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With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 – BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world.

Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries.

DHT hybrid transmission assembly control system discussed in this paper includes hydraulic control, hybrid mode switching control, shift control, dual motor control, clutch and motor thermal management. The hybrid mode is divided into four modes: the EV mode, the serial mode, the parallel mode and the launch mode. Hydraulic control includes torque-pressure conversion, clutch pressure kiss point adaption, clutch oil filling time adaption. Shift control includes shift type decision, shift sequence control, shift inertia process based on motor intervention. Thermal management includes clutch flow and motor flow distribution. Motor control include the current control, mode control and boost strategy of permanent magnet synchronous motor in dual hybrid system, which has good stability and robustness. Motor mode includes initialization mode, normal mode, fault mode, active discharge mode, power off mode.

The growing demand for transportation fuels and the global emphasis on reducing greenhouse gas (GHG) emissions have led to increased interest in analyzing transport GHG emissions from the life-cycle perspective. Methanol, a potentially carbon-neutral fuel synthesized from CO2 and H2, has emerged as a promising candidate. This paper conducts a comprehensive life-cycle analysis (LCA) of the GHG emissions associated with the methanol production process, utilizing data inventory from China in 2019. To simulate the synthesis and distillation process of methanol, Aspen Plus is employed, using parameters obtained from actual plants. GHG emissions are then calculated using the GREET model, incorporating updated industry statistics and research findings. The CO2 necessary for methanol production is captured from factory flue gas.

It is estimated that the share of autonomous vehicles in the market will reach an important point between 2050s and 2060s. Some major benefits of autonomy in ground vehicles can be regarded as reducing traffic, saving fuel and reducing emissions. Accordingly, it is anticipated that autonomous vehicles (AVs) will prevent driver error from happening, which is the primary cause of 90% of traffic accidents. However, it is a prerequisite that the AVs are accepted by the public, and be used regularly in daily life. AVs obliges everyone to be a passenger, thereby occupants will lose authority on the vehicle and have to deal with non-driving tasks during an automated ride. This will increase the lack of situational awareness, leading occupants to be more sensitive to motion sickness, where the major reasons of motion sickness are conflict between vestibular and visual senses, lack of control, unable to predict the direction of movement.

Ethanol fuel blends with gasoline for spark ignition (SI) internal combustion engines are widely used on account of their advantages in terms of fuel economy and emissions reduction potential. The focus of this paper is to study the effects of these blends on combustion characteristics such as in-cylinder pressure profiles, gas-phase emissions (e.g., unburned hydrocarbons, NOx) and particulates (e.g., particulate matter and particle number) using both measurement campaigns and digital engineering workflows. Nineteen load-speed operating points in a 1L 3-cylinder GDI SI engine were measured and modelled. The measurements for in-cylinder pressure and emissions were repeated at each operating point for three types of fuel: gasoline (E0, 0% by volume of ethanol blend), E10 (10 % by volume of ethanol blend) and E20 (20% by volume of ethanol blend).

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Gasoline Direct Injection (GDI) vehicles now make up the majority of European new car sales and a significant share of the existing car parc. Despite delivering measurable engine efficiency benefits, GDI fuel systems are not without issues. Fuel injectors are susceptible to the formation of deposits in and around the injector nozzles holes. It is widely reported that these deposits can affect engine performance and that different fuels can alleviate the buildup of those deposits. This project aims to understand the underlying mechanisms of how deposit formation ultimately leads to a reduction in vehicle performance. Ten GDI fuel injectors, with differing levels of coking were taken from engine testing and consumer vehicles and compared using a range of imaging and engine tests. At the time of writing, a new GDI engine test is being developed by the Co-ordinating European Council (CEC) to be used by the fuel and fuel additive industry.

The vehicles with level-5 autonomy (L5AVs) that have no human driver in the loop are also known as self-driving cars. L5AVs are assumed the next generation of ground transportation, which have growing attention from both industry and academia in most recent years. Most of the work related to feedback strategies of L5AVs are on developing mapping systems through a variety of sensors. These systems can be considered as an analogue to the perception and central nervous system of human drivers. For instance, innovative visualization systems are more powerful when compared to the visual perception system of a person, yet, mapping demands high computation loads. This burden causes delay in the feedback loop and thus, it might have an unfavorable influence on proper and safe control action. This study investigates the effect of time delay occurring in mapping systems on the stability of the controlled vehicle.

The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

Ambient temperature has significant impact on engine start ability and cold start emissions from diesel engines. These cold start emissions are accounted for substantial amount of the overall regulatory driving cycle emissions like NEDC or FTP. It is likely to implement the low temperature emissions tests for diesel vehicles, which is currently applicable only for gasoline vehicles. This paper investigates the potential of the intake heating strategy on reducing the driving cycle emissions from the latest generation of turbocharged common rail direct injection diesel engines at low ambient temperature conditions. For this investigation an air heater was installed upstream of the intake manifold and New European Driving Cycle (NEDC) tests were conducted at -7°C ambient temperature conditions for the different intake air temperatures. Intake air heating reduced the cranking time and improved the fuel economy at low ambient temperatures.

Diesel engines are the versatile power source and is widely used in passenger car and commercial vehicle applications. Environmental temperature conditions, fuel quality, fuel injection strategies and lubricant have influence on cold start performance of the diesel engines. Strategies to overcome the cold start problem at very low ambient temperature include preheating of intake air, coolant, cylinder block. The present research work investigates the effect of coolant temperatures on passenger car diesel engine’s performance and exhaust emission characteristics during the cold start at cold ambient temperature conditions. The engine is soaked in the -7°C environment for 6 hours. The engine coolant is preheated to the desired coolant temperatures of 10 and 20°C by an external heater and the start ability tests were performed.

Gasoline direct injection (GDI) engines have become very attractive in transportation due to several benefits over preceding engine technologies. However, GDI engines are associated with higher levels of particulate matter (PM) emissions, which is a major concern for human health. The aim of this work is to broaden the understanding of the effect of hydrogen combustion and the influence of the three way catalytic converter (TWC) on PM emission characteristics. The presence of hydrogen in GDI engines has been reported to reduce fuel consumption and improve the combustion process, making it possible to induce higher rates of EGR. A prototype exhaust fuel reformer build for on-board vehicle hydrogen-rich gas (reformate) production has been integrated within the engine operation and studied in this work.

In recent years magnetic resonance imaging (MRI) has been shown to be an attractive method for fluid flow visualization. In this work, we show how MRI velocimetry techniques can be used to non-invasively investigate and visualize the hydrodynamics of exhaust gas in a diesel particulate filter (DPF), both when clean and after loading with diesel engine exhaust particulate matter. The measurements have been used to directly measure the gas flow in the inlet and outlet channels of the DPF, both axial profiles along the length and profiles across the channel diameter. Further, from this information we show that it is possible to indirectly ascertain the superficial wall-flow gas velocity and the soot loading profiles along the filter channel length.

Realistically experiencing the sound and vibration data through actually listening to and feeling the data in a full-vehicle NVH simulator remarkably aids the understanding of the NVH phenomena and speeds up the decision-making process. In the case of idle vibration, the sound and vibration of the idle condition are perceived simultaneously, and both need to be accurately reproduced simultaneously in a simulated environment in order to be properly evaluated and understood. In this work, a case is examined in which a perceived idle quality of a vehicle is addressed. In this case, two very similar vehicles, with the same powertrain but somewhat different body structures, are compared. One has a lower subjective idle quality rating than the other, despite the vehicles being so similar.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

Lightweighting of vehicle panels enclosing vehicle cabin causes NVH degradation since engine, road, and wind noise acoustic sources propagate to the vehicle interior through these panels. In order to reduce this NVH degradation, there is a need to develop new NVH sound package materials and designs for use in lightweight vehicle design. Statistical Energy Analysis (SEA) model can be an effective CAE design tool to develop NVH sound packages for use in lightweight vehicle design. Using SEA can help engineers recover the NVH deficiency created due to sheet metal lightweighting actions. Full vehicle SEA model was developed to evaluate the high frequency NVH performance of “Vehicle A” in the frequency range from 200 Hz to 10 kHz. This correlated SEA model was used for the vehicle sound package optimization studies. Full vehicle level NVH laboratory tests for engine and tire patch noise reduction were also conducted to demonstrate the performance of sound package designs on “Vehicle A”.

Hands-free phone use is the most utilized use case for vehicles equipped with infotainment systems with external microphones that support connection to phones and implement speech recognition. Critically then, achieving hands-free phone call quality in a vehicle is problematic due to the extremely noisy nature of the vehicle environment. Noise generated by wind, mechanical and structural, tire to road, passengers, engine/exhaust, HVAC air pressure and flow are all significant contributors and sources of noise. Other factors influencing the quality of the phone call include microphone placement, cabin acoustics, seat position of the talker, noise reduction of the hands-free system, etc. This paper describes the work done to develop procedures and metrics to quantify the effects that influence the hands-free phone call quality.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

As the world's largest auto producer and consumer, China is both the most promising and complex market given the country's rapid economic growth, huge population, and many regional and segment preference differences. This research is aimed at developing data-driven demand models for customer preference analysis and prediction under a competitive market environment. Regional analysis is first used to understand the impact of geographical factors on customer preference. After a comprehensive data exploration, a customer-level mixed logit model is built to shed light on fast-growing vehicle segments in the Chinese auto market. By combining the data of vehicle purchase, consideration, and past choice, cross-shopping behaviors and brand influence are explicitly modeled in addition to the impact of customer demographics, usage behaviors, and attributes of vehicles.