Browse Topic: Gasoline
Stricter environmental legislation is driving ever-more-demanding performance targets for gasoline particulate filters (GPFs). This study constructs a multi-scale filtration model based on fractal characteristics, taking into account particle size distribution and particle deposition, to investigate the influence of the microstructure of porous media on GPF performance and analyze the impact of structural parameters on capture efficiency and pressure drop. The results show that: (1) Increasing the wall thickness can improve the capture efficiency and pressure drop, and a thicker wall has a stronger inertial interception capacity for larger particles. (2) A reduction in porosity markedly alters both filtration efficacy and flow pressure drop. For particles in the intermediate size range (0.1-0.5 μm), the capture efficiency of a low-porosity structure is more sensitive to the diffusion deposition of small particles, while the inertial collision efficiency of large particles is higher. (3) Shrinking the pore size markedly enhances capture efficiency while simultaneously increasing pressure drop; the finer pore network markedly improves the retention of sub-micron particles, but the passage restriction of large particles is more obvious.
This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model employs simplifications, such as assuming a uniform bulk fuel temperature and using empirically based convective correlations, these assumptions proved adequate for vehicle-level thermal management analysis. This adequacy is supported by the strong correlation between the model’s predictions and experimental field data across realistic driving scenarios. As a practical tool, the model successfully supports the optimization of thermal protection strategies and guides heat shield design decisions. Future work to incorporate measurement uncertainties, localized thermal stratification, and experimental validation of vapor composition would further strengthen predictive accuracy and extend the model's applicability to more detailed design phases.
This paper presents the collaborative efforts of the USCAR GPF OBD Working Group to evaluate and recommend On-Board Diagnostic (OBD) monitoring requirements for Gasoline Particulate Filters (GPFs). The group, comprising representatives from major OEMs, aims to establish a unified understanding of GPF monitoring capabilities and propose regulatory recommendations to CARB. The paper outlines the physics of soot generation and oxidation, regulatory interpretations, and diagnostic strategies, culminating in a proposed framework for GPF OBD compliance. The material in this paper was previously presented at the 2024 SAE OBD Symposium [1].
Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Here's why that's important. It's no secret that fuel economy has been one of the greatest driving forces of automotive evolution over the past several decades. As corporate average fuel economy (CAFE) standards have grown increasingly lofty, OEMs eke out new efficiencies from every area of the vehicle. One of those areas, of course, is the engine, and many OEMs have deployed gasoline direct injection (GDI) technology, which is becoming the most common engine technology on American roadways. But while GDI engines proliferate, varying fuel additization throughout North America has not necessarily kept pace with their unique needs and can, in fact, hinder those engines from meeting and sustaining their full fuel economy potential.
With the expansion of compressed natural gas (CNG) filling station in India, bi-fuel vehicles are gaining popularity in recent times. Bi-fuel engine runs on more than one fuel, say in both CNG and petrol. Hence, the engine must be optimized in both the fuel modes for performance and emissions. However, due to the inherent differences in combustion characteristics: ignition dynamics and fuel properties, they pose a significant challenge in case of detection of misfires. Misfires are caused because of faulty injection systems and ignition systems and incorrect fuel mixture. Accurate detection is essential as misfires deteriorate the catalysts performance and may impacts emission. Misfires (or engine roughness) is calculated from engine crankshaft speed signal. In this study, the effectiveness of crankshaft-based misfires detection method, comparison of misfire signals magnitude in bi-fuel modes and practices developed for accurate detection of misfires is presented.
This paper is to introduce a new catalyst family in gasoline aftertreatment. The very well-known three-way catalysts effectively reduce the main emission components resulting from the combustion process in the engine, namely THC, CO, and NOx. The reduction of these harmful emissions is the main goal of emission legislation such as Bharat VI to increase air quality significantly, especially in urban areas. Indeed, it has been shown that under certain operating conditions, three-way catalysts may produce toxic NH3 and the greenhouse gas N2O, which are both very unwanted emissions. In a self-committed approach, OEMs could want to minimize these noxious pollutants, especially if this can be done with no architecture change, namely without additional underfloor catalyst. In most Bharat VI gasoline aftertreatment system architectures, significant amounts of NH3 occur in two phases of vehicle driving: situations with the catalyst temperature below light-off, which appear after cold start or at low-speed urban driving and hot, high mass flow phases. In this paper, we will compare several approaches to reduce NH3 starting with an existing gasoline technology, diesel technologies modified to gasoline conditions and the especially developed novel gasoline Secondary Emission Treatment (SET) catalyst, providing both ammonia abatement and underfloor three-way functionality. SET is the combination addressing both the cold start phase and hot driving conditions. In addition, it fulfills the role of an underfloor three-way catalyst, responsible for CO and NOx hot phase treatment.
Over the past few decades, Compressed Natural Gas (CNG) has gained popularity as an alternative fuel due to its lower operating cost compared to gasoline and diesel, for both passenger and commercial vehicles. In addition, it is considered more environmentally friendly and safer than traditional fossil fuels. Natural gas's density (0.7–0.9 kg/m3) is substantially less than that of gasoline (715–780 kg/m3) and diesel (849–959 kg/m3) at standard temperature and pressure. Consequently, CNG needs more storage space. To compensate for its low natural density, CNG is compressed and stored at high pressures (usually 200-250 bar) in on-board cylinders. This results in an effective fuel density of 180 kg/m3 at 200 bar and 215 kg/m3 at 250 bar. This compression allows more fuel to be stored, extending the vehicle's operating range per fill and minimising the need for refuelling. Natural Gas Vehicles (NGVs), particularly those in the commercial sector like buses and lorries, need numerous CNG cylinders in order to maximise vehicle range on a single fill. However, increasing the number of on-board cylinders results in a proportional increase in refuelling time, which can have a detrimental impact on operational costs for commercial fleet owners. The CNG fuel system, which usually consists of large-volume petrol cylinders (up to 800 litres), is an essential part of vehicle development. A quick petrol fill-up time is ideal because these vehicles must frequently refuel because they frequently travel vast miles each day. At the moment, the refuelling time is calculated by evaluating the CNG filling time following prototype development. Design modifications to the fuel system are necessary if the filling time is too long, which results in severe time and cost penalties as well as delays in the development cycle of new vehicle products. A mathematical model based on a number of influencing factors has been created by combining AI and ML technology. At the initial Zero design release gateway, this model will forecast the time needed to fill up with CNG petrol on all commercial vehicle platforms. This early prediction will enable additional optimization to improve gas filling time. The goal of this research work is to optimize the filling time for various platform before physical vehicle builds.
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