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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.

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.

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.

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.

Incomplete-combustion and misfire are the hurdles in internal combustion engines to run on ultra-lean mixture, whereas high thermal efficiency has been achieved at lean mixture. The burning characteristics of n-heptane with 0% and 30% hydrogen additions were studied at 393K-453K and 100kPa-300kPa up to near lean burn limits, λ=0.8-2.0. The flame appeared in spherical shape only by 37-mJ ignition energy (IE) at λ=0.8-1.5, while further lean mixture, ≥1.6, could be ignited only by 3000-mJ with the distorted flame shape. The flame buoyed in the mixture when burning velocity calculated by kinetic mechanism was equal or less than 19.83 cm/s at the initial conditions of λ=1.8, 393K and 100kPa. The thermal instability under impact of initial pressure and temperature was higher at lean mixture than at stoichiometric mixture.

Hydrogen fuel is a future energy to solve the problems of energy crisis and environmental pollution. Hydrogen internal combustion engines can combine the advantage of hydrogen without carbon pollution and the main basic structure of the traditional engines. However, the power of the port fuel injection hydrogen engines is smaller than the same volume gasoline engine because the hydrogen occupies the volume of the cylinder and reduces the air mass flow. The turbocharger can increase the power of hydrogen engines but also increase the NOx emission. Hence, a comprehensive controlling strategy to solve the contradiction of the power, BTE and NOx emission is important to improve the performance of hydrogen engines. This paper shows the controlling strategy for a four-stroke, 2.3L hydrogen engine with a turbocharger. The controlling strategy divides the operating conditions of the hydrogen engine into six parts according to the engine speeds and loads.

HTL is a kind of biodiesel converted from wet biowaste via hydrothermal liquefaction (HTL), which has drawn increasing attention in recent years due to its wide range of raw materials (algae, swine manure, and food processing waste). However, from the previous experiments done in a constant volume chamber, it was observed that the presence of 20% of HTL in the blend produced as much soot as pure diesel at in chamber environment oxygen ratio of 21%, and even more soot at low oxygen ratios. It was also observed that n-butanol addition could reduce the soot emission of diesel significantly under all tested conditions. In this work, the spray and combustion characteristics of HTL and diesel blends with n-butanol added were investigated in a constant volume chamber. The in-chamber temperature and oxygen ranged from 800 to 1200 K and 21% to 13%, respectively, covering both conventional and low-temperature combustion (LTC) regimes.

Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

The SAE J300 classification was expanded to 0W-12 and 0W-8 viscosity grades in 2015, and lower viscosity engine oils have been studied in the industry. ILSAC GF-6B that will be introduced in 2020 will specify a 0W-16 requirement, but 0W-12 and 0W-8 grades are not considered. Because engine oil equal to or higher than the 0W-20 grade is recommended for almost all engines globally, suitable engine tests for 0W-12 and 0W-8 do not exist. Therefore, the Japan Automobile Manufacturers Association, Petroleum Association of Japan and Society of Automotive Engineers of Japan decided to establish new 0W-12 and 0W-8 low viscosity engine oil specifications. It is referred to as JASO GLV-1, and together with a new fuel economy engine test procedure, these engine oils for better fuel economy will be put on the Japanese market in 2019. Motoring friction torque tests are widely used to ascertain the friction reduction effect of fuel-economy engine oils.

This study employed a diesel particulate generator (DPG), with an installed engine oil injector for soot and ash accumulation in a diesel particulate filter (DPF). Ash was generated by engine oil injection into the diesel burner flame. The amount of soot accumulation per loading varied from 0.5 g/L to 8 g/L while ash accumulation amount per loading was maintained at 0.5 g/L. Initially, ash accumulation distribution in the DPF was visualized using X-ray computed tomography (CT). It was revealed that the form of ash accumulation changed depending on the amount of soot accumulation before active regeneration, i.e., a large amount of soot accumulation resulted in plug ash, whereas a small amount of soot accumulation resulted in wall ash. To clarify ash accumulation mechanisms, soot and ash transport behavior in DPF during active regeneration process was directly observed using a high-speed camera through an optically accessible D-shaped cut DPF covered with a quartz glass plate.