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The steering system is to provide the driver with the possibility of lateral vehicle guidance, i.e. to influence the lateral dynamics of the vehicle; moreover, it is crucial to promptly translate the steering input to have the vehicle in high-quality directional stability. An electrical power assisted steering (EPAS) system is the sophisticated variant to meet higher requirements for vehicle safety, ride comfort, and driver-assist. This research is to investigate if a CAE methodology could be innovated to better simulate the durability of a steering system under various working scenarios; figure out the critical features of the modeling; conduct a correct analysis procedure for validating the modeling and collecting data for evaluation. With step by step in modeling and analysis, a well-established example of CAE model of EPAS is enabled to highlight the novelty of steering vehicle level CAE methodology and therefore achieve the research goal.

The work presented here is part of the research done in the field of voice biometrics. This paper helps to understand the state-of-the-art in speaker recognition technology potentially capable of solving challenges related to speaker identification (to identify a speaker among multiple speakers) and speaker verification/authentication (to recognize the current speaking person at a pre-defined access level and authenticate accordingly). The research was focused on performing an unbiased evaluation of two individual voice biometric services. The level of accuracy in identifying and authenticating individuals using these services provides an insight into the current state of technology and the state of what other dual authentication methods could be used to achieve a desired True Acceptance Rate (TAR) and False Acceptance Rates (FAR).

Model-guided development of drivetrain control and calibration is a key enabler of robust and efficient vehicle design process. A number of CAE tools are available today for modeling hydro-mechanical systems. Automatic transmission behaviors are well understood to effectively tune the model parameters for targeted applications. Drivetrain models provide physical insight for understanding the effects of component interactions on system behaviors. They are also widely used in HIL/SIL environments to debug control strategies. Nonetheless, it is still a challenge to predict shift quality, especially during a sequence of multiple events, with enough accuracy to support model-guided control design and calibration. The inclusion of hydraulic circuits in simulation models often results in challenges for numerical simulation.

The fatigue analysis of a full car body requires the sheet metal (sheet fatigue), spot welds (spot weld fatigue) and seam welds (seam weld fatigue) to be thoroughly evaluated for durability. Traditionally this has always been done in the time domain, but recently new frequency domain techniques are able to perform these tasks with numerous advantages. This paper will summarize the frequency domain process and then compare the results and performance against the more usual time domain process.

This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process.

This paper describes a joint SAE automotive and aerospace Recommended Practice SAE J3168 now in development to standardize a process for Reliability Physics Analysis. This is a science-based approach to implement Physics-of-Failure research in conducting durability simulations in a Computer Aided Engineering Environment. It is used to calculate failure mechanism susceptibilities and estimate the likelihood of failure and the expected durability life of Electrical, Electronic and Electromechanical components and equipment, due to stresses such as mechanical shock, vibration, temperature cycling, etc. Reliability Physics Analysis is based on the material science principle of stress driven damage accumulation in materials. The process enables the identification of potential failure risks early in the design phase so that such risks can be designed out in order to efficiently design high reliable and robustness into electronic products.

There is an ongoing effort in the industry to develop an accelerated corrosion test for automotive heat exchangers. This has become even more important as automakers are focusing on corrosion durability of 15 years in the field versus current target of 10 years. To this end an acid immersion test was developed and reported in a previous paper for condensers (1). This paper extends those results to evaporators and establishes the efficacy of the test using these results and those reported in the literature. The paper also discusses variability in corrosion test results as observed in tests such as ASTM G85:A3 Acidified Synthetic Sea Water Test (SWAAT), and its relation to field durability.

All drivers come with a driving signature during a driving. By aggregating adequate driving data of a driver via multiple driving sessions, which is already embedded with driving behaviors of a driver, driver identification task could be treated as a supervised machine learning classification problem. In this paper, we use a random forest classifier to implement the classification task. Therefore, we collected many time series signals from 60 driving sessions (4 sessions per driver and 15 drivers totally) via the Controller Area Network. To reduce the redundancy of information, we proposed a method for signal pre-selection. Besides, we proposed a strategy for parameters tuning, which includes signal refinement, interval feature extraction and selection, and the segmentation of a signal. We also explored the performance of different types of arrangement of features and samples.

In early design activities (typically before the hardware is built), a reliability prediction is often required for the electronic components and systems in order to assess their future reliability and in many cases to meet customer specifications. These specifications may include the allocated reliability for a particular electronic unit and in the cases of functional safety products to meet the ASIL (Automotive Safety and Integrity Level) requirement specified by the functional safety standard ISO 26262. The standard allows for the use of “statistics based on field returns or tests” as a valid alternative to the handbook-based reliability prediction. This paper presents a newly developed SAE-J3083 standard “Reliability Prediction for Automotive Electronics Based on Field Return Data”, which covers the types of the required data, ways to collect it, and the methodology of how to process this data to calculate the failure rates and meet the expected safety goals.

The development costs that new design requires are subject to everyday discussions and saving opportunities are mandatory. Using CAE to predict design changes can avoid excessive costs with prototypes parts, considering the high reliability those current mathematical models can provide. This paper presents the methodology used during the development of a parabolic leaf spring for the rear suspension of a commercial truck, considering mainly the parabolic profiles and stress distribution on the leaves, calculated using CAE software (ANSYS) and experimental tests to measure the actual stress on each leaf, certifying the correlation between computational calculations and real stress on the parts during bench and vehicle evaluations.

In order to optimize the development of bolted joints used to components attachments in the Sidemember of commercial vehicles, the joints development has become relevant to better definition of the fasteners size, eliminating overweight and avoiding under or super-sized. This paper presents a development sequential approach of bolted joints applied on commercial vehicles ensuring the correct specifications usage of the fasteners and the joint to keep their clamp force. The evaluations were conducted based on theoretical and practical aspects applied on products and in the definition of all elements contained in a joint. The calculation methodology was developed based on standardized bolts and forces generated through the reactions of the components required for each vehicle family.

During a B-Car durability validation route, it was observed a squeak noise coming from front suspension structure. In the teardown, it was verified metal to metal contact between coil spring and damper spring plate and squeeze-out of spring pad. To reproduce the vehicle failure, it was developed in laboratory a fixture and test to reflect a B-Car McPherson suspension motion, to reproduce the failure and validate a proposal. After root cause understanding, the challenge was to design a new spring pad to avoid squeeze-out keeping the coil spring lower pigtail unchanged. It was tested some prototype parts also in vehicle to approve the design proposal.

In most aspects of mechanical design related to a motor vehicle there are two ways to treat dynamic fatigue problems. These are the time domain and the frequency domain approaches. Time domain approaches are the most common and most widely used especially in the automotive industries and accordingly it is the method of choice for the fatigue calculation of welded structures. In previous papers the frequency approach has been successful applied showing a good correlation with the life and damage estimated using a time based approach; in this paper the same comparative process has been applied but now extended specifically to welded structures. Both the frequency domain approach and time domain approach are used for numerically predicting the fatigue life of the seam welds of a thin sheet powertrain installation bracketry of a commercial truck submitted to variable amplitude loading. Predicted results are then compared with bench tests results, and their accuracy are rated.

Designing a durability test for an automatic transmission that appropriately reflects customer usage during the lifetime of the vehicle is a formidable task; while the transmission and its components must survive severe usage, overdesigning components leads to unnecessary weight, increased fuel consumption and increased emissions. Damage to transmission components is a function of many parameters including customer driving habits and vehicle and transmission characteristics such as weight, powertrain calibration, and gear ratios. Additionally, in some cases durability tests are required to verify only a subset of the total parameter space, for example, verifying only component modifications. Lastly, the ideal durability test is designed to impose the worst case loading conditions for the maximum number of internal components, be as short as practicable to reduce testing time, with minimal variability between tests in order to optimize test equipment and personnel resources.

Material authenticity is an important factor for appearance and perceived quality of the vehicle interior. The term authenticity implies ambivalence: For the product designer, it means identification and trueness of the origin of the material. The customers, however, can only access information on the nature of the materials via their own perception of surface features. Thus, the intended authenticity of a material always needs to be conveyed by its surface. Specific cases illustrate the context: 1. The customer touches a part of known matter, but various layers prevent from directly touching the natural material: e.g. leather at the steering wheel, applications of wood. 2. Perception of a thin surface layer indicates authentic material, which is not fulfilled by the whole part: e.g. plastic parts plated with metal. 3. A part consists of authentic material, but newly composed, so that it is not easily identified, such as recycled materials, e.g. leather fiber layers for seats.

Onboard, embedded cellular modems are enabling a range of new connectivity features in vehicles and rich, real-time data set transmissions from a vehicle’s internal network up to a cloud database are of particular interest. However, there is far too much information in a vehicle’s electrical state for every vehicle to upload all of its data in real-time. We are thus concerned with which data is uploaded and how that data is processed, structured, stored, and reported. Existing onboard data processing algorithms (e.g. for DTC detection) are hardcoded into critical vehicle firmware, limited in scope and cannot be reconfigured on the fly. Since many use cases for vehicle data analytics are still unknown, we require a system which is capable of efficiently processing and reporting vehicle deep data in real-time, such that data reporting can be switched on/off during normal vehicle operation, and that processing/reporting can be reconfigured remotely.

A transmission system model is developed at various complexities in order to capture the transient behaviors in drivability and fuel economy simulations. A large number of model parameters bring more degree of freedom to correlate with vehicular test data. However, in practice, it requires extensive time and effort to tune the parameters to satisfy the model performance requirements. Among the transmission model, a hydraulic clutch actuator plays a critical role in transient shift simulations. It is particularly difficult to tune the actuator model when it is over-parameterized. Therefore, it is of great importance to develop a hydraulic actuator model that is easy to adjust while retaining sufficient complexity for replicating realistic transient behaviors. This paper describes a systematic approach for reducing the hydraulic actuator model into a piecewise 1st order representation based on piston movement.

There is a strong business case for automotive companies to improve by understanding what consumers want, like and dislike. Various aspects of ergonomics such as reach, visibility, usability, feel are dependent on measuring consumer’s ability, opinions and satisfaction. Rating scales (such as adjective, continuous, logarithmic, etc.) are used to measure these complex attitudes. It is essential the correct rating scale and appropriate analysis methods are used to capture these attitudes. Previous psychology research has been conducted on the performance of different rating scales. This ratings scale research focused on scales and their reliability and validity for various applications. This paper will summarize past research, discuss the use of rating scales specific to vehicle ergonomics, and analyze the results of an automotive interface study that correlates the seven-point adjective rating scale to the system usability score (SUS).

Measurement of usability with the System Usability Scale (SUS) is successfully applied to products in many industries. The benefit of any measurement scale, however, is limited by the repeatability of the associated testing process. For SUS, these factors can include sample size, study protocol, previous experience, and pre study exposure to the system being tested. Differences in user exposure can influence the usability assessment of interfaces which could affect the validity of SUS scores.

Increasing number of vehicles are equipped with telematics devices and are able to transmit vehicle CAN bus information remotely. This paper examines the possibility of identifying individual drivers from their driving signatures embedded in these telematics data. The vehicle telematics data used in this study were collected from a small fleet of 30 Ford Fiesta vehicles driven by 30 volunteer drivers over 15 days of real-world driving in London, UK. The collected CAN signals included vehicle speed, accelerator pedal position, brake pedal pressure, steering wheel angle, gear position, and engine RPM. These signals were collected at approximately 5Hz frequency and transmitted to the cloud for offline driver identification modeling. A list of driving metrics was developed to quantify driver behaviors, such as mean brake pedal pressure and longitudinal jerk. Random Forest (RF) was used to predict driver IDs based on the developed driving metrics.