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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.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.

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.

Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved.

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.

This paper presents an advanced yaw stability control system that uses a sensor set including an inertial measurement unit to sense the 6 degrees-of-freedom motions of a vehicle. The full degree of the inertial measurement unit improves and enhances the vehicle motion state estimation over the one in the traditional electronic stability controls. The addition of vehicle state estimation leads to the performance refinement of vehicle stability control that can improve performance in certain situations. The paper provides both detailed system description and test results showing the effectiveness of the system.

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.

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.

Hardware-in-the-Loop (HIL) test system-based development has been used throughout Ford Motor Company for some time. The development of control strategies for Hybrid Electric Vehicle (HEV) Electronic Control Units (ECU) has benefited from HIL-based testing. It has proven to be an efficient tool for HEV software strategy development, implementation and validation. The Ford HEV HIL system is a very flexible tool able to simulate the whole HEV control system. Ford's HEV HIL system is based on the capabilities of MathWorks tools, dSPACE ControlDesk1, and ATI Vision2 tools and has demonstrated excellent performance. Comprehensive strategy validation is probably the most time consuming and the most routine task for mature vehicle programs. The goal of this paper is to demonstrate how this task is automated for HEV programs at Ford Motor Company.

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.

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.

A jet pump (also known as ejector) uses momentum of a high velocity jet (primary flow) as a driving mechanism. The jet is created by a nozzle that converts the pressure head of the primary flow to velocity head. The high velocity primary flow exiting the nozzle creates low pressure zone that entrains fluid from a secondary inlet and transfers the total flow to desired location. For a given pressure of primary inlet flow, it is desired to entrain maximum flow from secondary inlet. Jet pumps have been used in automobiles for a variety of applications such as: filling the Fuel Delivery Module (FDM) with liquid fuel from the fuel tank, transferring liquid fuel between two halves of the saddle type fuel tank and entraining fresh coolant in the cooling circuit. Recently, jet pumps have been introduced in evaporative emission control system for turbocharged engines to remove gaseous hydrocarbons stored in carbon canister and supply it to engine intake manifold (canister purging).