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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.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

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.

Car manufacturers put large efforts into reducing wind noise to improve the comfort level of their cars. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof designs are tested to meet low-frequency buffeting (also known as boom) targets and broadband noise targets for the fully open sunroof with deflector and for the sunroof in vent position. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process.

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.

Speaker performance in Acoustic Vehicle Alerting System (AVAS) plays a crucial role for pedestrian safety. Sound radiation from AVAS speaker has obvious directivity pattern. Considering this feature is critical for accurately simulating the exterior sound field of electrical vehicles. This paper proposes a new process to characterize the sound directivity pattern of AVAS speaker. The first step of the process is to perform an acoustic testing to measure the sound pressure radiated from the speaker at a certain number of microphone locations in a free field environment. Based on the geometry of a virtual speaker, the locations of each microphone and measured sound pressure data, an inverse method, namely the inverse pellicular analysis, is adopted to recover a set of vibration pattern of the virtual speaker surface. The recovered surface vibration pattern can then be incorporated in the full vehicle numerical model as an excitation for simulating the exterior sound field.

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.

The automotive industry is facing many significant challenges that go far beyond the design and manufacturing of automobile products. Connected, autonomous and electric vehicles, smart cities, urbanization and the car sharing economy all present challenges in a fast-changing environment which the automotive industry must adapt to. Cars no longer are just standalone systems, but have become constituent systems (CS) in larger System of Systems (SoS) context. This is reflected in the emergence of several acronyms such as vehicle-to-everything (V2X), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-grid (V2G) expressions. System of Systems are defined systems of interest whose elements (constituent systems) are managerially and operationally independent systems. This interoperating and/or integrated collection of constituent systems usually produce results unachievable by the individual systems alone, for example the use of car batteries as virtual power plants.

In this paper, we present a novel algorithm designed to accurately trigger the engine coolant flow at the optimal moment, thereby safeguarding gas-engines from catastrophic failures such as engine boil. To achieve this objective, we derive models for crucial temperatures within a gas-engine, including the engine combustion wall temperature, engine coolant-out temperature, engine block temperature, and engine oil temperature. To overcome the challenge of measuring hard-to-measure signals such as engine combustion gas temperature, we propose the use of new intermediate parameters. Our approach utilizes a lumped parameter concept with a mean-value approach, enabling precise temperature prediction and rapid simulation. The proposed engine thermal model is capable of estimating temperatures under various conditions, including steady-state or transient engine performance, without the need for extra sensors.

Hood insulators are widely used in automotive industry to improve noise insulation, pedestrian impact protection and to provide aesthetic appeal. They are attached below the hood panel and are often complex in shape and size. Pedestrian head impacts are highly dynamic events with a compressive strain rate experienced by the insulator exceeding 300/s. The energy generated by the impact is partly absorbed by the hood insulators thus reducing the head injury to the pedestrian. During this process, the insulator experiences multi-axial stress states. The insulators are usually made of soft multi-layered materials, such as polyurethane or fiberglass, and have a thin scrim layer on either side. These materials are foamed to their nominal thickness and are compression molded to take the required shape of the hood. During this process they undergo thickness reduction, thereby increasing their density.

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.

As part of the United States Department of Energy's SuperTruck program, Volvo Trucks and its partners were tasked with demonstrating 50% improvement in overall freight efficiency for a tractor-trailer, relative to a best in class 2009 model year truck. This necessitated that significant gains be made in reducing aerodynamic drag of the tractor-trailer system, so trailer side-skirts and a trailer boat-tail were employed. A Lattice-Boltzmann based simulation method was used in conjunction with a Kriging Response Surface optimization process in order to efficiently describe a design space of seven independent parameters relating to boat-tail and side-skirt dimensions, and to find an optimal configuration. Part 1 concerns a fully-skirted tractor-trailer system, and consists of an initial phase of optimization, followed by a mid-project re-evaluation of constraints, and an additional period of optimization.

Aerodynamic performance assessment of automotive shapes is typically performed in wind tunnels. However, with the rapid progress in computer hardware technology and the maturity and accuracy of Computational Fluid Dynamics (CFD) software packages, evaluation of the production-level automotive shapes using a digital process has become a reality. As the time to market shrinks, automakers are adopting a digital design process for vehicle development. This has elevated the accuracy requirements on the flow simulation software, so that it can be used effectively in the production environment. Evaluation of aerodynamic performance covers prediction of the aerodynamic coefficients such as drag, lift, side force and also lift balance between the front and rear axle. Drag prediction accuracy is important for meeting fuel efficiency targets, prediction of front and rear lifts as well as side force and yawing moment are crucial for high speed handling.

Aerodynamic simulations of a 1:8-scale simplified tractor-trailer, designated as the Generic Conventional Model (GCM), were conducted using a Lattice-Boltzmann based solver. Comparisons were made to experimental measurements from the NASA Ames 12-Foot Pressure Wind Tunnel, including drag coefficients as a function of yaw, static and transient surface pressures, and three-component particle image velocimetry. The baseline model configuration was tested at yaw angles from 0 to 12 degrees, allowing the calculation of the wind-averaged drag coefficient. Results demonstrated that the simulation predicted body-axis drag within experimental uncertainty and also resolved the correct pressure distribution and flow structure in the separated flow regions including the tractor-trailer gap and trailer wake regions. The comparison of the experimental transient pressure spectra showed good agreement with the simulation results, both in magnitude and identification of dominant spectral peaks.

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.

Given their high-power density, large range of speed change, and reputation of being quieter than counter-shaft gear sets, planetary gear sets (PGS) have advantages to be applied in electric vehicle (EV) applications. Since electric drive unit (EDU) designs are often subject to accelerated development timelines with more versatile gear set layouts than conventional automotive transmissions, accurate prediction of PGS load sharing is needed. In the past, PGS load sharing imbalance used to be considered as a gear set problem focusing only on the effect to gear performance. Finding a closed-form formula has been a focus in gear design. However, early bearing failure in wind turbine gearboxes exposed the limitation of this strategy. With extensive field and laboratory testing, engineers started to notice that load sharing imbalance is essentially a system issue. Non-torque loads on PGS should be considered in the estimation by a gearbox system model.

Understanding a system behavior involves developing an accurate relationship between the explanatory (predictive) variables and the output response. When the observed data is ill-conditioned with potential collinear correlations among the measured variables, some of the statistical methods such as least squared method (LSM) fail to generate good predictive models. In those situations, other methods like Principal Component Regression (PCR) are generally applicable. Additionally, the PCR reduces the dimensionality of the system by making use of covariance relationship among the variables. In this paper, an improved regression method over PCR is proposed, which is based on the Trivial Principal Components (TPC). The TPC regression (TPCR) makes use of the covariance of the output response and predictive variables while extracting principal components. A new method of selecting potential principal components for variable reduction in TPCR is also proposed and validated.

Hybridization is a mainstream technology for automobiles, and its application is rapidly expanding in other fields. Marine propulsion is one such field that could benefit from electrification of the powertrain. In particular, for boats to sail in enclosed waterways, such as harbors, channels, lagoons, a pure electric mode would be highly desirable. The main challenge to accomplish hybridization is the additional weight of the electric components, in particular the batteries. The goal of this project is to replace a conventional 4-stroke turbocharged Diesel engine with a hybrid powertrain, without any penalty in terms of weight, overall dimensions, fuel efficiency, and pollutant emissions. This can be achieved by developing a new generation of 2-Stroke Diesel engines, and coupling them to a state-of-the art electric system. For the thermal units, two alternative designs without active valve train are considered: opposed piston and loop scavenged engines.