Search Results

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.

Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

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.

Hybrid powertrain vehicles inherently create discontinuous sounds during operation. The discontinuous noise created from the electrical motors during transition states are undesirable since they can create tones that do not correlate with the dynamics of the vehicle. The audible level of these motor whines and discontinuous tones can be reduced via common noise abatement techniques or reducing the amount of regeneration braking. One electronic solution which does not affect mass or fuel economy is Masking Sound Enhancement (MSE). MSE is an algorithm that uses the infotainment system to mask the naturally occurring discontinuous hybrid drive unit and driveline tones. MSE enables a variety of benefits, such as more aggressive regenerative braking strategies which yield higher levels of fuel economy and results in a more pleasing interior vehicle powertrain sound. This paper will discuss the techniques and signals used to implement MSE in a hybrid powertrain equipped vehicle.

This paper describes the new Honda R&D Americas Scale Model Wind Tunnel (SWT). To help address Honda's ongoing need to improve fuel economy, reduce the driving force of a vehicle, and decrease product development time, the wind tunnel was developed and implemented to achieve high accuracy aerodynamic predictions for product development and a significantly improved capability for vehicle aerodynamics research. The SWT can accommodate model scales up to 50%. The ¾-open jet test section has a top speed of 250 km/h, a 5-belt moving ground plane with a long center belt for proper wake simulation, a test section designed specifically for very low static pressure gradient, three separate dynamic pressure measurement systems for state-of-the-art blockage corrections, and an overhead traverse for specialized measurement activities. This paper describes the decision process that led to the SWT, key commissioning results, and performance validation results with models installed.

The Honda Automotive Laboratories of Ohio (HALO) includes a new aeroacoustic wind tunnel located near Marysville, Ohio that started operations in 2022. This facility provides world-class aerodynamic flow quality and acoustic testing capabilities for the development of both passenger and motorsports vehicles. This closed-return ¾ open jet wind tunnel features a two-position flexible nozzle system with cross sections of 25 m2 and 18 m2, providing wind speeds of up to 250 km/h and 310 km/h, respectively. There is a ±180 degree turntable with boundary layer control systems, and interchangeable single belt and 5-belt moving ground plane (MGP) modules. Extensive applications of acoustic treatment in the test section and throughout the wind tunnel circuit provide a hemi-anechoic test environment and low background noise levels. A temperature control system provides uniform and stable air temperature over an operating environment between 10 °C and 50 °C.

The Ford Motor Company Rolling Road Wind Tunnel (RRWT) is a state-of-the-art aerodynamic wind tunnel test facility in Allen Park, Michigan. The RRWT has operated since January 2022 and is designed for passenger and motorsport vehicle development. The test facility includes an office area, three secure customer vehicle preparation bays, a garage area, a vehicle frontal area measurement system, and a full-scale ¾ open jet wind tunnel. The wind tunnel features an interchangeable single belt and 5-belt Moving Ground Plane (MGP) system with an integrated 6-component balance, a two-position nozzle, boundary layer removal systems, and two independent flow traverse systems. Each flow traverse has a large horizontal box beam and vertical Z-strut that can position the flow traverse accurately within the test volume.

The new BMW Aerodynamisches Versuchszentrum (AVZ) wind tunnel center includes a full-scale wind tunnel, "The BMW Windkanal" and an aerodynamic laboratory "The BMW AEROLAB." The AVZ facility incorporates numerous new technology features that provide design engineers with new tools for aerodynamic optimization of vehicles. The AVZ features a single-belt rolling road in the AEROLAB and a five-belt rolling road in the Windkanal for underbody aerodynamic simulation. Each of these rolling road types has distinct advantages, and BMW will leverage the advantages of each system. The AEROLAB features two overhead traverses that can be configured to study vehicle drafting, and both static and dynamic passing maneuvers. To accurately simulate "on-road" aerodynamic forces, a novel collector/flow stabilizer was developed that produces a very flat axial static pressure distribution. The flat static pressure distribution represents a significant improvement relative to other open jet wind tunnels.

Fabric materials have diverse applications in the automotive industry which include upholstery, carpeting, safety devices, and interior trim components. The textile industry has invested substantial effort toward development of standard testing techniques for characterizing mechanical properties of different fabric types (e.g. woven and knitted). However, there are presently no standards for determination of Young's modulus, Poisson's ratio and tensile stress-strain properties required for the detailed modeling of fabric materials in vehicle structural simulations. This paper presents results from uniaxial tensile tests of different automotive seat cover fabric materials. Digital image correlation, a full field optical method for measuring surface deformation, was used to determine tensile properties in both the warp/wale and the weft/course directions. The fabrics were tested with and without the foam backing.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Scania AB has opened the new CD7 climatic wind tunnel test facility, located at the Scania Technical Center in Södertälje, Sweden. This facility is designed for product development testing of heavy trucks and buses in a range of controllable environments. Having this unique test environment at the main development center enables Scania to test its vehicles in a controlled repeatable environment year round, improving lead times from design to production, producing higher quality and more reliable vehicles, and significantly improves the capability for large vehicle performance research. This state-of-the-art facility provides environmental conditions from -35°C to 50°C with humidity control from 5 to 95 percent. The 13 m2 nozzle wind tunnel can produce wind speeds up to 100 km/h. The dynamometer is rated at 800 kW for the rear axle and 150 kW for the front axle, which also has ±10° yaw capability.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

In recent years, there has been renewed attention focused on open jet correction methods, in particular on the two-measurement method of E. Mercker, K. Cooper, and co-workers. This method accounts for blockage and static pressure gradient effects in automotive wind tunnels and has been shown by both computations and experiments to appropriately adjust drag coefficients towards an on-road condition, thus allowing results from different wind tunnels to be compared on a more equitable basis. However, most wind tunnels have yet to adopt the method as standard practice due to difficulties in practical application. In particular, it is necessary to measure the aerodynamic forces on every vehicle configuration in two different static pressure gradients to capture that portion of the correction. Building on earlier proof-of-concept work, this paper demonstrates a practical method for implementing the two-measurement procedure and demonstrates how it can be used for production testing.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.

Zinc-based electrogalvanized (EG) and hot-dip galvanized (HDGI) coatings have been widely used in automotive body-in-white components for corrosion protection. The formability of zinc coated sheet steels depends on the properties of the sheet and the interactions at the interface between the sheet and the tooling. The frictional behavior of zinc coated sheet steels is influenced by the interfacial conditions present during the forming operation. Friction behavior has also been found to deviate from test method to test method. In this study, various lubrication conditions were applied to both bending under tension (BUT) test and a draw bead simulator (DBS) test for friction evaluations. Two different zinc coated steels; electrogalvanized (EG) and hot-dip galvanized (HDGI) were included in the study. In addition to the coated steels, a non-coated cold roll steel was also included for comparison purpose.

The Lockheed Martin Low-Speed Wind Tunnel (LSWT) is a closed-return wind tunnel with two solid-wall test sections. This facility originally entered into service in 1967 for aerodynamic research of aircraft in low-speed and vertical/short take-off and landing (V/STOL) flight. Since this time, the client base has evolved to include a significant level of automotive aerodynamic testing, and the needs of the automotive clientele have progressed to include acoustic testing capability. The LSWT was therefore acoustically upgraded in 2016 to reduce background noise levels and to minimize acoustic reflections within the low-speed test section (LSTS). The acoustic upgrade involved detailed analysis, design, specification, and installation of acoustically treated wall surfaces and turning vanes in the circuit as well as low self-noise acoustic wall and ceiling treatment in the solid-wall LSTS.

In today’s race for improved fuel economy and lower emissions from gasoline engines, precise metering of delivered fuel is essential. Gasoline Direct Injection fuel systems provide the means for improved combustion efficiency through mixture preparation and better atomization. These improvements can be achieved from both increasing fuel pressure and using multiple injection events, which significantly reduce the required energizing time per injection, and in a number of cases, force the injector to operate at less than full stroke. When the injector operates in this condition, the influence of variation in injector dynamics account for a large percentage of the delivered fuel and require compensation to ensure accurate fuel delivery. Injector dynamics such as opening delay and closing time are influenced by operating conditions such as fuel pressure, energizing time, and temperature.

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine.