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Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

The process of assembling the bearing and crimp ring to the steering pinion shaft is intricate. The bearing is pressed into its position via the crimp ring, which is tipped inward and fully fitted into a groove on the pinion shaft. Only when the bearing is pressed to a low surface on the pinion shaft, the caulking force for the crimp ring is achieved. The final caulking distance for the crimp ring confirms the proper bearing position. Simulating this transient fitting process using CAE is a challenging topic. Key factors include controlling applied force, defining contact between bearing and pinion surface, and defining contact between crimp ring and bearing surface from full close to half open transition. The overall CAE process is validated through correlation with testing.

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods.

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

This work aims to develop a PA6 nanocomposite with glass fiber (GF) and graphene nanoplatelets (GNPs) focusing on automotive parts application. Polyamide 6 is a semi-crystalline polymer that exhibits high fatigue and flexural strength, making it viable for rigorous applications. Along with the improved electrical, mechanical, thermal, and optical performance achieved in PA6 and GF-based nanocomposites, they can fill complex geometries, have great durability, and are widely utilized due to their capacity of reducing the weight of the vehicle besides a cost reduction potential. The glass fiber is a filamentary composite, usually aggregated in polymeric matrices, which aims to amplify the mechanical properties of polymers, mainly the tensile strength in the case of PA6.

Manufacturing processes impact many factors on a product. Depending on the selected method, development time, part performance and cost are affected. In the automotive sector, there is a growing demand for weight reduction due to the advent of electrification and the greenhouse gas emission regulations. In addition, geometric complexity is a challenging factor for the feasibility of mass production of parts. In this scenario, plastic materials are a very interesting option for application in various vehicle parts, since these materials can be molded by injection, vacuum forming, among others, while maintaining good mechanical properties. Almost a third of a vehicle’s parts are polymeric, making the development of these materials strategic for car manufacturers. This article investigates the impact of the presence of fiberglass in a thermoplastic automotive body part.

Polyurethane (PU) foams are versatile in automotive applications for sound absorption, due to their superior acoustic-absorbing properties, vibration damping and robustness, and seat cushioning products due to their easiness of manufacturing process and cost-effectiveness. In recent studies, micro- and nano-particles were used to improve sound absorption efficiency, these fillers help to form interconnected pore structures in the foam matrix, and this interconnection of pores is advantageous in dissipating heat generated from wave friction with the air. Some of the micro- and nano-particles used are natural fibers (like cellulose, fir, palm), silica, clay, graphene and derivatives, zeolite, and others. This review is an overview of recent advances in the incorporation of fillers in PU foams and the influence they have on the sound absorption capacity of the foams.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

Fretting fatigue was observed in standard cylindrical fatigue samples at the regions in contact with the grips of the test frames during fatigue testing for AlSi10Mg aluminum alloy produced by laser powder bed fusion process (L-PBF). The failure of the fatigue sample grips occurs much earlier than the failure of the gauge section. This results in a damaged sample and the sample cannot be reused to continue the test. This type of failure is rarely seen in materials produced by traditional manufacturing processes. In this study, X-ray residual stress analysis was performed to understand the cause of failure for L-PBF AlSi10Mg with the as-built surface condition. The result indicates that the fretting fatigue failure was caused by the strong tensile residual stress in the as-built state combining with the fretting wear between the sample and the grip. A few potential solutions to avoid the fretting fatigue failure were investigated.

The vehicle instrument panel (IP) system has several interactions with the surrounding components such as the Dash, Cowl, Cross Car Beam (CCB), Floor, Body Side etc. With such interactions comes different loadings, usage scenarios, interfaces and design challenges to overcome. For the specific case of the IP to Cowl & Dash interfaces, the position and performance in different load cases, such as, but not limited to, vibration and heat expansion loading as well as the assembly process. A design solution is required to enhance the performance in all these scenarios while maintaining the cost, weight & complexity as low as possible. This paper describes the development process of an optimized solution with a multi-disciplinary approach using advanced computer aided engineering (CAE) optimization tools, which involved performance in multiple virtual evaluations and mass.

When manufacturing the stators in EV motors, stator wires are first coated with a layer of resin to provide primary insulation. After winding, impregnating varnish fills all voids within the windings and between the windings and lamination. In addition to electrically insulating the copper wires, another function of the varnish fill is to mechanically secure the copper wires from movement. The process is not complicated in terms of physics. In essence, the mechanics of the varnish flow is the balance of inertia force, viscous force, gravity and surface tension. However, understanding the fluid dynamics of the varnish flow is critical to predicting the quality of the varnish fill, which has a tremendous impact on motor performance. With the advancement of computational fluid dynamics (CFD), the industry can benefit greatly if the varnish trickling process can be tuned, without physical tryouts, to achieve optimal fill.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

The advancement of Machine-learning (ML) methods enables data-driven creation of Reduced Order Models (ROMs) for automotive components and systems. For example, Gaussian Process Regression (GPR) has emerged as a powerful tool in recent years for building a static ROM as an alternative to a conventional parametric model or a multi-dimensional look-up table. GPR provides a mathematical framework for probabilistically representing complex non-linear behavior. Today, GPR is available in various programing tools and commercial CAE packages. However, the application of GPR is system dependent and often requires careful design considerations such as selection of input features and specification of kernel functions. Hence there is a need for GPR design optimization driven by application requirements. For example, a moving window size for training must be tuned to balance performance and computational efficiency for tracking changing system behavior.

Accurate determination of driveshaft torque is desired for robust control, calibration, and diagnosis of propulsion system behaviors. The real-time knowledge of driveshaft torque is also valuable for vehicle motion controls. However, online identification of driveshaft torque is difficult during transient drive conditions because of its coupling with vehicle mass, road grade, and drive resistance as well as the presence of numerous noise factors. A physical torque sensor such as a strain-gauge or magneto-elastic type is considered impractical for volume production vehicles because of packaging requirements, unit cost, and manufacturing investment. This paper describes a novel online method, referred to as Virtual Torque Sensor (VTS), for estimating driveshaft torque based on Machine-Learning (ML) approach. VTS maps a signal from Inertial Measurement Unit (IMU) and vehicle speed to driveshaft torque.

Traditionally, the controls system in production vehicles with automatic transmission interprets the driver’s accelerator pedal position as a demand for transmission input torque. However, with the advent of electrified vehicles, where actuators are located at different positions in the drivetrain, and of autonomous vehicles, which are self-driving, it is more convenient to interpret the demand (either human or virtual) in vehicle acceleration or wheel torque domain. To this end, a Wheel Torque-based longitudinal Control (WTC) framework was developed, wherein demands can be converted accurately between the vehicle acceleration or wheel torque domain and the transmission assembly input torque domain.

The Composite Hybrid Automotive Suspension System Innovative Structures (CHASSIS) is a project that developed structural commercial vehicle suspension components in high volume utilising hybrid materials and joining techniques to offer a viable lightweight production alternative to steel. Three components were selected for the project:- Front Subframe Front Lower Control Arm (FLCA) Rear Deadbeam Axle

The previously developed power-based fuel consumption theory for Internal Combustion Engine Vehicles (ICEV) is extended to Battery Electric Vehicles (BEV). The main difference between the BEV model structure and the ICEV is the bi-directional character of traction motors and batteries. A traction motor model was developed as a bi-linear function of positive and negative traction power. Another difference is that the accessories and cabin heating are powered directly from the battery, and not from the powertrain. The resulting unified model for ICEV and BEV energy consumption has linear terms proportional to positive and negative traction power, accessory power, and overhead, in varying proportions. Compared to the ICEV, the BEV powertrain has a high marginal efficiency and low overhead. As a result, BEV energy consumption data under a wide range of driving conditions are mainly proportional to net traction power, with only a small offset.

Metal binderjetting (MBJ) and bound metal deposition (BMD) are high throughput additive manufacturing process that have the potential to meet the needs of automotive volume production. In many cases, these processes require a sintering post-process to meet final dimensions. Because the sintering stage is performed free standing (i.e. without the use of tooling) and can involve up to a 20% dimensional change from green part to the final part shape, part distortion can be a concern. In this study, the sintering stage of a bridge geometry was simulated under different parameter settings using a Finite Element Analysis. The sensitivity of the simulation to various process parameter inputs was examined. Physical parts were then produced in 316L using a bound metal deposition and sintering process and compared to prediction. The sintering simulation indicated good agreement with experiment for some dimensions but highlighted the need for additional analysis.

Laser welding of the magnesium-bearing AA5xxx aluminum alloys is often beset by keyhole instability, especially in the lap through joint configuration. This phenomenon is characterized by periodic collapse of the keyhole leaving large voids in the weld zone. In addition, the top surface can exhibit undercut and roughness. In full penetration welds, keyhole instability can also produce a spikey root and severe top surface concavity. These discontinuities could prevent a weld from achieving engineering specification compliance, pose a craftsmanship concern, or reduce the strength and fatigue performance of the weld. In the case of a full penetration weld, a spikey root could compromise part fit-up and corrosion protection, or damage adjacent sheet metal, wiring, interior components, or trim.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.