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The automotive industry is currently experiencing a massive transformation, one like it has not quite seen in the past. With the advent of highly software-driven, always on, connected vehicles, the automotive industry is experiencing itself at a crossroads. While the traditional component-driven design approach to vehicle development worked in the favor of the industry for decades due to vehicles being mostly mechanical in nature, the industry now finds itself struggling to develop well-integrated vehicle solutions with the large dependency on software systems. The fast-paced nature of the software world makes it imperative to approach the development of automobiles from a Systems Engineering perspective. A function-based approach to the development of vehicle architectures can ensure cohesive systems development and a well-integrated vehicle.

The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and 2026 EPA requirements. The challenge lies in maintaining fuel efficiency and simultaneously reducing the carbon footprint while meeting future emission regulations. Alcohol (primarily methanol, ethanol, and butanol) and ether (dimethyl ether) fuels, owing to their comparable energy density to existing fuels, the comparative ease of handling, renewable production, and suitable emission characteristics may present an attractive drop-in replacement, fully or in part as an additive, to the gasoline/diesel fuels, without extensive modifications to the engine geometry. Additionally, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines.

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.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

The majority of transportation systems have continued to be powered by the internal combustion engine and fossil fuels. Heavy-duty applications especially are reliant on diesel engines for their high brake efficiency, power density, and robustness. Although engineering developments have advanced engines towards significantly fewer emissions and higher efficiency, the use of fossil-derived diesel as fuel sets a fundamental threshold in the achievable total net carbon reduction. Dimethyl ether can be produced from various renewable feedstocks and has a high chemical reactivity making it suitable for heavy-duty applications, namely compression ignition direct injection engines. Literature shows the successful use of DME fuels in diesel engines without significant hardware modifications.

The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles.

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.

Substantial effort has been devoted to utilizing homogeneous charge compression ignition (HCCI) to improve thermal efficiency and reduce emission pollutants in internal combustion engines. However, the uncertainty of ignition timing and limited operational range restrict further adoption for the industry. Using the spark-assisted compression ignition (SACI) technique has the advantage of using a spark event to control the combustion process. This study employs a rapid compression machine to characterize the ignition and combustion process of Dimethyl ether (DME) under engine-like background temperature and pressures and combustion regimes, including HCCI, SACI, and knocking onsite. The spark ignition timing was swept to ignite the mixture under various thermodynamic conditions. This investigation demonstrates the presence of four distinct combustion regimes, including detonation, strong end-gas autoignition, mild end-gas autoignition, and HCCI.

The performance of autonomous vehicle (AV) sensors, such as lidars or cameras, is often hindered during rain. Rain droplets on the AV sensors can cause beam attenuation and backscattering, which in turn causes inaccurate sensor readings and misjudgment by AV algorithms. Most AV systems are equipped with cleaning systems to remove contaminants, such as rain, from AV sensors. One such mechanism is to blow high-speed air over the AV sensors. However, the cleaning air can be hindered by incoming headwind, especially at higher vehicle speeds. An innovative idea proposed here is to use a visor to improve the cleaning performance of AV cleaning systems at higher vehicle speeds. The effectiveness of a baseline visor design was studied using computational fluid dynamics (CFD) air flow analysis and Lagrangian rain droplet tracking. The baseline visor improved the AV sensor cleaning performance in two ways. First, the visor protects the cleaning air flow from being disturbed by headwind.

Common aerodynamic research models have been used in aerodynamic research throughout the years to assist with the development and correlation of new testing and numerical techniques, in addition to being excellent tools for gathering fundamental knowledge about the physics around the vehicle. The generic truck utility (GTU) was introduced by Woodiga et al. [1] in 2020 following successful adoption of the DrivAer (Heft et al. [2]) by the automotive aerodynamics community with the goal to capture the unique flow fields created by pickups and large SUVs. To date, several studies have been presented on the GTU (Howard et. al 2021 [3], Gleason, Eugen 2022 [4]), however, with the increasing prevalence of electric vehicles (EVs), the authors have created additional GTU configurations to emulate an EV-style underbody for the GTU.

This paper presents a mechanical energy control volume analysis for incompressible flow around road vehicles using results from Detached Eddy Simulation Computational Fluid Dynamics calculations. The control volume approach equates the rate of work done by surface forces of the vehicle to (i) the rate of work and kinetic energy flux at the control volume boundaries (particularly in the vehicle wake) and (ii) the rate of energy loss in the domain. At the downstream control volume boundary, the wake terms can be divided into lift-induced and profile drag terms. The rate of energy loss in the domain can be used as a volumetric analog for drag (drag counts/m3, when normalized). This allows for a quantitative break down of the contributions of different flow features/regions to the overall drag force.

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 order to improve the fuel economy for future high-efficiency spark ignition engines, the applications of advanced combustion strategies are considered to be beneficial with an overall lean and/or exhaust gas recirculation diluted cylinder charge. Stronger and more reliable ignition sources become more favorable under extreme lean/EGR conditions. Existing research indicates that the frequency of plasma restrikes increases with increased flow velocity and decreased discharge current level, and a higher discharge current can reduce the gap resistance and maintain the stretched plasma for a longer duration under flow conditions. An in-house developed current boost control system provides flexible control of the discharge current level and discharge duration. The current boost ignition system is based on a multi-coil system with a discharge current level of 180mA.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

The rise of greenhouse gas emissions has reached historic levels, with 37 billion tons of CO2 released into the atmosphere in 2018 alone. In the European Union, 32% of these emissions come from transportation, with 73.3% of that percentage coming from vehicles. To address this problem, solutions such as cleaner fuels and more efficient engines are necessary. Artificial Intelligence can also play a crucial role in climate analysis and verification to move towards a more sustainable future. By utilizing connected vehicle data, automakers can analyze real-time vehicle performance data to identify opportunities for improvement and reduce carbon emissions. This approach benefits the environment, improves vehicle quality, and reduces engineering work time, making it a win-win solution. Connected vehicle data offers a wealth of information on vehicle performance, such as fuel consumption and carbon emissions.

Compression ignition engines used in heavy-duty applications are typically powered by diesel fuel. The high energy density and feedstock abundance provide a continuing source for the immense energy demand. However, the heavy-duty transportation sector is challenged with lowering greenhouse gas and combustion by-product emissions, including carbon dioxide, nitrogen oxides, and particulate matter. The continuing development of engine management and combustion strategies has proven the ability to meet current regulations, particularly with higher fuel injection pressure. Nonetheless, a transition from diesel to a renewable alternative fuel source will play a significant role in reducing greenhouse gases while maintaining the convenience and energy density inherent in liquid fuels. Dimethyl ether is a versatile fuel that possesses combustion properties suitable for compression ignition engines and physical properties helpful for clean combustion.

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.

In the present work, a practical semi-detailed soot model has been integrated with a recently developed turbulent premixed combustion model and an improved TRF (toluene reference fuel) chemical kinetic mechanism. The practical semi-detailed soot model includes a reduced PAH (polycyclic aromatic hydrocarbon) sub-mechanism, soot particle inception (or nucleation) through pyrene (A4), C2H2-assisted and PAH-assisted surface growth, soot coagulation, and soot oxidation by both O2 and OH. In the TRF mechanism recently improved by the author, eight dominant reactions for high-temperature operating conditions (T > 750 K) were identified and corrected. The turbulent premixed combustion model recently developed by the author includes a mechanism-dynamic-selection sub-model and a dynamic turbulent diffusivity sub-model in which Schmidt number is constructed as a function of local turbulence/thermodynamics conditions.

In the present work, five surrogate components (n-Hexadecane, n-Tetradecane, Heptamethylnonane, Decalin, 1-Methylnaphthalene) are proposed to represent liquid phase of diesel fuel, and another different five surrogate components (n-Decane, n-Heptane, iso-Octane, MCH (methylcyclohexane), Toluene) are proposed to represent vapor phase of diesel fuel. For the vapor phase, a 5-component surrogate chemical kinetic mechanism has been developed and validated. In the mechanism, a recently updated H2/O2/CO/C1 detailed sub-mechanism is adopted for accurately predicting the laminar flame speeds over a wide range of operating conditions, also a recently updated C2-C3 detailed sub-mechanism is used due to its potential benefit on accurate flame propagation simulation. For each of the five diesel vapor surrogate components, a skeletal sub-mechanism, which determines the simulation of ignition delay times, is constructed for species C4-Cn.