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The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

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).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

OEMs are racing to develop lightweight vehicles as government regulations now mandate automakers to nearly double the average fuel economy of new cars and trucks by 2025. Lightweight materials such as aluminum, magnesium and carbon fiber composites are being used as structural members in vehicle body and suspension components. The reduction in weight in structural panels increases noise transmission into the passenger compartment. This poses a great challenge in vehicle sound package development since simply increasing weight in sound package components to reduce interior noise is no longer an option [1]. This paper discusses weight saving approaches to reduce noise level at the sources, noise transmission paths, and transmitted noise into the passenger compartment. Lightweight sound package materials are introduced to treat and reduce airborne noise transmission into multi-material lightweight body structure.

Hands-free phone use is the most utilized use case for vehicles equipped with infotainment systems with external microphones that support connection to phones and implement speech recognition. Critically then, achieving hands-free phone call quality in a vehicle is problematic due to the extremely noisy nature of the vehicle environment. Noise generated by wind, mechanical and structural, tire to road, passengers, engine/exhaust, HVAC air pressure and flow are all significant contributors and sources of noise. Other factors influencing the quality of the phone call include microphone placement, cabin acoustics, seat position of the talker, noise reduction of the hands-free system, etc. This paper describes the work done to develop procedures and metrics to quantify the effects that influence the hands-free phone call quality.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Magnesium die-cast alloys are known to have a layered microstructure composed of: (1) An outer skin layer characterized by a refined microstructure that is relatively defect-free; and (2) A “core” (interior) layer with a coarser microstructure having a higher concentration of features such as porosity and externally solidified grains (ESGs). Because of the difference in microstructural features, it has been long suggested that removal of the surface layer by machining could result in reduced mechanical properties in tested tensile samples. To examine the influence of the skin layer on the mechanical properties, a series of round tensile bars of varying diameters were die-cast in a specially-designed mold using the AM60 Mg alloy. A select number of the samples were machined to different final diameters. Subsequently, all of the samples (as-cast as well as machined) were tested in tension.

Automotive structural parts made out of Advanced High Strength Steel (AHSS) are often produced in a multistage forming process using progressive dies or transfer dies. During each forming stage the steel is subjected to work hardening, which affects the formability of the steel in the subsequent forming operation. Edge flanging and in-plane edge stretching operations are forming modes that are typically employed in the last stage of the multistage forming processes. In this study, the multistage forming process was simulated by pre-straining a DP980 steel in a biaxial strain path with various strain levels followed by edge flanging and in-plane edge stretching. The biaxial prestrains were obtained using the Marciniak stretch test and edge flanging and in-plane edge stretching were accomplished by the hole expansion test using a flat punch and a conical punch, respectively.

Due to magnesium alloy's poor weldability, other joining techniques such as laser assisted self-piercing rivet (LSPR) are used for joining magnesium alloys. This research investigates the fatigue performance of LSPR for magnesium alloys including AZ31 and AM60. Tensile-shear and coach peel specimens for AZ31 and AM60 were fabricated and tested for understanding joint fatigue performance. A structural stress - life (S-N) method was used to develop the fatigue parameters from load-life test results. In order to validate this approach, test results from multijoint specimens were compared with the predicted fatigue results of these specimens using the structural stress method. The fatigue results predicted using the structural stress method correlate well with the test results.

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.

This paper proposes an approach to determine driver's driving behavior, style or habit during vehicle handling maneuvers and heavy traction and braking events in real-time. It utilizes intelligence inferred from driver's control inputs, vehicle dynamics states, measured signals, and variables processed inside existing control modules such as those of anti-lock braking, traction control, and electronic stability control systems. The algorithm developed for the proposed approach has been experimentally validated and shows the effectiveness in characterizing driver's handling behavior. Such driver behavior can be used for personalizing vehicle electronic controls, driver assistant and active safety systems, and the other vehicle control features.

Hot-formed steels, also called “Boron steels” or Ultra-High Strength Steels-UHSS, offer a great weight saving potential versus conventional cold-formed high strength steels used for crash relevant structural parts. Boron steels allow complex shaped parts due to the hot-forming process. In the hot forming process first the sheet metal with initial yield strength of around σy=400 MPa is blanked and then heated in an oven up to ∼950° Celsius. In the next step the “hot” sheet metal is stamped and at the same time rapidly cooled down and quench hardened in the stamping die. During this process the yield and ultimate tensile strength increase up to approximately σy>1100 MPa and UTS∼1500 MPa in the final stamped part. The enormous strength and the very good dimensional tolerances with nearly no springback result in the use of more and more hot-formed parts in the body, especially for crash relevant parts like structural reinforcements.

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 benefit and fuel reduction. This paper reviews the mass reduction and structural performance of aluminum, magnesium, and steel components for a lightweight multi material door design for a C/D segment passenger vehicle. Stiffness, durability, and crash requirements are assessed.

The Multi Material Lightweight Vehicle (MMLV), developed by Magna International and Ford Motor Company, is a result of US Department of Energy project DE-EE0005574. The project demonstrated 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 364 kg (23.5%) full-vehicle mass reduction. This resulted in environmental benefits and fuel economy improvements. A significant factor in the overall MMLV mass reduction was the decrease in the powertrain system weight from 340 kg (conventional) to 267 kg (MMLV). This enabled the application of a 1.0-liter three-cylinder engine as the main powerplant. By downsizing the engine, and by implementing material changes within the engine, the weight of the dressed engine was lowered by 29 kg.

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.

Cold idle operation of a modern design light duty diesel engine and the effect of multiple pilot injections on stability were investigated. The investigation was initially carried out experimentally at 1000rpm and at −20°C. Benefits of mixture preparation were initially explored by a heat release analysis. Kiva 3v was then used to model the effect of multiple pilots on in-cylinder mixture distribution. A 60° sector of mesh was used taking advantage of rotational symmetry. The combustion system and injector arrangements mimic the HPCR diesel engine used in the experimental investigation. The CFD analysis covers evolutions from intake valve closing to start of combustion. The number of injections was varied from 1 to 4, but the total fuel injected was kept constant at 17mm3/stroke. Start of main injection timing was fixed at 7.5°BTDC.

In present paper, the process of joining aluminum alloy 6111T4 and steel HSLA340 sheets by self-piercing riveting (SPR) is studied. The rivet material properties were obtained by inverse modeling approach. Element erosion technique was adopted in the LS-DYNA/explicit analysis for the separation of upper sheet before the rivet penetrates into lower sheet. Maximum shear strain criterion was implemented for material failure after comparing several classic fracture criteria. LS-DYNA/implicit was used for springback analysis following the explicit riveting simulation. Large compressive residual stress was observed near frequent fatigue crack initiation sites, both around vicinity of middle inner wall of rivet shank and upper 6111T4 sheet.

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.