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In the development of an FAW SUV, one of the goals is to achieve a state of the art drag level. In order to achieve such an aggressive target, feedback from aerodynamics has to be included in the early stage of the design decision process. The aerodynamic performance evaluation and improvement is mostly based on CFD simulation in combination with some wind tunnel testing for verification of the simulation results. As a first step in this process, a fully detailed simulation model is built. The styling surface is combined with engine room and underbody detailed geometry from a similar size existing vehicle. From a detailed analysis of the flow field potential areas for improvement are identified and five design parameters for modifying overall shape features of the upper body are derived. In a second step, a response surface method involving design of experiments and adaptive sampling techniques are applied for characterizing the effects of the design changes.

Weight reduction is very important in automotive design because of stringent demand on fuel economy. Structural optimization of dynamic systems using finite element (FE) analysis plays an important role in reducing weight while simultaneously delivering a product that meets all functional requirements for durability, crash and NVH. With advancing computer technology, the demand for solving large FE models has grown. Optimization is however costly due to repeated full-order analyses. Reanalysis methods can be used in structural vibrations to reduce the analysis cost from repeated eigenvalue analyses for both deterministic and probabilistic problems. Several reanalysis techniques have been introduced over the years including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA) and the Epsilon algorithm, among others.

To improve fuel economy, there is a trend in automotive industry to use light weight, high strength materials. Automotive body structures are composed of several panels which must be downsized to reduce weight. Because this affects NVH (Noise, Vibration and Harshness) performance, engineers are challenged to recover the lost panel stiffness from down-gaging in order to improve the structure borne noise transmitted through the lightweight panels in the frequency range of 100-300 Hz where most of the booming and low medium frequency noise occurs. The loss in performance can be recovered by optimized panel geometry using beading or damping treatment. Topography optimization is a special class of shape optimization for changing sheet metal shapes by introducing beads. A large number of design variables can be handled and the process is easy to setup in commercial codes. However, optimization methods are computationally intensive because of repeated full-order analyses.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

A Reliability-Based Design Optimization (RBDO) method for multiple failure regions is presented. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with an approximate global metamodel with local refinements. The latter serves as an indicator to determine the failure and safe regions. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. An “accurate-on-demand” metamodel is used in the PRRA that allows us to handle problems with multiple disjoint failure regions and potentially multiple most-probable points (MPP). The multiple failure regions are identified by using a clustering technique. A maximin “space-filling” sampling technique is used to construct the metamodel. A vibration absorber example highlights the potential of the proposed method.

Early in the engineering design cycle, it is difficult to quantify product reliability due to insufficient data or information to model uncertainties. Probability theory can not be therefore, used. Design decisions are usually based on fuzzy information which is imprecise and incomplete. Various design methods such as Possibility-Based Design Optimization (PBDO) and Evidence-Based Design Optimization (EBDO) have been developed to systematically treat design with non-probabilistic uncertainties. In practical engineering applications, information regarding the uncertain variables and parameters may exist in the form of sample points, and uncertainties with sufficient and insufficient information may exist simultaneously. Most of the existing optimal design methods under uncertainty can not handle this form of incomplete information. They have to either discard some valuable information or postulate the existence of additional information.

An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA calculates the sensitivity derivatives of the reliability to the parameters of the probability distributions. The approach is useful for analysis problems where reliability bounds need to be calculated because the probability distribution of the input variables is uncertain or for design problems where the design variables are random. The accuracy and efficiency of PRA is demonstrated on vibration analysis of a car and on system reliability-based optimization (RBDO) of an internal combustion engine.

Scuffing is a failure mechanism which can occur in various engineering components, such as engine cylinder kits, gears and cam/followers. In this research, the scuffing behavior of 4140 steel and ductile iron was investigated and compared through ball-on-disk scuffing tests. A step load of 22.2 N every two minutes was applied with a light mineral oil as lubricant to determine the scuffing load. Both materials were heat treated to various hardness and tests were conducted to compare the scuffing behavior of the materials when the tempered hardness of each material was the same. Ductile iron was found to have a consistently high scuffing resistance before tempering and at tempering temperatures lower than 427°C (HRC ≻45). Above 427°C the scuffing resistance decreases. 4140 steel was found to have low scuffing resistance at low tempering temperatures, but as the tempering temperature increases, the scuffing resistance increased.

The classical definition of reliability may not be readily applicable for repairable systems. Commonly used concepts such as the Mean Time Between Failures (MTBF) and availability can be misleading because they only report limited information about the system functionality. In this paper, we discuss a set of metrics that can help with the design of repairable systems. Based on a set of desirable properties for these metrics, we select a minimal set of metrics (MSOM) which provides the most information about a system, with the smallest number of metrics. The metric of Minimum Failure Free Period (MFFP) with a given probability generalizes MTBF because the latter is simply the MFFP with a 0.5 probability. It also generalizes availability because coupled with repair times it provides a clearer picture of the length of the expected uninterrupted service. Two forms of MFFP are used: transient and steady state.

In this paper, the development of random vibration testing schedules for durability design verification of engine mounted products is presented, based on the equivalent fatigue damage concept and the 95th-percentile customer engine usage data for 150,000 miles. Development of the 95th-percentile customer usage profile is first discussed. Following that, the field engine excitation and engine duty cycle definition is introduced. By using a simplified transfer function of a single degree-of-freedom (SDOF) system subjected to a base excitation, the response acceleration and stress PSDs are related to the input excitation in PSD, which is the equivalent fatigue damage concept. Also, the narrow-band fatigue damage spectrum (FDS) is calculated in terms of the input excitation PSD based on the Miner linear damage rule, the Rayleigh statistical distribution for stress amplitude, a material's S-N curve, and the Miles approximate solution.

Technological advancement in the automotive industry necessities a closer focus on the functional safety for higher automated driving levels. The automotive industry is transforming from conventional driving technology, where the driver or the human is a part of the control loop, to fully autonomous development and self-driving mode. The Society of Automotive Engineers (SAE) defines the level 4 of autonomy: “Automated driving feature will not require the driver to take over driving control.” Thus, more and more safety related electronic control units (ECUs) are deployed in the control module to support the vehicle. As a result, more complexity of system architecture, software, and hardware are interacting and interfacing in the control system, which increases the risk of both systematic and random hardware failures.

This research assesses the integration of different levels of electric vehicles (EVs) in the distribution system and observes its impacts on environmental emissions and power system operational costs. EVs can contribute to reducing the environmental emission from two different aspects. First, by replacing the traditional combustion engine cars with EVs for providing clean and environment friendly transportation and second, by integrating EVs in the distribution system through the V2G program, by providing power to the utility during peak hours and reducing the emission created by hydrocarbon dependent generators. The PG&E 69-bus distribution system (DS) is used to simulate the integration of EVs and to perform energy management to assess the operational costs and emissions. The uncertainty of driving patterns of EVs are considered in this research to get more accurate results.

An existing challenge in current state-of-the-art autonomous vehicles is the process of safely transferring control from autonomous driving mode to manual mode because the driver may be distracted with secondary tasks. Such distractions may impair a driver’s situational awareness of the driving environment which will lead to fatal outcomes during a handover. Current state-of-the-art vehicles notify a user of an imminent handover via auditory, visual, and physical alerts but are unable to improve a driver’s situational awareness before a handover is executed. The overall goal of our research team is to address the challenge of providing a driver with relevant information to regain situational awareness of the driving task. In this paper, we introduce a novel approach to estimating a driver’s visual focus of attention using a 2D RGB camera as input to a Multi-Input Convolutional Neural Network with shared weights. The system was validated in a realistic driving scenario.

The advantages of having higher stiffness to weight ratio and strength to weigh ratio that composite materials have resulted in an increased interest in them. In automotive engineering, the weight savings has positive impacts on other attributes like fuel economy and possible noise, vibration and harshness (NVH). The driveline of an automotive system can be a target for possible use of composite materials. The design of the driveshaft of an automotive system is primarily driven by its natural frequency. This paper presents an exact solution for the vibration of a composite driveshaft with intermediate joints. The joint is modeled as a frictionless internal hinge. The Euler-Bernoulli beam theory is used. Lumped masses are placed on each side of the joint to represent the joint mass. Equations of motion are developed using the appropriate boundary conditions and then solved exactly.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

The Intelligent Ground Vehicle Competition (IGVC) is a multidisciplinary exercise in product realization for college engineering students. They design, build, and compete with autonomous vehicles in events ranging from lane following, obstacle avoidance, platooning, to Global Positioning System (GPS) navigation. Technologies involved include electronic controls, computer-based vision systems, object detection, rangefinding, and global positioning. The real world applications are in intelligent transportation systems, the military, and manufacturing automation. Students have been creative and have learned a great deal. Industry recruiters have been highly supportive.

A comprehensive formulation is presented for the dynamics of a rotating flexible crankshaft coupled with the dynamics of an engine block through a finite difference elastohydrodynamic main bearing lubrication algorithm. The coupling is based on detailed equilibrium conditions at the bearings. The component mode synthesis is employed for modeling the crankshaft and block dynamic behavior. A specialized algorithm for coupling the rigid and flexible body dynamics of the crankshaft within the framework of the component mode synthesis has been developed. A finite difference lubrication algorithm is used for computing the oil film elastohydrodynamic characteristics. A computationally accurate and efficient mapping algorithm has been developed for transferring information between a high - density computational grid for the elastohydrodynamic bearing solver and a low - density structural grid utilized in computing the crankshaft and block structural dynamic response.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

The second part of a detailed examination of multivariate correlation of several axle assembly and component parameters to the assembly NVH performance (vibration) measured at the end of the assembly process is presented focusing on the multivariate linear regression analysis. The study is based on test results and measurements acquired from multiple axle assemblies built with the same hypoid gearset, thus effectively eliminating the affect of gearset variation on the test result. Several major components within the axle are considered including the differential housing (that controls wheel differentiation during turns), the axle housing, and several assembly parameters. Details of the multivariate regression include formulation of the linear regression model, model refinements through analysis of subsets of the variables, tests of significance and residual analysis.