions. Since no adaptive calibration control was possible relatively late ignition timing compromised engine performance. To improve performance, driveability, fuel economy and emission control, work was initiated in mid 1980 on a blow-thru electronic fuel injected engine scheduled for 1983½ production. Program assumptions were issued specifying a tuned EFI blow-thru inlet system, exhaust manifold mounted AiResearch T03 turbocharger with integral wastegate and 8.0:1 compression ratio with a dished piston. Also included were base engine revisions to accommodate increased thermal and mechanical loads.

The importance of the automotive test facility has increased significantly due in large part to continuous pressure on manufactures to shorten product development cycles. Test facilities are no longer used only for regulatory testing, or development testing in which the effects of small design changes (A-to-B testing) are determined; automotive manufacturers are beginning to use these facilities for final design validation, which has traditionally required on road testing. A host of resources have gone into the design and construction of facilities with the capability to simulate nearly any environment of practical importance to the automotive industry. As a result, there are now a number of test facilities, and specifically wind tunnels, in which engineers can test most aspects of a vehicle's performance in real-world environments.

Virtual Verification (VV) of engineering designs is a critical enabler in the Product Development (PD) process to reduce the time-to-market in a cost efficient manner. Reliance on cost effective VV methods have significantly increased with increased pressure to meet customer expectations for new products at reduced PD budgets. Computer Aided Engineering (CAE) is one such VV method that affords an engineer to make decisions about the ability of the designs to meet the design criteria even before a prototype is built. The first step of the CAE process is meshing which is a time consuming, manual and laborious process. Also mesh development time and accuracy significantly varies with the (1) component (trim body, engine, suspension, brakes, etc.), (2) features predominantly occurring in the component (welds, ribs, fillets, etc.), meshing guidelines based on which the model needs to be developed (durability, safety, NVH, etc.), and the expertise of the meshing engineer involved.

During the Product Development Process, the experimental engineers try to acquire the most reliable data from Proving Grounds early on the development process, aiming to support CAE model correlation and in this way ensuring that the vehicle is capable of withstanding customer loads. Those data, from Proving Grounds, are correlated to the most severe customer's usage and public road conditions. The proposal of this paper is to analyze how tire pressure affects structural durability, since safety, performance and fuel economy were already discussed on other opportunities. Tire pressure is important because it's one variable where the customer can monitor and act and because TPMS (Tire Pressure Measurement System) is not available on most vehicles sold in the Brazilian Market,

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

Automotive Product Development organisations are challenged with ever increasing levels of systems complexity driven by the introduction of new technologies to address environmental concerns and enhance customer satisfaction within a highly competitive and cost conscious market. The technical difficulty associated with the engineering of complex automotive systems is compounded by the increase in sophistication of the control systems needed to manage the integration of technology packages. Most automotive systems have an electro-mechanical structure with control and software features embedded within the system. The conventional methods for design analysis and synthesis are engineering discipline focused (mechanical, electrical, electronic, control, software).

In this paper, a systems engineering approach is explored to evaluate the effect of design parameters that contribute to the performance of the embedded Automatic Speech Recognition (ASR) engine in a vehicle. This includes vehicle designs that influence the presence of environmental and HVAC noise, microphone placement strategy, seat position, and cabin material and geometry. Interactions can be analyzed between these factors and dominant influencers identified. Relationships can then be established between ASR engine performance and attribute performance metrics that quantify the link between the two. This helps aid proper target setting and hardware selection to meet the customer satisfaction goals for both teams.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.

In the past twenty years, the explicit finite element method has been successfully employed for crash simulation. At present, crashworthiness analysis is still basically a calibration based engineering practice, but not a fully predictive process. The increasing expectations and requirements on CAE are even more challenging. To develop a predictive and reliable CAE tool, it is important to investigate the root causes that affect the numerical accuracy and the availability of the analytical method. Some of the challenging issues are discussed here from both theoretical and engineering aspects, such as convergence of explicit finite element method, locking-free shell element, analysis of material rupture, and modeling of spot weld.

CAE has played a key role in development of the rollover safety technology by reducing the required number of prototypes. CAE-led Design Of Experiments (DOE) studies have helped in developing the process to minimize the number of CAE runs and to optimize use of the prototypes. This paper demonstrates the use of CAE/DOE for the design and optimization of rollover vehicle prototypes and also investigates effects of various factors in the selection of vehicle configuration for rollover sensor calibration testing. The process described herein has been successfully applied to vehicle programs. Modeling and analysis guidelines are also presented for CAE engineers to help in optimizing vehicle prototypes at program level.

There is a strong business case for automotive interfaces to undergo usability testing throughout their product development lifecycle. System Usability Scale (SUS) is a simple and standard measure of usability. To meet the timing needs for product development, usability testing needs to be performed in a quick, cost effective manner. Hence the required sample size of participants for a usability study is one of the critical factors. To determine an acceptable sample size, a Monte Carlo simulation using SUS scores from eleven different in-vehicle automotive interface usability studies was used to create 500,000 subsamples of different sample sizes. The percentage of subsamples with mean scores within the confidence interval of the population mean was calculated. At a subsample size of thirty-five, 95% of the subsamples have a mean SUS score within the 95% confidence interval of the population mean.

Driver assistance features are increasingly dependent upon system architectures that distribute and share responsibilities across various function-based ECUs to minimize cost and redundancy while maximizing engineering efficiency. Clear and accurate system requirements are critical to success, and a robust methodology for validating and testing requirements is essential. Distributed systems are highly sensitive to requirement ambiguity and inaccuracy as they are designed on the assumptions of predictable logical behavior of each functional component. Requirement ambiguity drives variance in implementations which results in system incompatibilities. Errors in requirements lead to faulty implementations that fail not just the component test but also hinder the testing of the entire system of components.

This paper focuses on the methodology development and application of reliability-based design optimization to a vehicle exhaust system under noise, vibration and harshness constraints with uncertainties. Reliability-based design optimization provides a systematic way for considering uncertainties in product development process. As traditional reliability analysis itself is a design optimization problem that requires many function evaluations, it often requires tremendous computational resources and efficient optimization methodologies. Multiple functional response constraints and large number of design variables add further complexity to the problem. This paper investigates an integrated approach by taking advantages of variable screening, design of experiments, response surface model, and reliability-based design optimization for problems with functional responses. A typical vehicle exhaust system is used as an example to demonstrate the methodology.

As automotive competition is becoming more and more global, automotive products (vehicles or systems or components) are often developed in one market and made and/or used in other markets. The operating conditions are often different from one market to another market. If the market unique operating conditions are not considered in its development and manufacture process, the product may not fully perform its intended function over useful life period, and may experience fewer failure modes in one market but more or different failure modes in other markets. This paper presents a Reliability Engineering approach, based on “Failure Mode Avoidance”, to the product development for global automotive markets. The approach includes three phases: Discover operating conditions (noise factors) and their potential failure modes Develop countermeasures to deal with the noise factors, and Verify the effectiveness of the countermeasures using Reliability Demonstration Matrix.