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With the advent of this new era of electric-driven automobiles, the simulation and virtual digital twin modeling world is now embarking on new sets of challenges. Getting key insights into electric motor behavior has a significant impact on the net output and range of electric vehicles. In this paper, a complete 3D CFD model of an Electric Motor is developed to understand its churning losses at different operating speeds. The simulation study details how the flow field develops inside this electric motor at different operating speeds and oil temperatures. The contributions of the crown and weld endrings, crown and weld end-windings, and airgap to the net churning loss are also analyzed. The oil distribution patterns on the end-windings show the effect of the centrifugal effect in scrapping oil from the inner structures at higher speeds. Also, the effect of the sump height with higher operating speeds are also analyzed.

Instrument Panel Cluster (IPC), is a key ECU in vehicles. As IPC is a visual product, testing the software features of IPC is highly manual effort. Software Testing constitutes for approx. 35% of the total Software Development Life Cycle (SDLC). High focus on quick to market, shorter SDLC coupled with manual validation environment poses a challenge of increasing testing efficiency and improving software quality. This challenge drove the need to investigate a solution to automate the testing process and cut down the huge manual effort that goes into validating an Instrument Panel Cluster (IPC) software. The proposed intrusive and non-intrusive approaches to automate the testing process of IPC software employs a Frame Grabbing technique for the former approach and a Camera based technique for the latter. Both the approaches are robust, reliable, and scalable and covers the major portion of Vehicle Instrument cluster test scenarios.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

This paper presents a computational fluid dynamics (CFD) model for predicting power losses associated with churning of oil by gears or other similar rotating components. The modeling approach and parameters are optimized to ensure the accuracy, robustness, and computational efficiency of these predictions. These studies include a look at two types of mesh and a turbulence model selection. The focus is on multiple reference frame (MRF) modeling technique for its computational efficiency advantage. Model predictions are compared to previously published experimental data [1] under varying operating conditions typical for an automotive transmission application. The model shows good agreement with the hardware both quantitatively and qualitatively, capturing the trends with speed and submersion level. The paper concludes with presenting some key lessons learned, and recommendation for future work to ultimately build a highly reliable tool as part of the virtual product development.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

The past five years have seen significant research into autonomous vehicles that employ a by-wire steering rack actuator and no steering wheel. There is a clear synergy between these advancements and the parallel development of complete Steer-by-Wire systems for human-operated passenger vehicle applications. Steer-by-Wire architectures presented thus far in the literature require multiple layers of electrical and/or mechanical redundancy to achieve the safety goals. Unfortunately, this level of redundancy makes it difficult to simultaneously achieve three key manufacturer imperatives: safety, reliability, and cost. Hindered by these challenges, as of 2020 only one production car platform employs a Steer-by-Wire system. This paper presents a Steer-by-Wire architectural solution featuring fail-operational steering control architected with the objective of achieving all system safety, reliability, and cost goals.

Automotive technology is rapidly changing with electrification of vehicles, driver assistance systems, advanced safety systems etc. This advancement in technology is making the task of validation and verification of embedded software complex and challenging. In addition to the component testing, integration testing imposes even tougher requirements for software testing. To meet these challenges dSPACE is continuously evolving the Hardware-In-the-Loop (HIL) technology to provide a systematic way to manage this task. The paper presents developments in the HIL hardware technology with latest quad-core processors, FPGA based I/O technology and communication bus systems such as Flexray. Also presented are developments of the software components such as advanced user interfaces, GPS information integration, real-time testing and simulation models. This paper provides a real-world example of implication of integration testing on HIL environment for Chassis Controls.

Euro 7/VII regulations are currently under discussion and are expected to be the last big regulatory step in Europe. From available documentation, it is clear the aim of further regulating the extended conditions of use which are still responsible of high emission events (e. g. cold start or altitude) as well as regulating secondary emissions such as NH3, N2O, CH4, Aldehydes (HCHO). Even if not completely fixed yet, the EU7 limits will be challenging for internal combustion engines and even more for Diesel. Despite a consistent reduction of market share, Diesel engines are expected to remain a significant portion in certain sectors such as Heavy duty (HD) and Light-commercial vehicle (LCV) for some decades. In order to reach the new limits being proposed, besides minimizing engine-out emissions, Diesel powertrain will need an aftertreatment system able to work at very high efficiency right after engine start and in almost every working and environmental condition.

In traditional FE based structure-borne noise analysis, interior trims are normally modeled as lump masses in the FE structure model and acoustic specific impedance of the trim is assigned to the FE acoustics model when necessary. This simplification has proven to be effective and sufficient for low frequency analysis. However, as the frequency goes into the mid-frequency range, the elastic behavior of the trim may impose some effects on the structural and acoustic responses. The approach described in this paper is based on the structural FE and acoustic SEA coupling analysis developed by ESI, aiming to improve the modeling efficiency for a possible quick turnaround in virtual assessments.

The paper presents the main results of a study on the simulation of energy efficient management of on-board electric and thermal systems for a medium-size passenger vehicle featuring a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. A set of advanced technologies has been considered on the basis of very aggressive fuel economy targets: base-engine downsizing and friction reduction, combustion optimization, active thermal management, enhanced aftertreatment and downspeeding. Mild-hybridization has also been added with the goal of supporting the downsized/downspeeded engine performance, performing energy recuperation during coasting phases and enabling smooth stop/start and acceleration. The simulation has implemented a dynamic response to the required velocity and manual gear shift profiles in order to reproduce real-driver behavior and has actuated an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM).

As the proliferation of downsized boosted engines continues, it becomes increasingly important to understand how engine and vehicle hardware impact vehicle transient response. Several different methodologies can be used to understand hardware impacts, such as vehicle testing, 0-D vehicle models, and constant engine speed load steps. The next evolution of predicting vehicle transient response is to transition to a system level vehicle analysis by coupling a detailed engine model, utilizing crank angle resolved calculations, with a simple vehicle model. This allows for the evaluation of engine and vehicle hardware effects on vehicle acceleration and the rate of change of vehicle acceleration, or jerk, and the tradeoffs that can be made between the hardware in early program development. By comparing this system level vehicle model to the different methodologies, it can be shown that a system level vehicle analysis allows for higher fidelity evaluations of vehicle transient response.

Battery Electric Vehicles (BEVs) are becoming more competitive day by day to achieve maximum peak power and energy requirement. This poses challenges to the design of Thermal Interface Material (TIM) which maintains the cell temperature and ensure retention of cell and prevent electrolyte leak under different crash loads. TIM can be in the form of adhesives, gels, gap fillers. In this paper, TIM is considered as structural, and requires design balance with respect to thermal and mechanical requirements. Improving structural strength of TIM will have negative impact on its thermal conductivity; hence due care needs to be taken to determine optimal strength that meets both structural and thermal performance. During various crash conditions, due to large inertial force of cell and module assembly, TIM is undertaking significant loads on tensile and shear directions. LS-DYNA® is used as simulation solver for performing crash loading conditions and evaluate structural integrity of TIM.

This paper is a continuation of previously published technical paper SAE 2022-01-0314. The preceding work described an analytical methodology to predict the vehicle interior trim squeak and rattle issues upfront in the design cycle using a “relative displacement” or “contact force” metric; the methodology was implemented on the center floor console armrest latch using a linear finite element model. The work is logically extended to predict the squeak and rattle issues quantitatively using now an “acoustic noise” metric, this enables a direct comparison with the physical test results and helps to further refine the design best practices. This approach combines Finite Element Method (FEM) and Boundary Element Method (BEM) to estimate structural vibration response and acoustic sound pressure respectively.

The modern braking system in the field today may be controlled by over a million lines of computer code and may feature several hundred moving parts. Although modern brake systems generally deliver performance, even with partial failures present in the system, that is well above regulatory minimums, they also have a level of complexity that extends well beyond what the authors of existing regulations had envisioned. Complexity in the braking system is poised for significant increases as advanced technologies such as self-driving vehicles are introduced, and as multiple systems are linked together to provide vehicle-level “features” to the driver such as deceleration (which can invoke service braking, regenerative braking, use of the parking brake, and engine braking). Rigorous safety-case analysis is critical to bring a new brake system concept to market but may be too tedious and rely on too many assumptions to be useful in the early architecting stages of new vehicle development.

Machine Learning (ML) based technologies are increasingly being used to fulfill safety-critical functions in autonomous and advanced driver assistance systems (ADAS). This change has been spurred by recent developments in ML and Artificial Intelligence techniques as well as rapid growth of computing power. However, demonstrating that ML-based systems achieve the necessary level of safety integrity remains a challenge. Current research and development work focused on establishing safe operation of ML-based systems presents individual techniques that might be used to gain confidence in these systems. As a result, there is minimal guidance for supporting a safety standard such as ISO 26262 - Road Vehicles - Functional Safety, to enable the development of ML-based systems. This paper presents a survey of recent ML literature to identify techniques and methods that can contribute to meeting ISO 26262 requirements.

Remote Function Actuator (RFA) systems are widely used as the standard solution for conveniently accessing vehicles by remote control. To accelerate product development cycles and reduce engineering costs of physical test, a computer aided engineering (CAE) method has been developed to predict transmission range of the RFA system. Firstly, the detailed computational electromagnetic (CEM) models of the transmitting and receiving antennas were developed. Secondly, the articulated human model and the full vehicle meshed model were introduced to the CEM models to reflect the physical test environment. Lastly, the RFA system range model was built by including both the key fob held by an articulated human body and RFA module installed in the fully meshed vehicle. The transmission range could be extracted when the simulated received power reached the receiving sensitivity of the RFA module.

Premium instrument panels (IPs) contain passenger airbag (PAB) systems that are typically comprised of a stiff plastic substrate and a soft ‘skin’ material which are adhesively bonded. During airbag deployment, the skin tears along the scored edges of the door holding the PAB system, the door opens, and the airbag inflates to protect the occupant. To accurately simulate the PAB deployment dynamics during a crash event all components of the instrument panel and the PAB system, including the skin, must be included in the model. It has been recognized that the material characterization and modeling of the skin tearing behavior are critical for predicting the timing and inflation kinematics of the airbag. Even so, limited data exists in the literature for skin material properties at hot and cold temperatures and at the strain rates created during the airbag deployment.

Seat lateral support is often talked about as a design parameter, but usually in terms of psychological perception. There are many difficulties in quantifying lateral support mechanically to the engineering teams: Anthropometric variation causes different people to interact with the seat in different places and at different angles, BPD studies are usually planar and don’t distinguish between horizontal support and vertical resistance to sinking in, most mechanical test systems are typically single-DOF and can’t apply vertical and horizontal loads concurrently, and there is scant literature describing the actual lateral loads of occupants. In this study, we characterize the actual lateral loading on example seating from various sized/shaped occupants according to dynamic pressure distribution. From this information, a six-DOF load and position control test robot (KUKA OccuBot) is used to replicate that pressure distribution.

The impact of gasoline composition on vehicle particulate emissions response has been widely investigated and documented. Correlation equations between fuel composition and particulate emissions have also been documented, e.g. Particulate Matter Index (PMI) and Particulate Evaluation Index (PEI). Vehicle PM/PN emissions correlate very well with these indices. In a previous paper, global assessment with PEI on fuel sooting tendency was presented [1]. This paper will continue the previous theme by the authors, and cover China gasoline in more detail. With air pollution an increasing concern, along with more stringent emission requirements in China, both OEMs and oil industries are facing new challenges. Emissions controls require a systematic approach on both fuels and vehicles. Chinese production vehicle particulate emissions for a range of PEI fuels are also presented.

Felt-based dash panel insulation materials have traditionally been used as a sound barrier between the engine and passenger compartments in a vehicle to reduce the transmission of engine noise to the occupant space. Their structural performance has been mainly ignored due to the typically low stiffness and strength characteristics. Consequently, studies of the acoustic properties of these materials have been found in literature while no information was found on their mechanical behavior especially in dynamic loading conditions. More stringent requirements for occupant and pedestrian safety imposed by government regulations and the position of these materials in the impact zones of pedestrian head impact have brought attention to the material contribution to the energy absorption during the impact and the need to assess the mechanical properties of these materials.