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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.

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.

Accurate information about tire cornering stiffness is essential for the implementation of advanced vehicular control systems. Data-driven modelling method leverages the availability of high-quality measurement data alone, without vehicle parameters, which provides a tutorial to reconstruct the system dynamics and estimate tire cornering stiffness. As such, we collect the states and inputs of the vehicle to build its state space using the dynamic mode decomposition (DMD) method. Then, based on the entries of the system and input matrix, the tire cornering stiffness can be further identified by solving the linear equations via orthogonal regression with considering the measurement noise. The sufficient and necessary rank condition for the DMD execution is also analyzed. Additionally, we introduce two alternative ways to update the system and input matrices - recursive least squares (RLS) and sliding window (SW).

Vehicle speeds, in both longitudinal and lateral directions, are vital signals for vehicular electronic control systems. In in-wheel motor-driven vehicles (IMDVs), because no slave wheel can be used for reference, it becomes more challenging to conduct velocity estimation, especially when all wheels turn to slip. To reduce the dependence of speed estimation on physical plant parameters and environment perception, in this work, we develop a new method that estimates the longitudinal and lateral velocities of an IMDV by using the kinematic model with the Kalman Filter. For longitudinal velocity measurement, we propose a hybrid approach combining Long-Short Term Memory (LSTM) networks and the kinematic rules to obtain a reliable estimation. More specifically, when at least one effective driven wheel is available, that is, no-slip happening, the longitudinal velocity can be derived using the average of those effective wheels' rotational speeds.

The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety. Human-like Decision-making and Control for Automated Driving takes a step forward to address breaking through the limitation of future automation applications, investigating in depth: Human driving feature modeling and analysis Personalized motion control for AVs Human-like decision making for AVs Click here to access the full SAE EDGETM Research Report portfolio.

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).

A heavy-duty diesel engine (ETH-LAV single cylinder MTU396 heavy duty research engine) was simulated by RANS and advanced reacting flow models to gain insight into its soot and NOx emissions. Due to symmetry, a section of the engine containing a single injector-hole was simulated. Dodecane was used as a surrogate to emulate the evaporation properties of diesel and a 22-step reaction mechanism for n-heptane was used to describe combustion. The Conditional Moment Closure (CMC) method was used as the combustion model in two ways. In a more conventional modelling approach, CMC was fully interfaced with the CFD and a two-equation model was employed for determining soot while the extended Zeldovich mechanism was used for NOx. In a second approach called the Imperfectly Stirred Reactor (ISR) method, the CMC equation was integrated over space and the previous RANS-CMC solution was further analysed in a post-processing step with the focus on soot.

Thermoplastics find application in many automotive components. Off late, hardware testing is supplemented by analysis using finite element (FE) codes. One of the factors determining the analysis accuracy is the representation of the components with suitable material models. While a uniaxial tensile test on the specimens typically provides engineering stress-strain data, material plasticity models in commercial FE solvers, such as LS-DYNA and ABAQUS, require equivalent plastic strain versus true stress as input. Engineering stress and strain can be converted to the corresponding true stress and true strain using equations based on the constant volume assumption; however, these equations are valid only up to the point of necking.

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.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

A powerful and efficient turbocharger turbine benefits the engine in many aspects, such as better transient response, lower NOx emissions and better fuel economy. The turbine performance can be further improved by employing secondary flow injection through an injector over the shroud section. A secondary flow injection system can be integrated with a conventional turbine without affecting its original design parameters, including the rotor, volute, and back disk. In this study, a secondary flow injection system has been developed to fit for an asymmetric twin-scroll turbocharger turbine, which was designed for a 6-cylinder heavy-duty diesel engine, aiming at improving the vehicle’s performance at 1100 rpm under full-loading conditions. The shape of the flow injector is similar to a single-entry volute but can produce the flow angle in both circumferential and meridional directions when the flow leaves the injector and enters the shroud cavity.

This paper investigates the energy efficiency and emissions benefits possible with connected and autonomous vehicles (CAVs). Such benefits could be instrumental in decarbonising the transport sector. The impact of CAV technology on operation, usage and specification of vehicles for optimised energy efficiency is considered. Energy consumption reductions of 55% – 66% are identified for a fully autonomous road transport system versus the present. 46% is possible for a CAV on today’s roads. Smoothing effects and reduced stoppage in the drive cycle achieve a 31% reduction in travel time if speed limits are not reduced. CAV powertrain optimised for different scenarios requires just 10 kW – 40 kW maximum power whilst the vehicle mass is reduced by up to 40% relative to current cars. Urban-optimised powertrain, with only 10 kW – 15 kW maximum power, allows energy consumption reductions of over 71%.

Engines with reduced emissions and improved efficiency are of high interest for road transport. However, achieving these two goals is challenging and various concepts such as PFI/DI/HCCI/PCCI are explored by engine manufacturers. The computational fluid dynamics is becoming an integral part of modern engine development programme because this method provides access to in-cylinder flow and thermo-chemical processes to develop a closer understanding to tailor tumble and swirling motions to construct green engines. The combustion modelling, its accuracy and robustness play a vital role in this. Out of many modelling methods proposed in the past flamelet based methods are quite attractive for SI engine application. In this study, FlaRe (Flamelets revised for physical consistencies) approach is used to simulate premixed combustion inside a gasoline PFI single-cylinder, four-stroke SI engine. This approach includes a parameter representing the effects of flame curvature on the burning rate.

Despite the increasing use of carbon fibre reinforced plastic (CFRP) composites, titanium and aluminium alloys still constitute a significant proportion of modern civil aircraft structures, which are primarily assembled via mechanical joining techniques. Drilling of fastening holes is therefore a critical operation, which has to meet stringent geometric tolerance and integrity criteria. The paper details the development of a three-dimensional (3D) finite element (FE) model for drilling aerospace grade aluminium (AA7010-T7451 and AA2024-T351) and titanium (Ti-6Al-4V) alloys. The FE simulation employed a Coupled Eulerian Lagrangian (CEL) technique. The cutting tool was modelled according to a Lagrangian formulation in which the mesh follows the material displacement while the workpiece was represented by a non-translating and material deformation independent Eulerian mesh.

The underbody of a truck is responsible for an appreciable portion of the vehicle’s aerodynamic drag, and thus its fuel consumption. A better understanding of the underbody aerodynamics could lead to designs that are more environmentally friendly. Unfortunately there are difficulties with correctly replicating the ground condition and rotating wheels when using the classical approach of a wind-tunnel for aerodynamic investigation. This in turn leads to computational modelling problems. A lack of experimental data for Computational Fluid Dynamics (CFD) validation means that the flow field in this area has seldom been investigated. There is thus very little information available for the optimisation and design of underbody aerodynamic devices. This paper investigates the use of a water-towing tank, which allows the establishment of the correct near-ground flow while permitting good optical access. Using a 1/10 scale model, Reynolds Numbers of around 0.7 million are achieved.

The underbody of a truck is responsible for an appreciable portion of the vehicle’s aerodynamic drag, and thus its fuel consumption. This paper investigates experimentally the flow around side-skirts, a common underbody aerodynamic device which is known to be effective at reducing vehicle drag. A full, 1/10 scale European truck model is used. The chassis of the model is designed to represent one that would be found on a typical trailer, and is fully reconfigurable. Testing is carried out in a water towing tank, which allows the correct establishment of the ground flow and rotating wheels. Optical access into the underbody is possible through the clear working section of the facility. Stereoscopic and planar Particle Image Velocimetry (PIV) set-ups are used to provide both qualitative images of and quantitative information on the flow field.

In this study, the internal nozzle flow and macroscopic spray characteristics of a kind of wide distillation fuel (WDF) - kerosene were investigated both with numerical and experimental approaches. Simulation results indicate that compared with diesel fuel, kerosene cavitates more due to higher turbulent kinetic energy as a result of lower viscosity. The results from experiment indicate that under lower charge density, the spray penetration for kerosene is obviously shorter than that for diesel, especially for the lower injection pressure. This is because lower fuel viscosity results in a reduction in the size of the spray droplets, leading to lower momentum. However the spray angle of kerosene is larger compared with diesel due to stronger turbulence in the nozzle flow caused by increased cavitation for kerosene, which also accords well with the simulation results.

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.

Three-dimensional Computational Fluid Dynamics (CFD) has become an integral part in analysing engine in-cylinder processes since it provides detailed information on the flow and combustion, which helps to find design improvements during the development of modern engine concepts. The predictive capability of simulation tools depends largely on the accuracy, fidelity and robustness of the various models used, in particular concerning turbulence and combustion. In this study, a flamelet model with a physics based closure for the progress variable dissipation rate is applied for the first time to a spark ignited IC engine. The predictive capabilities of the proposed approach are studied for one operating condition of a gasoline port fuel injected single-cylinder, four-stroke spark ignited full-metal engine running at 3,500 RPM close to full load (10 bar BMEP) at stoichiometric conditions.