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The automotive industry is increasingly challenged to develop cleaner, more efficient solutions to comply with stringent emission standards. Hydrogen (H2)-powered internal combustion engines (ICEs) offer a promising alternative, with the potential to reduce carbon-based emissions and improve efficiency. However, hydrogen combustion presents two main challenges related to the calibration process: emissions control and measurement of the air excess coefficient (λ). Traditional lambda sensors struggle with hydrogen's combustion dynamics, leading to potential inefficiencies and increased pollutant emissions. Consequently, the determination of engine performance could also be compromised. This study explores the feasibility of using machine learning (ML) to replace physical lambda sensors with virtual ones in hydrogen-fueled ICEs. The research was conducted on a single-cylinder spark-ignition (SI) engine, collecting data across a range of air excess coefficients from 1.6 to 3.0. An advanced hybrid model combining long short-term memory (LSTM) networks and convolutional neural networks (CNNs) was developed and fine-tuned to accurately predict the air–fuel ratio; its predictive performance was compared to that obtained with the backpropagation (BP) architecture. The optimal configuration was identified through iterative experimentation, focusing on the neuron count, number of hidden layers, and input variables. The results demonstrate that the LSTM + 1DCNN model successfully converged without overfitting; it also showed better prediction ability in terms of accuracy and robustness when compared with the backpropagation approach. [ABSTRACT FROM AUTHOR]

Hydrogen fuel is gaining particular attention in internal combustion engines. In addition to zero-carbon emissions, major advantages relate to its combustion characteristics, which allow a significant increase in thermal efficiency under ultra-lean operation and with very low NOx levels. The ignition system is one of the main technology enablers, as it determines the capability to control ultra-lean operations, avoid backfire phenomena, and/or reduce the risks of abnormal combustions. The latter results from hydrogen's low ignition energy and it is associated with factors like high-temperature residuals, hot spots, and irregular spark plug discharge. The ACIS gen 2-Barrier Discharge Igniter excels in accelerating the initial flame growth speed by the generation of non-equilibrium low-temperature plasma, a strong ignition promoter for the combined action of kinetic and thermal effects. Moreover, its volumetric discharge facilitates combustion initiation on a wide region, in contrast to the localized ignition of traditional spark systems. In this work we present for the first time, to the best of our knowledge, experimental results showing the performance of a hydrogen engine with a low-temperature plasma discharge. Tests were conducted on a single-cylinder research engine, achieving ultra-lean conditions with cycle-to-cycle variability results below 2.5%. The analysis indicates that the H2-BDI combined solution is capable of accelerating the evolution of the flame front compared to traditional spark plugs, leading to a significant reduction in the cycle-to-cycle variability. A meticulous adjustment of the BDI control parameters further enhances igniter performance and contributes to a deeper understanding of the innovative approach proposed in this study. [ABSTRACT FROM AUTHOR]

The main challenge of improving spark ignition (SI) engines to achieve ever increasing thermal efficiencies and near-zero pollutant emissions today concerns developing turbulent combustion under homogeneous ultra-lean premixed mixtures (HULP). This continuous shift of the lean operation limit entails questions on the applicability limits of the combustion models used to date for SI engine design and optimization. In this work, an assessment of flamelet-based models, widely used in RANS SI engines simulations of premixed turbulent combustion, is carried out using an open-source 3D-CFD platform to clarify the applicability limits on HULP mixtures. Two different consolidated approaches are selected: the Coherent Flame Model (CFM) and the Flame Area Model (FAM). Both methodologies are embedded by the authors into the same numerical structure and compared against measurements over a simplified and controlled flame configuration, which is representative of engine-like conditions. The experimental steady-state flame of type "A" of the Darmstadt Turbulent Stratified Flame (TSF) burner is selected for the assessment. This configuration is characterized by flame measurements over a strong shear and mixing layer between the central high-speed CH 4 -air jet and the surrounding slow air co-flow, hence, it represents an interesting controlled condition to study turbulent HULP mixtures. A comparison between computed results and experimental data on trends of mean flow velocity, turbulence, temperature and mixture stratification was carried out. This enabled us to assess that the investigated flamelet-based combustion models failed in providing accurate and reliable results when the flame approaches turbulent HULP mixture conditions, demonstrating the urgency to develop models able to fill this gap. [ABSTRACT FROM AUTHOR]

The effect of the fuel injection ratio on the combustion and emission characteristics of stratified lean mixture combustion was investigated for a spray-guided liquefied petroleum gas (LPG) direct injection engine. Inter-injection spark-ignition combustion-a specially designed combustion strategy for LPG fuel derived from a two-staged injection-was employed to maximize the improvement in thermal efficiency when combustion stability is secured. When changing the fuel injection ratio, the optimum spark advance and fuel injection timings were experimentally determined to maximize the thermal efficiency based on sweeping timings. The optimum fuel injection ratio with the highest thermal efficiency (42.76%) and stable operation was 60%/40%, with the optimization of the spark advance and fuel injection timing, because of the locally rich mixture region in the recirculation zone. NOx emissions were at their highest level with a fuel injection ratio of 60%/40% because of the high combustion temperature, and the levels of total hydrocarbon and CO emissions with 50%/50% and 60%/40% fuel injection ratios were similar, whereas emissions at 70%/30% were significantly higher because of fuel wetting and the formation of over-lean mixture. [ABSTRACT FROM AUTHOR]

The complexity of modern hybrid powertrains poses new challenges for the optimal control concerning, on one hand, the thermal engine to maximize its efficiency, and, on the other hand, the vehicle to minimize the noxious emissions and CO2. In this context, the engine calibration has to be conducted by considering simultaneously the powertrain management, the vehicle characteristics, and the driving mission. In this work, a calibration methodology for a two-stage boosted ultra-lean pre-chamber spark ignition (SI) engine is proposed, aiming at minimizing its CO2 and pollutant emissions. The engine features a flexible variable valve timing (VVT) control of the valves and an E-compressor, coupled in series to a turbocharger, to guarantee an adequate boost level needed for ultra-lean operation. The engine is simulated in a refined 1D model. A simplified methodology, based on a network of proportional integral derivative (PID) controllers, is presented for the calibration over the whole operating domain. Two calibration variants are proposed and compared, characterized by different fuel and electric consumptions: the first one aims to exclusively maximize the brake thermal efficiency, and the second one additionally considers the electric energy absorbed by the E-compressor and drained from the battery. After a verification against the outcomes of an automatic optimizer, the calibration strategies are assessed based on pollutant and CO2 emissions along representative driving cycles by vehicle simulations. The results highlight slightly lower CO2 emissions with the calibration approach that minimizes the E-compressor consumption, thus revealing the importance of considering the engine calibration phase, the powertrain management, the vehicle characteristics, and its mission. [ABSTRACT FROM AUTHOR]