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Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features.

Sintered parts mechanical properties are very sensitive to final density, which inevitable cause an enormous density gradient in the green part coming from the compaction process strategy. The current experimental method to assess green density occurs mainly in set up by cutting the green parts in pieces and measuring its average density in a balance using Archimedes principle. Simulation is the more accurate method to verify gradient density and the main benefit would be the correlation with the critical region in terms of stresses obtained by FEA and try to pursue the optimization process. This paper shows a case study of a part that had your fatigue limit improved 1000% using compaction process simulation for better optimization.

In recent decades, significant technological advances have made cruise control systems safer, more automated, and available in more driving scenarios. However, comparatively little progress has been made in optimizing vehicle efficiency while in cruise control. In this paper, two distinct strategies are proposed to deliver efficiency benefits in cruise control by leveraging flexibility around the driver’s requested set speed, and road information that is available on-board in many new vehicles. In today’s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions. Introducing even a small allowable “error band” around the set speed can allow the propulsion system to operate in a pseudo-steady state manner across most terrain. As long as the vehicle can remain in the allowed speed window, it can maintain a roughly constant load, traveling slower up hills and faster down hills.

The On-Board Diagnostics (OBD) system can detect problems with the vehicle’s engine, transmission, and emissions control systems to generate error codes that can pinpoint the source of the problem. However, there are several wear and tear parts (air filter, oil filter, batteries, engine oil, belt/chain, clutch, gear tooth) that are not diagnosed but replaced often or periodically in motorcycles/ power sports applications. Traditionally there is a lack of availability of in-field and on-board assistive tools to diagnose vehicle health for 2wheelers. An alert system that informs the riders about health and remaining useful life of their motorcycle can help schedule part replacements, ensuring they are always trip-ready and have a stress-free ownership and service experience. This information can also aid in the correct assessment during warranty claims.

After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance.

Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst.

The thermal operational safety (TOS) of a vehicle ensures that no component exceeds its critical temperature during vehicle operation. To enhance the current TOS validation process, a data-driven approach is proposed to predict maximum component temperatures of a new vehicle project by leveraging the historical thermal wind tunnel data from previous vehicle projects. The approach intends to support engineers with temperature predictions in the early phase and reduce the number of wind tunnel tests in the late phase of the TOS validation process. In the early phase, all measurements of the new vehicle project are predicted. In the late phase, a percentage of measurements with the test vehicle used for the model training and the remaining tests are predicted with the trained ML model. In a first step, data from all wind tunnel tests is extracted into a joint dataset together with metadata about the vehicle and the executed load case.

In the automotive embedded system domain, the measurements from vehicle and Hardware-In-Loop are currently evaluated against the testcases, either manually or via automation scripts. These evaluations are localized; they evaluate a limited number of signals for a particular measurement without considering system-level behavior. This results in defect leakage. This study aims to develop a tool that can notify anomalies at the signal level in a new measurement without referring to the testcases, considering a more significant number of system-level signals, thereby significantly reducing the defect leakage. The tool learns important features and patterns of each maneuver from many historical measurements using deep learning techniques. We tried two CNN (convolution neural network) models. The first one is a specially designed CNN that does this maneuver classification and class-specific feature extraction.

Manual transmissions for passenger cars are facing pressures due to rapid growth of automatic transmissions, which already represents more than 60% of Brazil market, and from higher torque demand due to strict emission legislation, which turbo engines had presented great contribution to it. To solve this contradictory issue, gears with higher strength and lower cost have been studied to replacement Nickel by Niobium in the steels. Furthermore, this technology could be applied to solve the issues with electrified vehicle, where high torque, speed and lifetime are demanded pursued for gears. This study aimed to build prototypes and compare the S-N curves, fracture analysis, microstructure for three kinds of steels (QS4321 with Ni, QS1916 FG without Ni & with Nb and QS 1916 without Ni and Nb) in the condition carburized, hardened and tempered with and without shot peening.

As the automobile industry is moving towards Electrical vehicles, it becomes very important to have low cost and robust solution to seal all the internal Battery sub systems. It’s a known fact that various IC engine Vehicles are already using Room temperature vulcanized rubber (RTV) for many metal and composite sealing interfaces. Nevertheless, it always needs a good structural design to have good sealing performance. For designing a robust RTV joint for composite structures, it becomes important to have standard RTV chamfers. Sometimes even with these standards, it becomes very costly in having warranty issues when we have weak structure around RTV chamfers. Any joint structure involves multiple design parameters which might impact the sealing performance. Some of the joint structural parameters should be well designed at the early phase of product development cycle, which otherwise will later add lot of cost in modifying the product with its integrated components.

Hyper-elastic seals are extensively used in automotive applications for sealing various joints in assembly. They are also used in sealing battery packs. They are used in various sizes and shapes. Most of the gaskets used are solid gaskets. Hollow gaskets are also being used. Hollow gaskets typically have a fluid like air trapped inside. Analyzing these hollow gaskets also requires involving the physics of the fluid inside. The trapped fluid affects the performance of the gasket like contact pressure and width. Objective of this study is to analyze the hollow gasket performance including the effect of air trapped inside. The effect of air on performance of the hollow seal is also studied. Fluid Cavity capability in ABAQUS was selected after literature study to simulate the effect of trapped fluid (Air) on seal performance.

The NVH-development cycle of vehicle components often requires a source characterization separated from the vehicle itself, which leads to the implementation of test bench setups. In the context of frequency based substructuring and transfer path analysis, a component can be characterized using Blocked Forces. The following paper provides a comparison of methods between an acceleration-based in-situ and a new hybrid in-situ Blocked Force determination, using measurements of an artificially excited electric power steering (EPS). Under real-life conditions on a test rig, the acceleration-based in-situ approach often shows limitations in the lower frequency range, due to relatively bad signal-to-noise ratio at the indicator sensors, while delivering accurate results in the higher spectrum. Due to considerable loads on components in operation, the stiffness of the test-rig cannot be decreased arbitrarily.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

Any young person who has taken delight in playing with toy slot cars knows that the world of racing and the world of electric cars has been intertwined for a long time. And anyone who has driven a modern performance electric vehicle knows that the instant acceleration, exhilarating speeds, and joy of driving of slot cars is reflected in these full sized “toys”, with the many more practical benefits that come from being full-sized and steerable. There is strong foreshadowing of a vibrant future for performance cars in some of the EV’s on the market now and in the near future, some offering “ludicrous” acceleration, and others storied nameplates with performance to match. The ease at which powerful electric drives can capably hurtle a massive vehicle around the track at high speeds, combined with the potential for the same electric drives to exert powerful regenerative braking, creates a very interesting situation for brake engineers.

Raising demands towards lightweight design paired with a loss of originally predominant engine noise pose significant challenges for NVH engineers in the automotive industry. From an aeroacoustic point of view, low frequency buffeting ranks among the most frequently encountered issues. The phenomenon typically arises due to structural transmission of aerodynamic wall pressure fluctuations and/or, as indicated in this work, through rear vent excitation. A possible workflow to simulate structure-excited buffeting contains a strongly coupled vibro-acoustic model for structure and interior cavity excited by a spatial pressure distribution obtained from a CFD simulation. In the case of rear vent buffeting no validated workflow has been published yet. While approaches have been made to simulate the problem for a real-car geometry such attempts suffer from tremendous computation costs, meshing effort and lack of flexibility.

The advancing electrification of the powertrain is giving rise to new challenges in the field of acoustics. Film capacitors used in power electronics are a potential source of high-frequency interfering noise since they are exposed to voltage harmonics. These voltage harmonics are caused by semiconductor switching operations that are necessary to convert the DC voltage of the battery into three-phase alternating current for an electrical machine. In order to predict the acoustic characteristics of the DC-link capacitor at an early stage of development, a multiphysical chain of effects has to be addressed to consider electrical and mechanical influences. In this paper, a new method to evaluate the excitation amplitude of film capacitor windings is presented. The corresponding amplitudes are calculated via an analytical strain based on electromechanical couplings of the dielectric within film capacitors.

Driving cycles are valuable tools for emissions calibration at engine and powertrain test beds. While generic velocity profiles were sufficient in the past, legislative changes and increasing complexity of powertrain and exhaust aftertreatment systems require a new approach: Realistically transient cycles - which include critical driving maneuvers and can be tailored to a specific powertrain configuration - are needed to optimize the emission behavior of the said powertrain. For the generation of realistic velocity profiles, the Markov chain approach has been widely used and described in literature. However, this approach, so far, has only been used to generate cycles that are statistically representative of a large database of real driving trips, which is typically not available during the early stages of development of a new powertrain.

Over the last two decades, engine research was mainly focused on reducing fuel consumption in view of compliance with more stringent homologation cycles and customer expectations. As it is well known, the objective of overall engine efficiency optimization can be achieved only through the improvement of each element of the efficiency chain, of which mechanical constitutes one of the two key pillars (together with thermodynamics). In this framework, the friction reduction for each mechanical subsystem has been one of the most important topics of modern Diesel engine development. The present paper analyzes the crankshaft potential as contributor to the mechanical efficiency improvement, by investigating the synergistic impact of crankshaft design itself and oil viscosity characteristics (including new ultra-low-viscosity formulations already discussed by the author in [1]).

Service-oriented architectures (SOAs) are well-established solutions in the IT industry. Their use in the automotive domain is still on the way. Up to now, the automotive domain has taken advantage of service-oriented architectures only in the area of infotainment and not for systems with hard real-time requirements. However, applying SOA to such systems has just started but is missing suitable design and verification methodologies. In this context, we target to include the notion of model-based design to address fail-operational systems. As a result, a model-based approach for the development of fail-operational systems based on dynamic reconfiguration using a service-oriented architecture is illustrated. For the evaluation, we consider an example function of an automatically controlled braking system and analyze the reconfiguration time when the function fails.

Classical decentralized architectures based on large networks of microprocessor-based Electronic Control Units (ECU), namely those used in self-driving cars and other highly-automated applications used in the automotive industry, are becoming more and more complex. These new, high computational power demand applications are constrained by limits on energy consumption, weight, and size of the embedded components. The adoption of new embedded centralized electrical/electronic (E/E) architectures based on dynamically reconfigurable hardware represents a new possibility to tackle these challenges. However, they also raise concerns and questions about their safety. Hence, an appropriate evaluation must be performed to guarantee that safety requirements resulting from an Automotive Safety Integrity Level (ASIL) according to the standard ISO 26262 are met. In this paper, a methodology for the evaluation of dynamically reconfigurable systems based on centralized architectures is presented.