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The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM).

The environmental and sustainable energy concerns in transport are being addressed through the decarbonisation path and the potential of hydrogen as a zero-carbon alternative fuel. Using hydrogen to replace fossil fuels in various internal combustion engines shows promise in enhancing efficiency and achieving carbon-neutral outcomes. This study presents an experimental investigation of hydrogen (H2) combustion and engine performance in a boosted spark ignition (SI) engine. The H2 engine incorporates both port fuel injection (PFI) and direct injection (DI) hydrogen fuel systems, capable of injecting hydrogen at pressures of up to 4000 kPa in the DI system and 1000 kPa in the PFI operations. This setup enables a direct comparison of the performance and emissions of the PFI and DI operations. The study involves varying the relative air-to-hydrogen ratio (λ) at different speeds to explore combustion and engine limits for categorising and optimising operational regions.

Parking-slot detection plays a critical role in the self-parking system for autonomous driving. To enhance the complexity of the environmental situations in parking-slot datasets and reduce the difficulty of manual annotation, we design several data synthesis methods to generate new parking-slots under different situations. Methods introduced in this paper include synthesizing parking-slots in AVM (around view monitor) images, generating parking-slots in fisheye images and adding 2D symbols inside parking-slots to form special ones. To test the influence of our synthetic data, we conduct a series of experiments on different tasks. In the parking-slot detection experiments, we design a novel two-stage parking-slot detection method. We use YOLOv7 as the object detector and different from previous methods, we detect the complete parking-slots and marking points at the same time. Then we match marking points and give them a certain order in the second stage.

The development of autonomous driving generally requires enormous annotated data as training input. The availability and quality of annotated data have been major restrictions in industry. Data synthesis techniques are then being developed to generate annotated data. This paper proposes a 2D data synthesis pipeline using original background images and target templates to synthesize labeled data for model training in autonomous driving. The main steps include: acquiring templates from template libraries or alternative approaches, augmenting the obtained templates with diverse techniques, determining the positioning of templates in images, fusing templates with background images to synthesize data, and finally employing the synthetic data for subsequent detection and segmentation tasks. Specially, this paper synthesizes traffic data such as traffic signs, traffic lights, and ground arrow markings in 2D scenes based on the pipeline.

The exponential rise in greenhouse gas (GHG) emissions into the environment is one of the major concerns of international organisations and governments. As a result, lowering carbon dioxide (CO2) and methane (CH4) emissions has become a priority across a wide range of industries, including transportation sector, which is recognised as one of the major sources of these emissions. Therefore, renewable energy carriers and powertrain technologies, such as the use of alternative fuels and combustion modes in internal combustion engines, are required. Dual-fuel operation with high substitution ratios using low carbon and more sustainable fuels can be an effective short-term solution. Hythane, a blend of 20% hydrogen and 80% methane, could be a potential solution to this problem.

This study aims to improve the dual fuel combustion for low/zero carbon fuels. Seven cases were tested in a single cylinder optical engine and their ignition and combustion characteristics are compared. The baseline case is the conventional diesel combustion. Four cases are diesel-gas (compressed natural gas) dual-fuel combustion operations, and two cases are diesel-hythane combustion. The diesel fuel injection process was visualized by a high-speed copper vapour laser. The combustion processes were recorded with a high-speed camera at 10000 Hz with an engine speed of 1200 rpm. The high-speed recordings for each case included 22 engine cycles and were postprocessed to create one spatial overlapped average combustion image. The average combustion cycle images were then further thresholded and these images were then used in a new method to analyze the cycle-to-cycle variation in a dimensionless, for all cases comparable value.

Climate change mitigation is the main challenge for the automotive industry, as the government issues legislation to combat CO2 emissions. In addition to electrification and battery electric vehicles, using low-carbon and zero-carbon fuels in Internal Combustion (IC) engines can also be an effective way to reach net zero-carbon transport. This study investigated and compared the combustion characteristics, performance and emissions of a highly boosted spark ignition (SI) engine fuelled with EU VI 95 RON E10 gasoline and blends of second-generation bio-gasoline with different ethanol contents of 5% (E5), 10% (E10), and 20% (E20). The single-cylinder SI engine was equipped with a centrally mounted high-pressure injector and supplied externally boosted air. Engine experiments were conducted at 2000 RPM and 3000 RPM with low and high load operations.

The automobile industry is under intense pressure to reduce carbon dioxide (CO2) emissions of vehicles. There is also increasing pressure to reduce the other tail-pipe emissions from vehicles to combat air pollution. Electric powertrains offer great potential for eliminating tailpipe CO2 and all other tailpipe emissions. However, current battery technology and recharging infrastructure still present limitations for some applications, where a continuous high-power demand is required. Furthermore, not all markets have the infrastructure to support a sizeable electric fleet and until the grid energy generation mix is of a sufficiently low carbon intensity, then significant vehicle life-cycle CO2 savings could not be realized by the Battery Electric Vehicles. This investigation examines the effects of combustion, efficiencies, and emissions of two alcohol fuels that could help to significantly reduce CO2 in both tailpipe and the whole life cycle.

An appropriate energy management strategy can further reduce the fuel consumption of P2 hybrid electric vehicles (HEV) with simple hybrid configuration and low cost. The rule-based real-time energy management strategy dominates the energy management strategies utilized in commercial HEVs, due to its robustness and low computational loads. However, its performance is sensitive to the setting of parameters and control actions. To further improve the fuel economy of a P2 HEV, the energy management strategy of the HEV has been re-designed based on the globally optimal control theory. An optimization strategy model based on the longitudinal dynamics of the vehicle and Bellman’s dynamic programming algorithm was established in this research and an optimal power split in the dual power sources including an internal combustion engine (ICE) and an electric machine at a given driving cycle was used as a benchmark for the development of the rule-based energy management strategy.

Pumping Mean Effective Pressure (PMEP) is the main factor limiting the improvement of thermal efficiency in a spark-ignition (SI) engine under low load. One of the ways to reduce the pumping loss under low load is to use Cylinder DeActivation (CDA). The CDA aims at reducing the firing density (FD) of the SI engine under low load operation and increasing the mass of air-fuel mixture within one cycle in one cylinder to reduce the throttling effect and further reducing the PMEP. The multi-stroke cycles can also reduce the firing density of the SI engine after some certain reasonable design, which is feasible to improve the thermal efficiency of the engine under low load in theory. The research was carried out on a calibrated four-cylinder SI engine simulation platform. The thermal efficiency improvements of the 6-stroke cycle and 8-stroke cycle to the engine performance were studied compared with the traditional 4-stroke cycle under low load conditions.

With the rapid development of autonomous driving technologies, the proportion of autonomous vehicles (AVs) will increase and influence road capacity. In this study, the simulation of mixed traffic flow was studied using an improved cellular automata model. Safety inter-vehicle spacing, the length of vehicles and reaction time are introduced into the cellular automata model. We delete the acceleration, deceleration and randomization rule for ideal conditions. Numerical simulations are utilized to analyze road capacity with different proportions of AVs. Road capacity is about 2200 pcu/h/lane for pure manual vehicle (MV) traffic flow and about 3600 pcu/h/lane for pure AV traffic flow. The capacity increases by 19.2% when there are 50% AVs in the traffic flow. And the capacity increases by around 63.6% due to the pure AV traffic flow.

A 2-stroke boosted uniflow scavenged direct injection gasoline (BUSDIG) engine was researched and developed at Brunel University London to achieve higher power-to-mass ratio and thermal efficiency. In the BUSDIG engine concept, the intake scavenge ports are integrated to the cylinder liner and controlled by the movement of piston top while exhaust valves are placed in the cylinder head. Systematic studies on scavenging ports, intake plenum, piston design, valve opening profiles and fuel injection strategies have been performed to investigate and optimise the scavenging performance and in-cylinder fuel/air mixing process for optimised combustion process. In order to achieve superior power performance with higher thermal efficiency, the evaluation and optimisation of the boost system for a 1.0 L 2-cylinder 2-stroke BUSDIG engine were performed in this study using one dimensional (1D) engine simulations.

Lean-burn combustion is an effective method for increasing the thermal efficiency of gasoline engines fueled with stoichiometric fuel-air mixture, but leads to an unacceptable level of high cyclic variability before reaching ultra-low nitrogen oxide (NOx) emissions emitted from conventional gasoline engines. Multi-point micro-flame ignited (MFI) hybrid combustion was proposed to overcome this problem, and can be can be grouped into double-peak type, ramp type and trapezoid type with very low frequency of appearance. This research investigates the micro-flame ignition stages of double-peak type and ramp type MFI combustion captured by high speed photography. The results show that large flame is formed by the fast propagation of multi-point flame occurring in the central zone of the cylinder in the double-peak type. However, the multiple flame sites occur around the cylinder, and then gradually propagate and form a large flame accelerated by the independent small flame in the ramp type.

This study experimentally investigates the impact of Miller cycle strategies on the combustion process, emissions, and thermal efficiency in heavy-duty diesel engines. The experiments were conducted at constant engine speed, load, and engine-out NOx (1160 rev/min, 1.76 MPa net IMEP, 4.5 g/kWh) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Early Intake Valve Closing (EIVC) and Late Intake Valve Closing (LIVC) timing strategies were compared to a conventional intake valve profile. While the decrease in effective compression ratio associated with the use of Miller valve profiles was symmetric around bottom dead center, the decrease in volumetric efficiency (VE) was not. EIVC profiles were more effective at reducing VE than LIVC profiles. Despite this difference, EIVC and LIVC profiles with comparable VE decrease resulted in similar changes in combustion and emissions characteristics.

Controlled Auto-Ignition (CAI) combustion can effectively improve the thermal efficiency of conventional spark ignition (SI) gasoline engines, due to shortened combustion processes caused by multi-point auto-ignition. However, its commercial application is limited by the difficulties in controlling ignition timing and violent heat release process at high loads. Stratified flame ignited (SFI) hybrid combustion, a concept in which rich mixture around spark plug is consumed by flame propagation after spark ignition and the unburned lean mixture closing to cylinder wall auto-ignites in the increasing in-cylinder temperature during flame propagation, was proposed to overcome these challenges.

Low combustion efficiency and high hydrocarbon emissions at low loads are key issues of natural gas and diesel (NG-diesel) dual fuel engines. For better engine performance, two diesel-spray-orientated (DSO) bowls were developed based on the existing diesel injector of a heavy-duty diesel engine with the purpose of placing more combustible natural gas/air mixture around the diesel spray jets. A protrusion-ring was designed at the rim of the piston bowl to enhance the in-cylinder flame propagation. Numerical simulations were conducted for a whole engine cycle at engine speed of 1200 r/min and indicated mean effective pressure (IMEP) of 0.6 MPa. Extended coherent flame model 3 zones (ECFM-3Z) combustion model with built-in soot emissions model was employed. Simulation results of the original piston bowl agreed well with the experimental data, including in-cylinder pressure and heat released rate (HRR), as well as soot and methane emissions.

The Gasoline direct-injection (GDI) engine can emit high levels of particulate matter and unburned Hydrocarbons when operating under stratified charge combustion mode. Injecting late in the compression stroke means the fuel has insufficient time to atomise and evaporate. This could cause fuel film accumulation on the piston surface and combustion liner. Locally fuel rich diffusion combustion could also result in the formation of soot particles. Employing a split-injection strategy can help tackle these issues. The first injection is initiated early in the intake stroke and could ensure a global homogeneous charge. The second injection during the compression stroke could help form a fuel-rich charge in the vicinity of the spark plug. Many studies have established the crucial role that a split-injection strategy plays in the stratified charge operation of GDI engines.

Regulations have been established for the monitoring and reporting of greenhouse gas (GHG) emissions and fuel consumption from the transport sector. Low carbon fuels combined with new powertrain technologies have the potential to provide significant reductions in GHG emissions while decreasing the dependence on fossil fuel. In this study, a lean-burn ethanol-diesel dual-fuel combustion strategy has been used as means to improve upon the efficiency and emissions of a conventional diesel engine. Experiments have been performed on a 2.0 dm3 single cylinder heavy-duty engine equipped with port fuel injection of ethanol and a high-pressure common rail diesel injection system. Exhaust emissions and fuel consumption have been measured at a constant engine speed of 1200 rpm and various steady-state loads between 0.3 and 2.4 MPa net indicated mean effective pressure (IMEP).

Some of the challenges of optimising the gasoline direct-injection engines are achieving high rates of atomisation and evaporation of fuel sprays for effective fuel-air mixture formation. This is especially important for the stratified charge when operating under cold-start and part-load conditions. Poorly mixed charge results in the increased production of total Hydrocarbons and Nitrogen Oxides. Many studies have previously focused on improving the spray characteristics of a single fuel injection strategy from direct-injection gasoline injectors, with fuel rail pressures of up to 20MPa. The current study focuses on a split injections strategy and its influence on the spray's structure, fuel-air mixing and atomisation rates. Short pulse widths in the range of 0.3ms to 0.8ms are employed. In particular, the effects of dwell times between the two injections on the second injection's spray characteristics are evaluated.

In Spark Ignition (SI)-Controlled Auto Ignition (CAI) hybrid combustion, the in-cylinder temperature and total mass of dilution charge are usually increased compared to the traditional SI engine in order to achieve and control the auto-ignition combustion, which would in turn lead to the variations of the diluted flame propagation combustion. In this study, the optical measurements were performed to understand the flame characteristics at highly diluted conditions. The results showed that the decrease of the flame propagation speed of rich mixture was less than that of lean mixture at highly diluted conditions. However, the inhomogeneous distribution of residual gas led to asymmetric development of flame propagation. The high temperature, strong dilution and rich mixture created local auto-ignition sites which were located in front of the main flame and gradually merged with the main flame.