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Air pollution from internal combustion engines poses a significant apprehension for both global warming and public health on a worldwide scale. The adoption of hybridization and electrification within the vehicular fleet can help to tackle these challenges. This study evaluates a waste heat recovery system for electric power generation, based on the Inverted Brayton Cycle (IBC) coupled with a high-performance gasoline engine. The Mercedes-Benz CLS 350 CGI engine platform was modelled in AVL Boost software and validated against the reference published experimental data. The engine model was then modified to incorporate the IBC to study the performance of the proposed hybrid propulsion system. The IBC power output was calculated at a wide range of engine speed and load, and results showed that up to 18 kW of extra power output can be generated by the IBC system.

Li-ion batteries (LIBs) optimum performance and lifetime depend on temperature, with the commonly suggested operating temperature being in the range of 25 to 40 °C. It's also crucial to keep the temperature difference between battery cells below 5°C. Operation at different temperature ranges can adversely affect or degrade the performance and lifetime of LIBs. A battery thermal management system (BTMS) is essential for keeping the battery temperature within the optimum range. This paper aims to develop and analyze the BTMS for an electric heavy-duty truck. To achieve this aim, battery cells and modules are modelled in ANSYS Fluent software. Validation with experimental results and mesh sensitivity studies are also performed to increase confidence in simulation data. The model is then analyzed for a specific cooling systems to investigate its effect on battery thermal performance during the operation.

The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

Operating condition of rotor embedded magnet materials for permanent magnet synchronous motor (PMSM) critically affect electric vehicle (EV) range and dynamic characteristics. The rotor liquid cooling technique has a deep influence on PMSM performance improvement, and begin to be studied and applied increasingly in EV field. Here, the fluid, thermal, and electromagnetic characteristics of motor with and without hollow-shaft cooling are researched comprehensively based on 100 kW PMSM with housing water jacket (HWJ) and hollow-shaft rotor water jacket (SWJ). The solid models are constructed considering temperature-dependent power loss and anisotropic thermal conductivity. After the fluid models are set up by using Reynolds stress model (RSM), conjugate heat transfer is conducted through computational fluid dynamics (CFD) simulation, and is verified by real PMSM test bench experiments.

To reduce the energy consumption level of electric vehicles, the working range of the regenerative braking system will gradually expand to the high state of charge of the battery. The time delay in the control signal transmission path of the high state of charge regenerative braking control process will affect the regenerative braking. At the same time, regenerative braking under a high state of charge puts forward higher requirements for the control accuracy of regenerative current. In the research of this paper, the motor model, battery model, and vehicle dynamics model are firstly established by using MATLAB/Simulink, and the dynamic relationship between regenerative current and regenerative braking torque is analyzed at the same time. Considering the system time delay, this paper proposes a high-charge regenerative braking control strategy (SPPC) that combines Smith prediction and prescribed performance control.

Reducing exhaust emissions has been a major focus of research for a number of years since internal combustion engines (ICE) contribute to a large number of harmful particles entering the environment. As a way of reducing emissions and helping to tackle climate change, many countries are announcing that they will ban the sale of new ICE vehicles soon. Electrical vehicles (EVs) represent a popular alternative vehicle propulsion system. However, although they produce zero exhaust emissions, there is still concern regarding non-exhaust emission, such as brake dust, which can potentially cause harm to human health and the environment. Despite EVs primarily using regenerative braking, they still require friction brakes as a backup as and when required. Moreover, most EVs continue to use the traditional grey cast iron (GCI) brake rotor, which is heavy and prone to corrosion, potentially exacerbating brake wear emissions.

Electrification is one of the main solutions for the decarbonization of the transport system. It is employed widely by the automotive industry in light- and medium-duty vehicles and recently started to be considered in heavy-duty applications. However, powertrain electrification of heavy-duty vehicles, especially for Class 8 trucks, is very challenging. In this study, the battery-electric powertrain energy and technical performance of a DAF 44 tones truck are compared for two different electric drives. The case study truck is modeled in AVL CRUISE M software and the battery electric powertrain is evaluated for long haul driving cycle. The minimum number of battery packs is determined by defining the lowest energy consumption of the powertrain designed for the proposed drive cycle. Also, a transient analysis is accomplished to investigate the impact of various electric drives on energy consumption and performance of the proposed electric powertrain.

While the shift to vehicle electrification plays a pivotal role in governments’ targets towards carbon neutrality, there exists certain technical challenges that need to be addressed. The motorsport car industry is also affected by this policy with the electric cars being included in the formula SAE and formula E competitions as one of the main categories. Moreover, there is a gap in the literature in energy assessment of the electric powertrain used in Formula SAE (FSAE) and Formula Student (FS) cars. In this paper, a Formula Student electric car powertrain was designed as a case study for energy analysis. The proposed electric powertrain is equipped with a four-wheel drive system. The vehicle was modelled in AVL CRUISE M software using technical and measured lab data as input parameters. Simulations were run in a transient driving cycle for a real circuit layout used in previous SAE competitions.

Gasoline-related factors that affect low-speed pre-ignition (LSPI) include the distillation properties of gasoline, manganese (Mn), ethanol, diesel fuel, detergent for aftermarket, and iron (Fe). The combined effect of Mn with ethanol or high calcium engine oil (high-Ca oil) has not been sufficiently clarified. Therefore, appropriate countermeasures for LSPI have not yet been implemented. To clarify the effect of the gasoline properties and additives on LSPI, engine tests were conducted using gasoline with different “PM Index” values, an indicator of distillation properties, different concentrations of Mn, ethanol, diesel fuel, detergent, Fe, and high-Ca oil. The results showed that the LSPI frequency tended to increase with the PM Index, Mn up to 60 ppm, diesel fuel up to 2 vol.%, and detergent up to three times the standard amount.

Small diesel engines are a common primer for micro and mini-grid systems, which can supply affordable electricity to rural and remote areas, especially in developing countries. These diesel generators have no exhaust after-treatment system thus exhaust emissions are high. This paper investigates the potential of introducing simulated synthetic gas (syngas) to diesel in a small diesel engine to explore the opportunities of widening fuel choices and reducing emissions using a 5.7kW single cylinder direct injection diesel generator engine. Three different simulated syngas blends (with varying hydrogen content) were prepared to represent the typical syngas compositions produced from downdraft gasification and were injected into the air inlet. In-cylinder pressure, ignition delay, premixed combustion, combustion stability, specific energy consumption (SEC), and gaseous and particle emissions were measured at various power settings and mixing ratios.

TWC exposure to extreme temperature could result in irreversible damage or thermal failure. Thus, a strategy embedded in the engine control unit (ECU) called catalyst overheating protection (COP) will be activated to prevent TWC overheating. When COP is activated, the command air-fuel ratio will be enriched to cool the catalyst monolith down. Fuel enrichment has been proven a main prerequisite for ammonia formation in hot TWCs as a by-product of NOx reduction. Hence, COP events could theoretically be a source of post-catalyst ammonia from petrol vehicles, but this theory is yet to be confirmed in published literature. This paper validated this hypothesis using a self-programmed chassis-level test. The speed of the test vehicle was set to constant while the TWC temperature was raised stepwise until a COP event was activated.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

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.

The principal objective of the present work is to investigate the fundamental characteristics of a commercially available outwardly opening twin-fluid injector, which utilizes air-assisted atomization principle to attain pulse-type injection of fuel-air mixture. The electromagnetic characteristics of this injector were simulated and the effects of dominating parameters on the electromagnetic force to drive injector were ascertained. On that basis, this paper elaborates on the fundamental characteristics of air-assisted spray using gasoline and kerosene with the employment of two types of optical testing techniques. The spray morphological evolution under varied fuel injection durations and ambient pressures were captured with high-speed shadowgraph thus the corresponding external macroscopic characteristics were obtained and further compared. Spray droplet velocity and diameter at fixed monitoring location were measured by using PDPA (Phase Doppler Particle Analyzer).

Gear rattle noise is one of the important characteristics of manual and dual-clutch transmission，it is generated by the impact of unloaded meshing gear pairs in the transmission due to engine torsional vibration. Based on a front-drive manual transmission and a five dynos drivetrain NVH test bench with high-speed sine wave generator function, this paper designs an experimental program suitable for transmission rattle noise. By driving dynamometer to simulate the torque fluctuation of real engine, the main research is to study the characteristics of the transmission rattle noise under different excitation amplitudes and different excitation frequencies, and the sensitivity of rattle noise under different gears, different oil temperatures, different excitation amplitudes and excitation frequencies is analyzed. Finally, the transmission maps of rattle noise in different gears can be obtained.

Non-exhaust and exhaust particles from traffic were evaluated to account for nearly equal proportions in traffic-related emissions. Among non-exhaust emissions, tyre wear has been a crucial contributor to Particulate matter (PM), with its mass contribution as high as 30% to non-exhaust emissions from traffic. As exhaust emissions control regulation becomes stricter, which leads to a substantial reduction in exhaust emissions from road traffic, currently relative contributions of non-exhaust particles generated from tyre wear to PM is becoming more important. Accordingly, possible regulatory requirement and effectively control strategy of tyre wear particles needs to be developed. This review paper covers the physical properties, chemical composition, emission rates, and mathematic model development of tyre wear particles.

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s.

A Gas to liquid (GTL) fuel was investigated for its combustion and emission performance in an IVECO EURO5 DI diesel engine with a DOC (Diesel Oxidation Catalyst) and DPF (Diesel Particle Filter) installed. The composition of the GTL fuel was analyzed by GC-MS (gas chromatography-mass spectrometry) and showed the carbon distribution of 8-20. Selected physical properties such as density and distillation were measured. The GTL fuel was blended with standard fossil diesel fuel by ratios of diesel/GTL: 100/0, 70/30, 50/50, 30/70 and 0/100. The engine was equipped with a pressure transducer and crank angle encoder in one of its cylinders. The properties of ignition delay and maximum in-cylinder pressure were studied as a function of fraction of the GTL fuel. Particle emissions were measured using DMS500 particle size instrument at both upstream (engine out) and downstream of the DPF (DPF out) for particle number concentrations and size distribution from 5 nm to 1000 nm.

As a new type of energy, free-piston linear generator (FPLG) attracts more research on its stable operation and power performance, while less on its combustion and emission performance. So, in this paper, the emission characteristics of FPLG in two different modes are studied through a port fuel injection (PFI) mode which was verified by the experiment and a gasoline direct injection (GDI) mode. The results showed that: both the GDI mode and the PFI mode produced large amounts of nitrogen oxide (NOx) during the working process. But the GDI mode produced before the PFI mode and it produced nearly 2 times than the PFI mode. However, the formation rate of NOx in GDI mode is much lower than that in PFI mode. Meanwhile, in both modes, 90% of NOX was generated in the cylinder at the temperature higher than 1750K, and only about 10% of NOX was generated at a temperature lower than 1750K.

Heat Release Rate (HRR) analysis is indispensable in engine research. The HRR of Internal Combustion Engines (ICEs) is most sensitive to gamma (γ). The proposed HRR models in literature were largely based on γ expressed as functions of temperature. However, γ is depended on temperature as well as the excess air ratio (λ). In this work, an improved HRR model based on γ(T, λ) was used to investigate the combustion behaviour of standard diesel, Gas-to-Liquid (GTL) diesel and Hydrotreated Vegetable Oil (HVO) diesel in a 96 kW, multiple fuel injection, Euro V, Direct Injection (DI) engine. The improved HRR model (Leeds HRR model) was validated for the alternative fuels by comparing the fuel masses predicted by the model to the measured fuel masses. The fuel masses predicted by the Leeds HRR model were also compared to the predictions from four HRR models that were based on γ(T).