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Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

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.

In this paper, a simplified and systematic approach to integrate reliability and durability aspects in design process is presented. A six step process is explained with the help of examples. Two alternatives for gathering means and standard deviations for key parameters are discussed. First a DOE approach based on orthogonal arrays is presented. Second approach is based on Taylor Series expansion. An example of beam design is solved with both of these approaches. The Second example also considers the degradation with time in service.

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.

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.

This paper is divided into two parts: Part 1 - Systems engineering fundamentals Part 2 - Engine cooling design from a systems engineering perspective In Part 1, we explain how the task of designing a complex system can be made easier by the application of Systems Engineering principles. (This part is self contained and may be of general interest to those who have no special interest in engine cooling). Systems Engineering provides three key benefits: It facilitates communication: Requirements define the problem, they allow team members to see their own work in context Key information is standardized and made easier to visualize and verify. An “audit trail” is maintained ensuring that important information is documented, and human memory is no longer relied on for important decisions. Translates requirements into design.

ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.

A wet clutch continues to play a critical role for step-ratio automatic transmissions and finds new utilities in hybrid and electrified propulsion systems. A torque transfer function is often employed in practice for sophisticated clutch slip controls. It provides a simple, yet practical framework to represent clutch torque as a function of actuator force. An accurate transfer function is also increasingly desired in today's vehicle design process to enable upfront assessment of clutch controls through simulations. The most common approach is based on Coulomb's linear friction model, where the coefficients are adaptively identified based on vehicle data. However, it is generally difficult to tune Coulomb's model for hydrodynamic behaviors even if the reference vehicle data are available. It also remains a challenge to produce in-vehicle clutch behaviors on a component test bench to determine realistic transfer function before prototype vehicles are built.

Finite Element Analysis (FEA) models are routinely being adopted as a means of up-front design for automotive body structure design. FEA models play two important functions: first as a means of assessing design versus an absolute target; secondly they are used to assess the performance of design alternatives required to meet targets. Means of assessing model capability versus task is required to feed appropriate information into the design process. Being able to document model capability improves the credibility of the FEA model information. A prior paper addressed assessing the absolute performance of model technology using a metric based on a statistical hypotheses test that determines membership in a reference set. This paper extends the use of quality technology to determining the capability of the FEA model to span the design space using Designed Experiments.

Vehicle thermal protection is an important aspect of the overall vehicle development process. It involves optimizing the exhaust system routing and designing heat shields to protect various components that are in near proximity to the exhaust system. Reduced time to market necessitates an efficient process for thermal protection development. A robust procedure that utilizes state of the art CFD simulation techniques proactively during the design phase is described. Simulation allows for early detection of thermal issues and development of countermeasures several months before prototype vehicles are built. Physical testing is only used to verify the thermal protection package rather than to develop heat shields. The new procedure reduces the number of physical tests and results in a robust, efficient methodology.

To reduce the cost of prototype and physical test, CAE analysis has been widely used to evaluate the vehicle performance during product development process. Combining CAE analysis and optimization approach, vehicle design process can be implemented more efficiently with affordable cost. Reliability based design optimization (RBDO) formulation considers variations of input variables, such as component gauges and material properties. As a result, the design obtained by using RBDO is more reliable and robust compared to those by deterministic optimization. The RBDO process starts from running simulation at DOE sampling data points, generating surrogate models (response surface) and performing robust and reliability based design optimization on the surrogate models by using Monte Carlo simulation. This paper presents a RBDO framework in Excel enviroment.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

The number of computational fluid dynamics (CFD) simulations performed during the vehicle aerodynamic development process continues to expand at a rapid rate. One key contributor to this trend is the number of analytically based designed experiments performed to support vehicle aerodynamic shape development. A second contributor is the number of aerodynamic optimization studies performed for vehicle exterior components such as mirrors, underbody shields, spoilers, etc. A third contributor is the increasing number of “what if” exploratory studies performed early in the design process when the design is relatively fluid. Licensing costs for commercial CFD solutions can become a significant constraint as the number of simulations expands.

Computer Aided Engineering (CAE) has been widely utilized as an essential component of the product development phase in the automotive industry. Every successful automotive company has established its own design and development approach in order to build competitive, better and safer cars, in a more cost-effective manner. In an ever demanding automotive sector, every key player wants its products to be hitting the roads at shorter intervals but also develop them to be highly competitive. Ford product development processes define the multiple phases of the product development progression and timeline. For each of the phases, Vehicle CAE models are built to assess Vehicle NVH / Durability/ Safety/ Thermal & Aero and other performances. The design level of the input data and the data availability timings, to build the Vehicle CAE models, play a significant role in determining the quality and timing of the product development progression.

Level 2 (L2) partial driving automation systems are rapidly emerging in the marketplace. L2 systems provide sustained automatic longitudinal and lateral vehicle motion control, reducing the need for drivers to continuously brake, accelerate and steer. Drivers, however, remain critically responsible for safely detecting and responding to objects and events. This paper summarizes variations of L2 systems (hands-on and/or hands-free) and considers human drivers’ roles when using L2 systems and for designing Human-Machine Interfaces (HMIs), including Driver Monitoring Systems (DMSs). In addition, approaches for examining potential unintended consequences of L2 usage and evaluating L2 HMIs, including field safety effect examination, are reviewed. The aim of this paper is to guide L2 system HMI development and L2 system evaluations, especially in the field, to support safe L2 deployment, promote L2 system improvements, and ensure well-informed L2 policy decision-making.

A Magic Cube (MQ) approach for crashworthiness design has been proposed in previous research [1]. The purpose of this paper is to extend the MQ approach to the blast protection design of a military vehicle system. By applying the Space Decompositions and Target Cascading processes of the MQ approach, three subsystem design problems are identified to systematize the blast protection design problem of a military vehicle. These three subsystems, including seat structure, restraint system, and under-body armor structure, are most influential to the overall blast-protective design target. The effects of a driver seat subsystem design and restraint-system subsystem design on system blast protection are investigated, along with a focused study on the under-body blast-protective structure design problem.

Cross-section properties and joint stiffness properties of the body structure define its characteristic behavior. During the transitional product development process, body structure joints are optimized on an individual basis to reduce cost and weight. The objective of this paper is to present a methodology to analyze the entire body structure design by optimizing each body joint for stiffness and cost. This methodology utilizes joint sensitivity data from FEA, section properties, and cost/weight data. When the joint stiffness status does not meet the target during the design process, the methodology is an effective tool in making decisions regarding the gage increase/decrease for each part constituting body structure joints. Additionally, the methodology has been applied to body structure joints and door upper frame separately.

Mode management is an essential part of the design process for NVH performance. System resonances must be sufficiently separated to minimize interaction from source inputs and each other [1]. Such resonances are typically determined through experimental modal testing conducted in a lab environment under controlled and repeatable conditions. Global vehicle and suspension system response demonstrate soft nonlinear behavior, however. Their resonant frequencies may thus decrease under on-road input not reproducible in a lab environment. Subsequently, mode management charts derived from lab testing may not be representative of the vehicle's on-road dynamic response. This paper presents modal model determination methodologies, and examines suspension system and vehicle global dynamic response under lab modal test and operating conditions. Vehicle suspension modes measured under static and dynamic (rolling) conditions will be compared.

The front-end design process in the automotive industry today is time consuming and expensive. Although CFD (Computational Fluid Dynamics) modeling is helpful, many vehicle development tests in different wind tunnels are still required to balance the competing requirements of power train cooling, vehicle aerodynamics, climate control, styling, body structure, and product cost. For example, engine cooling and climate control heat exchangers require adequate airflow to achieve their performance. But, this airflow increases cooling drag and can compromise vehicle handling. Internal air deflectors (ducting) are often used to make the frontal opening more efficient and help prevent heat recirculation from the hot engine compartment to the A/C condenser at idle. But this increases product cost and can compromise underhood temperature. A more efficient and faster process is needed to support these trade-off discussions.