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Recent developments in the prediction of the contribution of wind noise to the interior SPL have opened a realm of new possibilities in terms of i) how the convective and acoustic sources terms can be identified, ii) how the interaction between the source terms and the side glass can be described and finally iii) how the transfer path from the sources to the interior of the vehicle can be modelled. This paper discusses in detail these three aspects of wind noise simulation and recommends appropriate methods to deliver required results at the right time based on i) simulation and experimental data availability, ii) design stage and iii) time available to deliver these results. Several simulation methods are used to represent the physical phenomena involved such as CFD, FEM, BEM, FE/SEA Coupled and SEA.

As material cleanliness and bearing lubrication have improved, wheel bearings are experiencing less raceway spalling failures from rotating fatigue. Warranty part reviews have shown that two of the larger failure modes for wheel bearings are contaminant ingress and Brinell damage from curb and pothole impacts. Warranty has also shown that larger wheels have higher rates of Brinell warranty. This paper discusses the Brinell failure mode for bearings. It reviews a vehicle test used to evaluate Brinell performance for wheel bearings. The paper also discusses a design of experiments to study the effects of factors such as wheel size, vehicle loading and vehicle position versus the bearing load from a vehicle side impact to the wheel. As the trend in vehicle styling is moving to larger wheels and low profile tires, understanding the impact load can help properly size wheel bearings.

Connected and automated vehicle (CAV) technologies promise a substantial decrease in traffic accidents and traffic jams, and bring new opportunities for improving vehicle’s fuel economy. However, testing autonomous vehicles in a real world traffic environment is costly, and covering all corner cases is nearly impossible. Furthermore, it is very challenging to create a controlled real traffic environment that vehicle tests can be conducted repeatedly and compared fairly. With the capability of allowing testing more scenarios than those that would be possible with real world testing, simulations are deemed safer, more efficient, and more cost-effective. In this work, a full-scale simulation platform was developed to simulate the infrastructure, traffic, vehicle, powertrain, and their interactions. It is used as an effective tool to facilitate control algorithm development for improving CAV’s fuel economy in real world driving scenarios.

The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.

CAE capabilities have long been used for performing static and dynamic structural analysis during the seat design process. More recently, the soft parts of the seat including foams, trim and suspension have also been modeled with CAE. The purpose of this modeling is to better understand the physical phenomena which are involved in the sitting process, to enhance seat design knowledge, and to replace as much physical testing during the design process with virtual, CAE testing. This paper presents the first part of a multi-phased, both experimental and numerical project. The aim of this first stage is to assess the capabilities of a CAE methodology to predict FMVSS 202a backset. Based on CAD data, a finite element mesh of the seat was built. The mechanical behavior of all parts was characterized through experiments on material samples.

This paper presents the second part of a multi-phased, both experimental and numerical project, devoted to the use of Virtual Prototyping techniques for seat design. The aim of this stage is to assess the capabilities of a CAE methodology to predict some comfort-related mechanical parameters, such as overall hardness and plushness, as a base engineering approach to quantify an occupant perception of both long- and short-term comfort. For hardness, a simple human surrogate (SAE AM50 Buttock Form) is applied on the bottom cushion of a fully trimmed, current production FORD seat, following a load cycle. For initial softness, a round probe is indented at different locations of both backrest and bottom cushions, following loading cycles. The resulting load-deflection curves predicted by numerical simulation are in good agreement with the experimental ones.

Heating devices are effective technologies to strengthen emission robustness of AfterTreatment Systems (ATS) and to guarantee emission compliance in the new boundaries given by upcoming legislations. Moreover, they allow to manage the ATS warm-up independently from engine operating conditions, thereby reducing the need for specific combustion strategies. Within heating devices, an attractive solution to provide the required thermal power without mandating a 48V platform is the fuel burner. In this work, a model-based control coordinator to manage the interaction between engine, ATS and fuel burner device has been developed, virtually validated, and optimized. The control function features a burner model and a control logic to deliver the needed amount of thermal energy, while ensuring ATS hardware protection.

To develop future electrical and autonomous cars, it is important to virtually test the car in real driving circumstances, including on wet road or under heavy rain conditions. It is especially critical to check that no water prevents the sensors of the driving assistance systems or autonomous cars from working properly, that water intrusion does not disturb electrical equipment, and that the driver’s visibility remains good under rain condition. ESI Group has introduced the Finite Point Method (FPM) in Virtual Performance Solution (VPS) as a CFD mesh free module in order to simulate the interaction of water with the car structure. It was initially specialized for tank sloshing and water drain applications for car closures and is now extended to other application fields. The objective is to enable a holistic prediction of the car behavior under realistic driving conditions, using a virtual car prototype.

This paper presents an integrated simulation process which has been performed in order to assess the riding comfort performance of a vehicle seat system virtually. Present methods of seat comfort design rely on the extensive testing of numerous hardware prototypes. In order to overcome the limitations of this expensive and time-consuming process, and to fasten innovation, simulation-based design has to be used to predict the seat comfort performance very early in the seat design process, leading to a drastic reduction in the number of physical prototypes. The accurate prediction of the seat transfer function by numerical simulation requires a complete simulation chain, which takes into account the successive stages determining the final seat behaviour when submitted to vibrations. First the manufacturing stresses inside the cushion, resulting from the trimming process, are computed.

The establishment of highly-diluted combustion strategies is one of the major challenges that the next generation of sustainable internal combustion engines must face. The desirable use of high EGR rates and of lean mixtures clashes with the tolerable combustion stability. To this aim, the development of numerical models able to reproduce the degree of combustion variability is crucial to allow the virtual exploration and optimization of a wide number of innovative combustion strategies. In this study ignition experiments using a conventional coil system are carried out in a closed volume combustion vessel with side-oriented flow generated by a speed-controlled fan. Acquisitions for four combinations of premixed propane/air mixture quality (Φ=0.9,1.2), dilution rate (20%-30%) and lateral flow velocity (1-5 m/s) are used to assess the modelling capabilities of a newly developed spark-ignition model for large-eddy simulation (GLIM, GruMo-UniMORE LES Ignition Model).

Quality and refinement are of paramount importance for luxury vehicles. The rapid electrification of the automotive industry has increased the contribution of aeroacoustics to the consumer perception of sound quality. The ability to predict whole vehicle aeroacoustic interior noise is essential in the development of vehicles with an extraordinary acoustic environment. This publication summarises the development of a process to combine lattice Boltzmann computational fluid dynamics simulations, with a whole vehicle statistical energy analysis model, to predict the aeroacoustic contribution from all relevant sources and paths. The ability to quantify the relative contribution of glazing panels and path modifications was also investigated. The whole vehicle aeroacoustic interior noise predictions developed, were found to be within 2dB(A) of comparable test vehicle wind tunnel measurements, across a broad frequency range (250-5000 Hz).

This paper summarizes the validation of prototype vehicle-to-infrastructure (V2I) safety applications based on Dedicated Short Range Communications (DSRC) in the United States under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). After consideration of a number of V2I safety applications, Red Light Violation Warning (RLVW), Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure Warning (RSZW/LC) were developed, validated and demonstrated using seven different vehicles (six passenger vehicles and one Class 8 truck) leveraging DSRC-based messages from a Road Side Unit (RSU). The developed V2I safety applications were validated for more than 20 distinct scenarios and over 100 test runs using both light- and heavy-duty vehicles over a period of seven months. Subsequently, additional on-road testing of CSW on public roads and RSZW/LC in live work zones were conducted in Southeast Michigan.

The comfort assessment of seats in the automotive industry has historically been accomplished by subjective ratings. This approach is expensive and time consuming since it involves multiple prototype seats and numerous people in supporting processes. In order to create a more efficient and robust method, objective metrics must be developed and utilized to establish measurable boundaries for seat performance. Objective measurements already widely accepted, such as IFD (Indentation Force Deflection) or CFD (Compression Force Deflection) [1], have significant shortcomings in defining seat comfort. The most obvious deficiency of these component level tests is that they only deal with a seats' foam rather than the system response. Consequently, these tests fail to take into account significant factors that affect seat comfort such as trim, suspension, attachments and other components.

Statistical Energy Analysis (SEA) is widely used for modeling the vibro-acoustic response of large and complex structures. SEA makes simulations practical thanks to its intrinsic statistical approach and the lower computational cost compared to FE-based techniques. However, SEA still requires underlying models for subsystems and junctions to compute the SEA coefficients which appear in the power balance equations of the coupled system. Classically, such models are based on simplified descriptions of the structures to allow analytical or semi-analytical developments. To overcome this limitation, the authors have proposed a general approach to SEA which only requires the knowledge of impedances of the structures to compute SEA coefficients. Such impedances can always be computed from an accurate FE model of each component of a coupled system.

The Hybrid FE-SEA method has been used to create a fast/efficient model to predict structure-borne noise propagation in a fully trimmed vehicle over the frequency range from 200 to 1000 Hz. The method was highlighted along with the modeling process and extensive validation results in previously published papers [1-3]. The use of the model to analyze structure-borne noise in the full vehicle, and to design and evaluate the impact of counter measures was described. In this study, the Hybrid FE-SEA method is used identify potential design changes to improve the acoustic performance. First, results from a noise path analysis are used to identify key contributors to interior noise. Next, potential design strategies for reducing the interior noise are introduced along with implications on the model. Finally, sample prediction results illustrating the impact of design changes on interior noise levels are shown along with experimental validation results.

This paper studies the use of active rear steering (4-wheel steering) to change the transient lateral dynamics and body motion of passenger cars in the stable or linear region of the tires. Rear steering systems have been used for several decades to improve low speed turning maneuverability and high speed stability, and various control strategies have been previously published. With a model-based, feed-forward rear steer control strategy, the lateral transient can be influenced separately from the steady-state steering gain. This lateral transient is influenced by many vehicle parameters, but we will look at the influence of active rear steer and various tire types such as all-season, snow, and summer. This study will explore the ability for a rear steering system to change the lateral transient to a step steer input, compared to the effect of changing tire types.

Transmission spin loss has significant influence on the vehicle fuel economy. Transmission output chain may contribute up to 10~15% of the total spin loss. However, the chain spin loss information is not well documented. An experimental study was carried out with several transmission output chains and simulated transmission environment in a testing box. The studies build the bases for the chain spin loss modeling and depicted the influences of the speed, the sprocket sizes, the oil levels, the viscosity, the temperatures and the baffle. The kriging method was employed for the parameter sensitivity study. A closed form of empirical model was developed. Good correlation was achieved.

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

Currently, Model Based Development (MBD) is the de-facto methodology in automotive industry. This has led to conversions of legacy code to Simulink models. Our previous work was related to implementing the C2M tool to automatically convert legacy code to Simulink models. While the tool has been implemented and deployed on few OEM pilot code-sets there were several improvement areas identified w.r.t. the generated models. One of the improvement areas identified was that the generated model used atomic blocks instead of abstracted blocks available in Simulink. E.g. the generated model used an ADD block and feedback loop to represent an integration operation instead of using an integrator block directly. This reduced the readability of the model even though the functionality was correct. Thus, as a user of the model, an engineer would like to see abstract blocks rather than atomic blocks.

Torque ripple of electric motors is a unique feature in Battery Electric Vehicles (BEV) affecting vehicle performance. It is one of the disturbances from electric motors resulting in unpleasant vehicle fore-aft vibrations at specific vehicle speeds. In this study, the torque ripple modeling and simulation procedure has been developed. Critical modeling contents in a full vehicle ADAMS model and a brief overview of the propulsion control are described. Analytical data sets for torque fluctuations (torque ripple) from a couple of different sources are incorporated in the model. The CAE simulation procedure was applied to simulate vehicle performances of a General Motors Battery Electric Vehicle in an early vehicle design phase. Torque ripple phenomena are simulated both in an open-loop and closed-loop propulsion control environment to see how much vehicle fore-aft vibration suppression is achieved by the motor control methods.