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Numerical durability analysis is the only approach that can be used to assess the durability of vehicles in early stages of development. In these stages, where there are no physical prototypes available, the road wheel forces (or spindle forces) for durability testing on Belgian PG (Proving Ground) must be predicted by VPG (Virtual Proving Ground) or derived from the measured forces of predecessor vehicles. In addition, the tuning parts and geometry are not fixed at these stages. This results in the variation of spindle forces during the development stages. Therefore, it is not reasonable to choose the forces predicted at a specific tuning condition as standard forces. It is more reasonable to determine the standard forces stochastically using the DB of the measured forces of predecessor vehicles. The spindle forces measured or predicted on Belgian PG are typically stationary random.

In this paper, a design procedure for the optimized light weight front cross member, which is a sub frame of the car chassis, without sacrificing basic functional requirements is presented. As the first step, optimal structural integrity was calculated and extracted using a CAE technique with the available volume constraint of the package layout. Quantitative design loads for the cross member was achieved by measurement. Dynamic load analysis using ADAMS was also performed to determine the loads. Later, these calculated loads were applied to the FEM stress analysis of the cross member. Furthermore, durability analysis was also performed using load profile database measured from ‘Hyundai Motor Co. Proving Ground’. Four constant amplitude durability tests and two static tests were performed on the cross member prototypes to confirm design reliability.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

Researchers desire to evaluate system reliability uniquely and efficiently. Despite its strong technical demand, little progress has been made on system reliability analysis in the last two decades. Up to now, bound methods for system reliability prediction have been dominant. For system reliability bounds, the second order bound method gives fairly accurate prediction for system reliability assuming that the probabilities of second-order joint events are accurately obtained. Two primary challenges in system reliability analysis are evaluation of the probabilities of second-order joint events and no unique system reliability for design optimization. Firstly, the greatest technical demand is found in an accurate and efficient method to numerically evaluate the probability of a second-order joint event.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

The verification of the durability for vehicle body and chassis components is a basic requirement for the vehicle development process. For this, automotive company performs durability test on the proving ground or predict the durability using CAE technology. The representative proving ground test that verifies the durability of vehicle body and chassis components are belgian(hereinafter B/G) and cross-country(hereinafter X/C) test road. The B/G test road verifies the durability of body and chassis components for periodic road load that the vehicle undergoes while travelling on a rough road with regular speed. The X/C test road is composed of squat, dive, bumping and bottoming test modes and this test verifies the durability under aperiodic road load. Because of the relatively long test load of X/C, the road load signal of X/C is too long and enormous to apply it to durability analysis.

Elastomers are traditionally designed for use in applications that require specific mechanical properties. Unfortunately, these properties change with respect to many different variables including heat, light, fatigue, oxygen, ozone, and the catalytic effects of trace elements. When elastomeric mounts are designed for NVH use in vehicles, they are designed to isolate specific unwanted frequencies. As the elastomers age however, the desired elastomeric properties may have changed with time. This study looks at the variability seen in new vehicle engine mounts and how the dynamic properties change with respect to miles accumulated on fleet and durability test vehicles.

Ground systems in automobiles become more important as more electric devices are installed and the amount of currents flowing increases. The performance of the devices depends on the ground voltage, which is generated between ground points by I-R voltage drops. Therefore, low ground voltages are required for the reduction of the unnecessary power dissipation as well as the reliable performance of the devices. In this paper, we propose an automotive ground system model to analyze ground structure and reveal the main cause of ground voltages. The equivalent resistor network model is presented to describe the relationship between ground points. Then, we validate the model by comparing the simulation results with the measurements in a real car. The presented analysis can provide guidance on designing a reliable ground system such as how to reduce the ground voltages for the proper operation of devices.

Aluminum steering knuckles are widely employed for weight reduction and improvement of ride & handling performance. In this study, a high strength aluminum alloy for cast-forged knuckle was designed to achieve higher mechanical properties than those of the conventional foundry alloy. Using this alloy, high strength knuckles were manufactured and performed test of mechanical properties, suspension module strength and durability. The strength and the elongation of the developed knuckle were increased by 20% and 40%, respectively, as compared with the conventional alloy. Also this knuckle passed the static strength and durability test of the front suspension module.

Customer vehicle usage monitoring is one of the most fundamental elements to consider in the process of developing a durable vehicle. The extant method to research customer vehicle usage takes considerable time and effort because it requires attaching a series of sensors to the vehicle-gyroscope, accelerometer, microphone, and GPS-to gather information through data logs and then to analyze data in a computer where designated analyzing software has been installed. To solve the problem, this paper introduces a new concept of integrated system developed to examine customer vehicle usage that can analyze data by collecting it from a variety of sensors installed on a smartphone.

This report explains the basic theory of designing the accelerating durability test road and the role of each factors contributing to the test road surface profile. Also this road is designed by considering the charactors of vehicle suspension system and conditions of driving. In test road, the factors affecting to the vehicle structural durability are correlation among surface shape of road profile, frequency of vehicle suspension system,distribution of axle twist angle and vibration of road profile height. Road PSD magnitude and frequency delay is used to control these factors relation.

This paper describes Hyundai's research and development work on a flexible fuel vehicle (FFV). The work on FFV has been conducted to evaluate its potential as an alternative to the conventional gasoline vehicle. Hyundai FFV described here can operate on M85, gasoline, or any of their combinations, in which the methanol concentration is measured by an electrostatic type fuel sensor. For that operation, a special FFV ECU has been developed and incorporated in the FFV. The characteristics affecting FFV operation, such as FFV ECU control strategy and injector flow rate, have been investigated and optimized by experiment. Various development tests have been performed in view of engine performance, durability, cold startability, and exhaust emissions reduction. The exhaust gas aftertreatment system being consisted of manifold type catalytic converter(MCC) and secondary air injection system has shown good emission reduction performance including formaldehyde emission.

The Illinois Institute of Technology (IIT) Interprofessional Projects (IPRO®) Program engages multidisciplinary teams of students in semester-long projects, with a total of thirty to thirty-five different projects offered every semester. This program greatly contributes to IIT's signature undergraduate education experience, with each interprofessional course delivering a team-oriented, project-based requirement within the undergraduate curriculum. Among its many benefits, each interprofessional course offers students the opportunity to integrate the education and research environment of the university to tackle real-world problems. In the process, students get the chance to develop and emerge from the experience with maturity, confidence, and valuable professional skills that are highly sought after in the workplace, simultaneously preparing them for the realities of today's global, highly-competitive environment [1].

As data emerges as the most valuable resource in the world, the evolution of the related data industry is progressing faster. In this study, we tried to discover effective factors for fuel cell durability by using big data analysis techniques with accumulated vehicle actual road data (de-identified Blue Link Data). Basic analysis is performed assuming factors that are expected to have a significant impact on the fuel cell durability performance, and durability factor modeling according to the clustering between driving patterns and durability performance is used to determine. Now can see the change in durability performance. By analyzing the correlation between each driving pattern and durability performance, it is possible to know the weight of the effective factor affecting the durability. If the effective factor with high weight is improved in the actual vehicle unit, the durability performance is expected to increase, and the effect will be verified through real road operation.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

Performance data offers a powerful tool for system condition assessment and health monitoring. In most applications, a host of various types of sensors is employed and data on key parameters (describing the system performance) is compiled for further analysis and evaluation. In ensuring the adequacy of the data acquisition process, two important questions arise: (1) is the complied data robust and reasonable in representing the system parameters; and (2) is the duration of data acquisition adequate to capture a favorable percentage (say for example 90%) of the critical values of a given system parameter? The issue related to the robustness and reasonableness of data can be addressed through known values for key parameters of the system. This is the information that is not often available. And as such, methods based on trends in a given system parameter, expected norms, the parameter's relation with other known parameters, and simulations can be used to assure the quality of the data.

Many high risks of failure in developing and applying new technologies exist in the recent automotive industry because of big volume of selling cars in a global market. Several recalls cost companies more than $ 100 million per problem. New technologies always have uncertainty in performing intended functions at various given conditions despite the fact that engineers do their best to develop technologies to meet all the requirements. Uncertainty of new technologies put companies into danger of failing in their business. Therefore, many companies tend to take interest in reducing risks from the uncertainty in technologies, but the increasing complexity of modern automotive technologies make it difficult to develop complete technologies. A new engineering methodology called SPEED Engineering was introduced to reduce the risks of new technology applications and to facilitate engineers to conceive innovative ideas dominating the market in the future.

Currently, the interest in accelerated reliability testing (ART) of electric vehicles parts has been increasing. In particular, an electric motor and battery are vital components of battery powered electric vehicles. The electric motor has two major roles, to discharge or charge battery when it is driven or braking. For analyzing the exact behavior mechanism of electric motor and predicting lithium-ion battery cell degradation, new accelerated reliability testing technology is required. This paper describes the results of research and development in new approach to reliability testing for electric vehicles. The methodology to measure a precise motor output torque of the rotating rotor using telemetry system was provided. The electric energy quantities as well as the used quantities of the electric power were also analyzed. The results of research and development in new approach to reliability testing for electric vehicles were systematized and reflected in development.