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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).

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.

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.

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.

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.

Wide Distillation Fuel (WDF) refers to the fuels with a distillation range from Initial Boiling Point (IBP) of gasoline to Final Boiling Point (FBP) of diesel. Polyoxymethylene Dimethyl Ethers (PODEn) have high oxygen content and cetane number, are promising green additive to diesel fuel. In this paper, WDF was prepared by blending diesel and gasoline at ratio of 1:1, by volume; the mass distribution of oligomers in the PODE3-4 product was 88.9% of PODE3 and 8.46% of PODE4. Diesel fuel (Diesel), WDF (G50D50) and WDF (80%)-PODE3-4 (20%) (G40D40P20) were tested in a light-duty single-cylinder diesel engine, combustion characteristic, fuel consumption and exhaust emissions were measured. The results showed that: at idling condition, G40D40P20 has better combustion stability, higher heat release rate, higher thermal efficiency compared with G50D50.

There is increasing demand for engine diagnostic and control with in-cylinder pressure signal. However, the application of cylinder pressure sensors are restricted by the high cost of the sensor. Another possible way for engine combustion state estimation is by processing of instantaneous crankshaft speed signal, but it is limited by the precision and complexity of the algorithm. It could be a solution by processing one cylinder pressure signal in combination with a crankshaft speed signal. The indicated torque could be estimated through engine speed processing and also from the measure cylinder pressure for the reference cylinder. Measurement results from experiments show that the indicated torque error traces of different cylinder are similar in shape. According to this assumption, the reference cylinder with cylinder pressure signal available can serve as both a parameter calibration information source and an error reduction measure.

This paper presents a statistical characterisation of the effects of variations in vehicle parameters on vehicle road load data using a quarter vehicle as a case study. A model of a quarter vehicle test rig constructed from a commercial SUV is created in a multi-body dynamics (MBD) simulation environment to reproduce the real-life behaviour of the SUV. The model is thereafter validated by correlating the response data collected from both the model and laboratory test rig to the same road input. In order to ensure that only the effects of the variation of the vehicle parameters are captured, a time domain drive signal for a kerb strike road event on the physical vehicle is generated from the proving ground data collected during durability testing of the vehicle.

The development of intelligent tire technology from concept to application covers multi-disciplinary fields. During the course of development, the computational method can play a significant role in understanding tire behavior, assisting in the design of the intelligent tire prototype system and in developing tire parameters estimation algorithm, etc. In this paper, a finite element tire model was adopted for developing a strain-based intelligent tire system. The finite element tire model was created considering the tire's composite structure and nonlinear properties of its constituent materials, and the FE model was also validated by physical tests. The FE model is used to study tire strain characteristics by steady state simulation for straight line rolling, traction and braking, as well as cornering. Tire loading conditions were estimated by feature extraction and data fitting.

Upcoming stringent regulations on emissions and fuel efficiency are driving the automotive industry towards lightweight vehicle design. Thus, a higher share of carbon fiber composite materials in vehicle structures is expected. Current literature addresses development processes of composite components under a limited scope, however the considerations of design parameters used in these studies are inadequate for the realistic development of a full vehicle structure, especially in a resource-constrained development project. In addition, existing vehicle structure design philosophies applied for metal structures cannot be directly ported over for composite design due to differences in material properties, failure modes and design for manufacturing limitations.

A study of Multiple Premixed Compression Ignition (MPCI) with mixtures of gasoline and diesel is performed on a light-duty single cylinder diesel engine. The engine is operated at a speed of 1600rpm with the same fuel mass per cycle. By keeping the same intake pressure and EGR ratio, the influence of different blending ratios in gasoline and diesel mixtures (90vol%, 80vol% and 70vol% gasoline) is investigated. Combustion and emission characteristics are compared by sweeping the first (−95 ∼ −35deg ATDC) and the second injection timing (−1 ∼ 9deg ATDC) with an injection split ratio of 80/20 and an injection pressure of 80MPa. The results show that compared with diesel combustion, the gasoline and diesel mixtures can reduce NOx and soot emissions simultaneously while maintaining or achieving even higher indicated thermal efficiency, but the HC and CO emissions are high for the mixtures.