Abstract With gasoline direct injection, fuel injection parameters should be optimally tuned, which is critical in determining the fuel-air mixing and the charge quality during combustion. Fuel injection timing and pressure are two critical parameters considered in this study to examine the role of fuel injection pressures at different fuel injection timings. The resulting air-fuel mixture's sensitivity was investigated concerning ignition timing and engine speed variations based on the engine's combustion and emissions characteristics. The experiments were conducted on a 500-cc, single-cylinder, wall-guided Gasoline Direct Injection engine. All the tests were performed with a fixed fuel injection quantity of ∼22 mg. Three fuel injection pressures, i.e., 100, 150 and 200 bar, and three fuel injection timings indicating different stages of the intake stroke, namely early intake (315° bTDC), mid-intake (270° bTDC) and late-intake (225° bTDC), were considered for the experiments. The degree of complete combustion was maximised with the lowest combustion duration for early fuel injection timing (315° bTDC) due to higher mixture homogeneity and proper charge formation during ignition. . Overall, fuel injection timing of 315° bTDC with 100 bar FIP showed the highest thermal efficiency and lower carbon monoxide and hydrocarbon emissions at all considered engine speeds. In contrast, nitric oxide emissions were significantly higher for these parameters.

Corrigendum to ‘Influence of fuel formulation on exhaust emissions from gasoline direct injection vehicle’ [Fuel Processing Technology, Volume 272, July 2025, 108215

Intermediate-volatility (IVOCs) and semivolatile organic compounds (SVOCs) from light-duty gasoline vehicles (LDGVs) are major precursors of secondary organic aerosol (SOA), yet their emission characteristics under evolving engine and aftertreatment technologies remain poorly understood. In this study, two-dimensional gas chromatography-mass spectrometry (GC-MS) (GC × GC-MS) resolved over 2000 organic species from LDGV exhausts, enabling refined emission profiles across vehicles with port fuel injection (PFI) and gasoline direct injection (GDI) engines. Oxygenated compounds, primarily acids and carbonyls, accounted for 40.5-58.5% of gaseous I/SVOCs. Although average I/SVOC emission factors showed no significant differences across the testing fleet, PFI vehicles emitted more reduced species, likely due to a lower combustion efficiency. In contrast, GDI engines significantly enhanced oxidized I/SVOC abundances by 45.6%, particularly within the intermediate-volatility range, and increased the overall oxidation state of the emitted organics. Furthermore, the application of gasoline particulate filters (GPFs) increased benzylic carbonyl emissions, suggesting potential oxidative effects. Under cold-start conditions, especially at low ambient temperatures, I/SVOC emissions were further elevated. Incorporating oxidized IVOCs into emission profiles refined the IVOC-to-total gaseous organics ratio to 27.8% and also corrected SOA prediction biases by up to 38.2%. These findings underscore the need to prioritize I/SVOCs in emission control strategies and improve SOA modeling accuracy.

Renewable fuels are derived from natural and replenished sources. They offer a sustainable and cleaner alternative to fossil fuels, helping to reduce greenhouse gas emissions, enhance energy security through diversification and create economic opportunities. Butanol and ethanol have emerged as promising renewable alternatives to conventional gasoline for spark-ignition engines and flex-fuel vehicles. This research analyse the liquid and vapor penetration behaviours of binary and ternary blends of biofuels and gasoline fuels compared to single-component fuels in a gasoline direct injection system. Simulation of spray G in GDI engine is carried by the computational fluid dynamics code CONVERGE v2.4. The Unsteady Reynolds Averaged Navier–Stokes Renormalization k-ε turbulence model is considered for this work. The latest experimental data from Engine combustion network data uses to validate the work. Results show that for all gasoline-ethanol blends, the higher penetration for both liquid and vapor phases are increases by 0.18%, 0.19% respectively for increase in ethanol proportion. Sauter Mean Diameter decreases by 0.75%. Also, G60E40 give the better results among all the blends. To overcome some disadvantages of ethanol this study added butanol into the fuel blends to further enhance the overall advantages of biofuels. For gasoline-ethanol-butanol blends the liquid and vapor penetration is increased by 0.13%, 0.17% respectively with decrement of SMD by 0.11%. G20E60B20 perform better among all blends. It also observed that for gasoline-ethanol-butanol blends, the liquid and vapor penetration for G100 is lower and all SMD curves lies between G100 and G15E85 fuels. No significant difference is observed when part of ethanol is replaced by butanol thereby indicating that GEB blends can be used in a Gasoline Direct Injection-based Flexible Fuel Vehicles and the ethanol concentration can be replaced with some amount of butanol for its benefits.

For most of the time, soot oxidation in gasoline particulatefilters(GPFs) occurs in oxygen-lean and high-temperature environments dueto the typically stoichiometric combustion mode of gasoline directinjection (GDI). The oxygen-lean and high-temperature atmospheresinevitably change the oxidation characteristics, altering the physicochemicalproperties of GDI soot particles and consequently affecting the controlstrategies of GPF regeneration. To gain deeper insight into the GDIsoot oxidation process, a detailed investigation into the changesin the physicochemical properties of GDI soot under different oxygenconcentrations was performed. The morphology, nanostructure, surfacefunctional group, and hybridized carbon state of the soot sampleswere analyzed using high-resolution transmission electron microscopy(TEM), X-ray photoelectron spectroscopy (XPS), and Fourier transforminfrared spectroscopy (FT-IR). The macroscopic morphology resultsshow that, with the conversion level increased to 80%, both GS-20and GS-1 soot particles become more compact. The fractal dimension(Df) of GS-20 and GS-1 soot particlesincreased by 19.5 and 26.4%, respectively, while their primary particlesizes decreased by 27.4 and 25.5%. Simultaneously, the radius of gyration(Rg) was reduced by 38.2 and 43.4%, respectively.A comparison between the two soot samples indicates that the oxygen-leanatmosphere tends to yield soot with a smaller Rg, a larger primary particle size, and Df than the oxygen-rich atmosphere, thereby making the subsequentoxidation of GS-1 soot more challenging. From the nanostructural perspective,both soot samples undergo a gradual transformation into a more orderedstructure, as evidenced by a similar decrease in fringe tortuosity() and an increase in fringe length (). However, at the same conversion level,GS-1 soot demonstrates a shorter and higher compared to GS-20, which facilitates oxygenpenetration into the primary particles and thus accelerates oxidation.Chemical analysis further reveals that under oxygen-rich conditions,the preferential oxidation of sp3-hybridized carbon leadsto a lower sp2/sp3 ratio in GS-1 soot comparedto GS-20 at the same level of carbon conversion. In contrast, underoxygen-lean conditions, the relatively limited availability of oxygenleads to a surface concentration of C–OH groups on GS-1 sootthat is up to 9.3% lower and CO groups up to 13.8% lower thanthose on GS-20 at 80% conversion.

Global regulations on vehicle emissions have compelled the automotive industry to undergo rapid advancements over the past decades, prioritizing strategies for developing engines with greater efficiency and lower emissions. While the market for electric vehicles continues to expand, internal combustion (IC) engines remain indispensable and constitute the majority of the global vehicle fleet. Among the critical parameters for controlling engine performance, emissions, and fuel economy, the air–fuel ratio (AFR) is a key determinant. Inadequate control or fluctuations in AFR, whether in steady-state or transient conditions, can lead to higher levels of pollutant emissions, increased fuel consumption, and engine instability. For more than a century, AFR control has progressed from carburetor-based systems to electronically controlled port fuel injection (PFI) or gasoline direct injection (GDI) systems. In PFI or GDI systems, the precision of AFR regulation depends heavily on the performance of the controller governing the actuation of fuel injectors. This article reviews recent advancements in AFR control techniques, categorizing them into feedback, model-based, parameter estimation, robust, fuzzy, adaptive, neural network (NN), and machine learning (ML) approaches. At the end of each section, a comparative discussion outlines the most appropriate use cases for each technique, their relative performance against alternative approaches, implementation limitations, and studies that have demonstrated their applicability in real-world scenarios. Furthermore, graphical representations are provided to illustrate the annual volume of published research on IC engine control using each technique. These figures offer valuable insights into prevailing research trends and support the identification of promising directions for the development and implementation of future control strategies.

Overlooked crisis: cold temperature amplifies particle number emissions from gasoline vehicles.

Corrigendum to “Influence of fuel formulation on exhaust emissions from gasoline direct injection vehicle ” [Fuel Processing Technology, 272 (2025), 108215

Numerical analysis of misfire in an automotive lean-burn direct-injection spark-ignition engine

Effect of injection timing on gas jet developments in a hydrogen low-pressure direct-injection spark-ignition engine

Combustion and flame propagation characteristics of toluene reference fuel in an optical GDI engine

Interaction between in-cylinder airflow and spray and its influence on flame development in a loop-scavenged two-stroke GDI engine

Effects of Multi-stage Ignition Strategies on Combustion Characteristics in a Direct Injection Spark Ignition Engine under Simulated EGR Conditions

Three TOP TIERTM gasoline deposit control additives (DCAs) of differing chemistries were tested for their impact on particulate matter emissions in terms of particulate mass (PM) and particle number (PN) at operating conditions representative of road load, cold start, and high load on a 2.0 L, 4-cylinder, gasoline direct injection (GDI) spark ignition (SI) engine. The PM-PN emissions were measured using an Exhaust Emissions Particle Sizer (EEPS). Deposit control additives or detergents are gasoline additives used to prevent and clean combustion chamber and injector deposits in gasoline spark ignition (SI) engines. All three gasoline additives were tested at each operating condition at three different treatment rates. In addition, one of the additives was tested with a fuel-based friction modifier (FM). The results showed that of the treatment rates tested, the lowest allowable concentration (LAC) for all additives requires the least time for the emissions to settle. However, the impact of the gasoline additives on PM-PN emissions is not linear and changes with additive concentration depending on the additive chemistry and operating conditions. The additive with the friction modifier resulted in an increase of over 19% particle number and over 30% particulate mass at the road load operating condition, while the increase at high load was over 27% for particle number and 11% for particle mass.

Comprehensive experimental analysis on combustion characteristics of acetone-butanol-ethanol fuel in a constant volume combustion chamber under gasoline direct injection conditions: Effect of fuel composition

The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO2 emissions were lower.

Effect of copper and manganese as active phases in ceria and ceria-praseodymia catalysts for soot combustion in the exhaust of Gasoline Direct Injection (GDI) vehicles

The displacement of fossil fuel use for transport with sustainable alternatives is urgently required to reduce greenhouse gas emissions and address global climate change. Advanced biofuels from renewable feedstocks, for example waste biomass, present an opportunity to decarbonise the use of combustion for propulsion however sustainable utilisation of these fuels also requires consideration of impacts on other exhaust pollutants that negatively affect the environment and human health. Therefore, while exhaust after-treatment systems are an established and effective means of emission reduction during combustion of hydrocarbon fuels, there is a need to understand impacts of biofuel use on the performance of devices including three-way catalysts (TWC) for simultaneous reduction of nitrogen oxides (NOx), carbon monoxide (CO) and unburnt hydrocarbons (THC). This experimental study therefore investigates the effects of four potential biofuel molecules, 2-methylfuran (MF), 2-methyltetrahydrofuran (MTHF), gamma valerolactone (GVL) and linalool (LNL), on combustion, engine-out exhaust emissions and pollutant conversion across a three-way catalyst (TWC) in a gasoline direct-injection engine. The potential biofuel molecules were blended with reference gasoline (RGL) at 20% wt/wt and supplied to a light-duty direct-injection spark ignition engine operated at constant conditions, with gaseous and particulate exhaust emissions measured pre- and post- TWC during catalyst warm-up during engine cold-start and at steady state. While the biofuel blends displayed similar rates of heat release rate relative to gasoline combustion, the MF blend significantly increased CO and NOx engine-out emissions both during cold-start and at steady state. The use of GVL reduced NOx, while hydrogen (H 2 ) emissions correlated with blend hydrogen carbon ratio. All of the biofuel blends increased the TWC inlet temperature required for pollutant conversion, while MF, LNL and GVL increased H 2 levels post-TWC at higher temperatures. LNL exhibited higher particulate levels post-TWC than gasoline only, despite lower engine-out emissions during combustion of the biofuel blend.

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R2) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines.

Hybrid Electric Vehicles(HEVs) are recognized as a promising mid-term solution for reducing greenhouse gas emissions and accelerating vehicle electrification. This research presents the development of a new hybrid-dedicated 2.5 L turbocharged engine equipped with gasoline direct injection, aiming to achieve high efficiency and dynamic performance for midsize SUV application. To enhance thermal efficiency, the combustion system was optimized by tuning intake cam closing timing and geometric compression ratio, effectively realizing the Atkinson cycle with a high expansion ratio and reduced effective compression ratio. Under low-speed and high-load engine conditions, a multiple injection strategy, including late injection during compression stroke, and a diluted combustion using low-pressure exhaust gas recirculation(LP-EGR), were applied to suppress knocking and improve combustion phasing. The engine test showed a 5% reduction in fuel consumption across high-frequency HEV operating points, while maintaining sufficient power and torque output. Additional improvements in piston geometry and injection strategy contributed to faster catalyst heating and lower engine-out raw emissions. The proposed hybrid system, featuring two motors and a hybrid dedicated engine, demonstrated superior system response through new control strategies. This development provides a viable powertrain solution for future hybrids, combing performance, efficiency, and environmental benefits.