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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.

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.

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.

Disc brake squeal is always a challenging multidisciplinary problem in vehicle noise, vibration, and harshness (NVH) that has been extensively researched. Theoretical analysis has been done to understand the mechanism of disc brake squeal due to small disturbances. Most studies have used linear modal approaches for the harmonic vibration of large models. However, time-domain approaches have been limited, as they are restricted to specific friction models and vibration patterns and are computationally expensive. This research aims to use a time-domain approach to improve the modeling of brake squeal, as it is a dynamic instability issue with a time-dependent friction force. The time-domain approach has been successfully demonstrated through examples and data.

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.

Currently the automotive industry has been under extremely important technological changes. Part of these changes are related to the way that users interact with the vehicle and fundamental components are the new digital cluster and screens. These devices have created a disruption in the way information is transmitted to the user, being essential for vehicle operation, including safety. Due to new operating conditions, multiple evaluations need to be performed, one of them is the solar temperature Load to ensure correct operation without compromising user safety. This test is required to identify the thermal performance on the screens mounted on the instrument panel. The performance identification is performed on both sides, analytical and physical. In regards finite element simulation it represents the solar chamber as the main source of heat and being the main mechanism of transmission the radiation.

The steering system is to provide the driver with the possibility of lateral vehicle guidance, i.e. to influence the lateral dynamics of the vehicle; moreover, it is crucial to promptly translate the steering input to have the vehicle in high-quality directional stability. An electrical power assisted steering (EPAS) system is the sophisticated variant to meet higher requirements for vehicle safety, ride comfort, and driver-assist. This research is to investigate if a CAE methodology could be innovated to better simulate the durability of a steering system under various working scenarios; figure out the critical features of the modeling; conduct a correct analysis procedure for validating the modeling and collecting data for evaluation. With step by step in modeling and analysis, a well-established example of CAE model of EPAS is enabled to highlight the novelty of steering vehicle level CAE methodology and therefore achieve the research goal.

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.

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.

This paper presents a method of using CAE to determine the pre-load and torque applied to a U-Bolt rear Spring Seat. In this paper it is review two U-bolt design and the stresses generated by the pre-load torque applied, based in this study a process to determine the minimal preload and the torque is discussed. By this process it is possible to determine the minimum Torque and the correct pre-load in the U-Bolt element and assuring the correct fastening of the components avoiding over stress in the Bar elements.

Noise and vibration evaluation of driveline clunk can be challenging as it is the result of driver input conditions and is transient in nature. As with many noise and vibration challenges, the use of computer-aided engineering (CAE) simulation is useful as it allows for detailed study of the phenomenon and prediction of potential improvements. A hybrid approach of physical test-based measurements and CAE analysis can be used to leverage the advantages of CAE in a comprehensive evaluation including the total vehicle noise, vibration, and harshness (NVH) performance. In this paper we present work performed to facilitate engineering evaluations of driveline clunk using both measured test and CAE simulation data. We explain how we used measured test data to inform the CAE analysis, how the simulation approached modelling of the transient clunk event, and how the measured data was used to provide contextual sound for realistic evaluation of the CAE output as heard by the passengers.

As the automotive industry strives for an increased fuel economy, lightweighting is a key factor and can be realized through composite materials. Composites have better strength-to-weight ratio as compared to metals. In this paper, static and fatigue analysis is performed on an oil pan made of polyamide-6,6 and 50% glass fiber (PA66-GF50). PA66 has a glass transition temperature of 170°C; therefore, it is suitable for automotive applications where the operating range is −40°C to 150°C. Long glass fiber (LGF) composite has an aspect ratio of 30-50 in the oil pan. Fibers break in the molding process but are still considerably longer than with conventionally compounded short glass fiber (SGF) composite, where the aspect ratio of fiber is between 10 and 20. However, the computer-aided engineering (CAE) procedure for life prediction of short glass fiber-reinforced (SGFR) plastic versus LGF-reinforced plastic is the same.

A cast magnesium AE44 subframe was designed and manufactured for a C Class sedan to reduce weight and improve vehicle fuel economy. Corrosion mitigation strategies were developed to reduce the likelihood of galvanic corrosion. Both a proving ground vehicle corrosion test and a laboratory component corrosion test were conducted. The vehicle test result demonstrated that the corrosion mitigation strategies were effective. They also provided lessons learned on clearance between magnesium and steel components and options to improve the subframe’s corrosion resistance. The magnesium subframe achieved 5 kg (32%) weight reduction from the equivalent steel subframe and met all the required structural performance targets.

A cast magnesium subframe was designed and manufactured for a C Class sedan to reduce weight and improve vehicle fuel economy. The magnesium subframe achieved 5 kg (32%) weight reduction from the equivalent steel subframe and met all the required structural performance targets. All the joints of the magnesium subframe were tested for bolt load retention. The tests were conducted with a temperature profile of 100°C to -30°C designed to investigate the creep behavior of the selected magnesium alloy AE44 under high stress.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

A torque converter is a type of fluid coupling device used to transfer engine power to the gearbox and driveline. A bypass clutch equipped in a torque converter assembly is a friction element which when fully engaged, can directly connect the engine to the gearbox. The torque converter is an important launch device in an automatic transmission which decouples engine speed from gearbox input speed while providing torque multiplication to drive the vehicle. During partial pedal launch, it is desired to engage the bypass clutch early and reduce the converter slippage in order to reduce power loss and achieve better fuel economy. However, engaging the bypass clutch early and aggressively may disturb the wheel torque and cause unpleasant driving experiences. This paper describes a multi-input multi-output (MIMO) control method to coordinate both engine and converter bypass clutch to simultaneously deliver desired wheel torque and reduce converter slippage.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

There are a variety of test protocols associated with vehicle fuel economy and emissions testing. As a result, a number of test protocols currently exist to measure axle efficiency and spin loss. The intent of this technical paper is to describe a methodology that uses a singular axle efficiency and spin loss procedure. The data can then be used to predict the effects on vehicle FE and GHG for a specific class of vehicles via simulation. An accelerated break-in method using a comparable energy approach has been developed, and can be used to meet the break-in requirements of different vehicle emission test protocols. A “float to equilibrium” sump temperature approach has been used to produce instantaneous efficiency data, which can be used to more accurately predict vehicle FE and GHG, inclusive of Cold CO2. The “Float to Equilibrium” approach and “Fixed Sump Temperature” approach has been compared and discussed.