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In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

Realistically experiencing the sound and vibration data through actually listening to and feeling the data in a full-vehicle NVH simulator remarkably aids the understanding of the NVH phenomena and speeds up the decision-making process. In the case of idle vibration, the sound and vibration of the idle condition are perceived simultaneously, and both need to be accurately reproduced simultaneously in a simulated environment in order to be properly evaluated and understood. In this work, a case is examined in which a perceived idle quality of a vehicle is addressed. In this case, two very similar vehicles, with the same powertrain but somewhat different body structures, are compared. One has a lower subjective idle quality rating than the other, despite the vehicles being so similar.

Lightweighting of vehicle panels enclosing vehicle cabin causes NVH degradation since engine, road, and wind noise acoustic sources propagate to the vehicle interior through these panels. In order to reduce this NVH degradation, there is a need to develop new NVH sound package materials and designs for use in lightweight vehicle design. Statistical Energy Analysis (SEA) model can be an effective CAE design tool to develop NVH sound packages for use in lightweight vehicle design. Using SEA can help engineers recover the NVH deficiency created due to sheet metal lightweighting actions. Full vehicle SEA model was developed to evaluate the high frequency NVH performance of “Vehicle A” in the frequency range from 200 Hz to 10 kHz. This correlated SEA model was used for the vehicle sound package optimization studies. Full vehicle level NVH laboratory tests for engine and tire patch noise reduction were also conducted to demonstrate the performance of sound package designs on “Vehicle A”.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

Hydrogen fueled internal combustion engines have potential for high thermal efficiencies; however, high efficiency conditions can produce high nitrogen oxide emissions (NOx) that are challenging to treat using conventional 3-way catalysts. This work presents the results of an experimental study to reduce NOx emissions while retaining high thermal efficiencies in a single-cylinder research engine fueled with hydrogen. Specifically, the effects on engine performance of the injection of water into the intake air charge were explored. The hydrogen fuel was injected into the cylinder directly. Several parameters were varied during the study, including the amount of water injected into the intake charge, the amount of fuel injected, the phasing of the fuel injection, the number of fuel injection events, and the ignition timing. The results were compared with expectations for a conventionally operated hydrogen engine where load was controlled through changes in equivalence ratio.

The fatigue strength and failure behavior of A5754-O adhesively bonded single lap joints by a hot-curing epoxy adhesive were investigated in this paper. The single lap joints tested include balanced substrate joints (meaning same thickness) and unbalanced substrate joints, involving combinations of different substrate thicknesses. Cyclic fatigue test results show that the fatigue strength of bonded joints increase with the increasing substrate thickness. SEM and Energy Dispersive X-ray (EDX) were employed to investigate the failure mode of the joints. Two fatigue failure modes, substrate failure and failure within the adhesive were found in the testing. The failure mode of the joint changes from cohesive failure to substrate failure as the axial load is decreased, which reveals a fatigue resistance competition between the adhesive layer and the aluminum substrate.

This paper details the lightweighting efforts of the Ford Research & Advanced Transmission team as part of the Multi Material Lightweight Vehicle Project. The Multi Material Lightweight Vehicle (MMLV) developed by Magna International and Ford Motor Company is a result of a US Department of Energy project DE-EE0005574. The project demonstrates the lightweighting potential of a five passenger sedan, while maintaining vehicle performance and occupant safety. Prototype vehicles were manufactured and limited full vehicle testing was conducted. The Mach-I vehicle design, comprised of commercially available materials and production processes, achieved a 364kg (23.5%) full vehicle mass reduction, enabling the application of a 1.0-liter three cylinder engine resulting in a significant environmental benefits and fuel consumption reduction.

The efforts of the ISO “Test Variability Task Force” have been aimed at improving the understanding and at reducing brake dynamometer test variability during performance testing. In addition, dynamometer test results have been compared and correlated to vehicle testing. Even though there is already a vast amount of anecdotal evidence confirming the fact that different procedures generate different friction coefficients on the same brake corner, the availability of supporting data to the industry has been elusive up to this point. To overcome this issue, this paper focuses on assessing friction levels, friction coefficient sensitivity, and repeatability under ECE, GB, ISO, JASO, and SAE laboratory friction evaluation tests.

When a vehicle with a partially filled fuel tank undergoes sudden acceleration, braking, turning or pitching motion, fuel sloshing is experienced. It is important to establish a CAE methodology to accurately predict slosh phenomenon. Fuel slosh can lead to many failure modes such as noise, erroneous fuel indication, irregular fuel supply at low fuel level and durability issues caused by high impact forces on tank surface and internal parts. This paper summarizes activities carried out by the fuel system team at Ford Motor Company to develop and validate such CAE methodology. In particular two methods are discussed here. The first method is Volume Of Fluid (VOF) based incompressible multiphase Eulerian transient CAE method. The CFD solvers used here are Star CD and Star CCM+. The second method incorporates Fluid-Structure interaction (FSI) using Arbitrary Lagrangian-Eulerian (ALE) formulation.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

Failure mode and fatigue behavior of dissimilar laser welds in lap-shear specimens of aluminum and copper sheets are investigated. Quasi-static tests and fatigue tests of laser-welded lap-shear specimens under different load ranges with the load ratio of 0.1 were conducted. Optical micrographs of the welds after the tests were examined to understand the failure modes of the specimens. For the specimens tested under quasi-static loading conditions, the micrograph indicates that the specimen failed through the fusion zone of the aluminum sheet. For the specimens tested under cyclic loading conditions, two types of failure modes were observed under different load ranges. One failure mode has a kinked crack initiating from the interfacial surface between the aluminum and copper sheets and growing into the aluminum fusion zone at an angle close to 90°.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

Engine dynamometer testing was performed comparing fuels having different octane ratings and ethanol content in a Ford 3.5L direct injection turbocharged (EcoBoost) engine at three compression ratios (CRs). The fuels included midlevel ethanol “splash blend” and “octane-matched blend” fuels, E10-98RON (U.S. premium), and E85-108RON. For the splash blends, denatured ethanol was added to E10-91RON, which resulted in E20-96RON and E30-101 RON. For the octane-matched blends, gasoline blendstocks were formulated to maintain constant RON and MON for E10, E20, and E30. The match blend E20-91RON and E30-91RON showed no knock benefit compared to the baseline E10-91RON fuel. However, the splash blend E20-96RON and E10-98RON enabled 11.9:1 CR with similar knock performance to E10-91RON at 10:1 CR. The splash blend E30-101RON enabled 13:1 CR with better knock performance than E10-91RON at 10:1 CR. As expected, E85-108RON exhibited dramatically better knock performance than E30-101RON.

The effective deployment of FMEAs within complex automotive applications faces a number of challenges, including the complexity of the system being analysed, the need to develop a series of coherently linked FMEAs at different levels within the systems hierarchy and across intrinsically interlinked engineering disciplines, and the need for coherent linkage between critical design characteristics cascaded through the systems levels with their counterparts in manufacturing. The approach presented in this paper to address these challenges is based on a structured Failure Mode Avoidance (FMA) framework which promotes the development of FMEAs within an integrated Systems Engineering approach. The effectiveness of the framework is illustrated through a case study, centred on the development of a diesel exhaust aftertreatment system.

The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time.

Stop/Start technology for conventional automatic transmissions has recently received considerable attention in the automotive industry due to the potential fuel economy, and CO2 emission reduction, benefit at minimal cost. Stop/Start was first developed for manual transmission applications in the EU and Japanese markets. When stop/start is applied to any automatic transmission powertrain the powertrain control challenge is to restart the engine in a manner that simultaneously minimizes the delay in transferring torque to the driven wheel(s) and provides a consistently smooth launch feel with low NVH. It has recently been shown that stop/start can be added to a gas engine powertrain with a conventional torque converter automatic transmission while achieving the desired launch characteristics with minimal change to the powertrain hardware and cost.

Aggressive battery usage profiles in electrified vehicle applications require extensive efforts in developing battery management strategy and system design determination to guarantee safe operation under every real-world driving conditions. Experiment based approaches have been widely used for battery system development, but higher costs and longer testing time restrain the number of test cases in the product development process. Battery experiments tend to be conservative to avoid inherent risks of battery failure modes under aggressive battery operation close to the capability limits. Battery Hardware-In-the-Loop (HIL) is an alternative way to overcome the limitations of experiment-based approaches. Battery models in the HIL should provide real-time computation capability and high (at least acceptable) prediction accuracy. Equivalent circuit model (ECM) based HILs have been used owing to their relatively good balance between computational time and prediction accuracy.

A new performance simulation capability has been developed for powersplit HEVs to enable analytical assessment of new engine technologies in the context of HEV system operation and to analyze/understand important system dynamics and control interactions affecting HEV performance. This new capability allows direct simulation with closed-loop controls and the driver, is compatible with Ford standard HEV system simulation capabilities and enables simulation with multiple levels of model fidelity and feature content across the vehicle system. The combined plant Vehicle Model Architecture (VMA) in Simulink was used for the infrastructure. The simulation capability includes a Dymola model of the powersplit transaxle, a Vehicle System Control (VSC) model implemented in Simulink, a high fidelity 2L Atkinson GT-Power engine model, and a simplified representation of the engine controls in Simulink.

The present investigation details an experimental procedure for frontal impact responses of a generic steel front bumper crush can (FBCC) assembly subjected to a rigid full and 40% offset impact. There is a paucity of studies focusing on component level tests with FBCCs, and of those, speeds carried out are of slower velocities. Predominant studies in literature pertain to full vehicle testing. Component level studies have importance as vehicles aim to decrease weight. As materials, such as carbon fiber or aluminum, are applied to vehicle structures, computer aided models are required to evaluate performance. A novel component level test procedure is valuable to aid in CAE correlation. All the tests were conducted using a sled-on-sled testing method. Several high-speed cameras, an IR (Infrared) thermal camera, and a number of accelerometers were utilized to study impact performance of the FBCC samples.