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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.

A new turbocharged 60° 2.7L 4V-V6 gasoline engine has been developed by Ford Motor Company for both pickup trucks and car applications. This engine was code named “Nano” due to its compact size; it features a 4-valves DOHC valvetrain, a CGI cylinder block, an Aluminum ladder, an integrated exhaust manifold and twin turbochargers. The goal of this engine is to deliver 120HP/L, ULEV70 emission, fuel efficiency improvements and leadership level NVH. This paper describes the upfront design and optimization process used for the NVH development of this engine. It showcases the use of analytical tools used to define the critical design features and discusses the NVH performance relative to competitive benchmarks.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

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.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

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.

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.

A deeper understanding of the complex phenomenology associated with the multiphase flow-induced noise and vibration in a dynamic valve is of critical importance to the automotive industry. To this purpose, a two-dimensional axisymmetric numerical model has been developed to simulate the complex processes that are responsible for the noise and vibration in a poppet valve. More specifically, an Eulerian multiphase flow model, a dynamic mesh and a user-defined function are utilized to facilitate the modeling of this complicated two-phase fluid-structure interaction problem. For a two-phase flow through the valve, our simulations showed that the deformation and breakup of gas bubbles in the gap between the poppet and the valve seat generates a vibration that arises primarily from the force imbalance between the spring and the two-phase fluid flow induced forces on the poppet.

Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. Past studies of lane change detection have relied on vehicular data, such as steering angle, velocity, and acceleration. In this paper, we use three physiological signals from the driver to detect lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel system is proposed which uses a Granger causality test for feature selection and a neural network for classification. Test results showed that for 30 lane change events and 60 non lane change events in on-the-road driving, a true positive rate of 70% and a false positive rate of 10% was obtained.

This paper presents the extended Kalman filter-based sideslip angle estimator design using a nonlinear 5DoF single-track vehicle dynamics model with stochastic modeling of tire forces. Lumped front and rear tire forces have been modeled as first-order random walk state variables. The proposed estimator is primarily designed for vehicle sideslip angle estimation; however it can also be used for estimation of tire forces and cornering stiffness. This estimator design does not rely on linearization of the tire force characteristics, it is robust against the variations of the tire parameters, and does not require the information on coefficient of friction. The estimator performance has been first analyzed by means of computer simulations using the 10DoF two-track vehicle dynamics model and underlying magic formula tire model, and then experimentally validated by using data sets recorded on a test vehicle.

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.

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.

Distance to empty (DTE) estimation is an important factor to electric vehicle (EV) applications due to its limited driving range. The DTE calculation is based on available energy of the battery and power usage by the powertrain components (e.g. electric motor) and climate control components (e.g. PTC heater and electric AC compressor). The conventional way of estimating the DTE is to treat the power consumed by the climate control system the same as the power by the powertrain for either instantaneous or rolling average estimation. The analysis in this study shows that the power consumption by the climate control system should be estimated based on the current ambient conditions and driver's input instead of using the recorded data from the past driving cycles. The climate control should also be considered separately from the powertrain in power usage rolling average calculation, which results in improvements in DTE estimation especially for extreme hot and cold conditions.

There have been many articles published in the last decade or so concerning the components of an electronic stability control (ESC) system, as well as numerous statistical studies that attempt to predict the effectiveness of such systems relative to crash involvement. The literature however is free from papers that discuss how engineers might develop such systems in order to achieve desired steering, handling, and stability performance. This task is complicated by the fact that stability control systems are very complex and their designs and what they can do have changed considerably over the years. These systems also differ from manufacturer to manufacturer and from vehicle to vehicle in a given maker of automobiles. In terms of ESC hardware, differences can include all the components as well as the addition or absence of roll rate sensors or active steering gears to name a few.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

Downhill tests are widely used as a method of evaluation, development and validation of braking efficiency, friction pair durability, braking balance, as well as fade characteristics and recovery of friction material properties. This test procedure is used for both: passenger vehicles and light & heavy commercial vehicles. The energy levels in the brake system are higher on commercial vehicles and the thermal characteristics much more critical. Added to the fact that such tests are conducted on public highways, it has an intrinsic security risk for both the vehicle tested and all others around. Until a few years ago, it was still feasible to conduct tests downhill on different routes keeping a high security level. Given an increasing traffic on highways, where the test is currently carried out, a need to create a similar downhill procedure (called Guaporé Mountain Test) within a Proving Ground under controlled conditions has been noticed.

During the development of a new friction material, besides the interface between lining/drum is also fundamental take in account all aspects involving the attachment of the linings on the brake shoes. This paper presents an optimization approach to the development and manufacturing parameters of brake linings, applied on medium and heavy duty commercial vehicles, aiming to assure the correct specification of the riveted joint clamp forces. These evaluations were conducted based on the quality tools documents and the theoretical aspects of the product usage as well as the modeling of key elements of the referred mechanism throughout various known applications. A calculation methodology was developed based on brake geometry, its generated forces and braking reactions required for each vehicle family.