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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.

Currently, conventional spark–ignition engines face challenges in meeting the ever-growing demands of customers and increasingly stringent regulations regarding pollutant emissions. A combination of innovative strategies and carbon-neutral fuels is deemed necessary in order to further reduce fuel consumption and minimize engine emissions. The present work aims to assess the performance of combustion strategies using low-carbon-content fuel, such as methanol M100, ignited by a plasma-assisted igniter (PAI) under ultra-lean conditions. The experimental campaign is conducted on a single-cylinder research engine at 1000 rpm and low loads, moving up to the engine lean stable limits. The specific purpose of this work is to determine the benefits brought by the proposed strategy, referred to as M100–PAI, which compared market gasoline E5 ignited by the PAI system and conventional spark. The synergy between M100 (methanol) and Plasma-Assisted Ignition (PAI) in internal combustion engines yielded notable benefits. This combination significantly improved combustion stability if compared to the other combinations tested, by extending the lean stable limit to λ = 2.0, reducing cycle-to-cycle variability, and facilitating faster flame front acceleration, resulting in enhanced homogeneity. These enhancements, obtained with the combination M100–PAI, contributed to higher fuel efficiency, showing a 10% efficiency gain over the combination E5–gasoline spark ignition. The findings highlight the potential of innovative combustion strategies using low-carbon fuels and advanced ignition systems to meet stringent emissions regulations while improving engine performance.

Efficient and low-polluting mobility is a major demand in all countries. Hybrid electric vehicles have already shown to be suitable to respond to this need, being a reliable alternative to conventional cars, at least in urban environments. Nevertheless, such vehicles present a yet unexplored potential. In this paper, we will investigate how the powertrain efficiency may possibly benefit, in an integrated drivetrain for a hybrid electric vehicle, based on a turbocharged gasoline engine, of an innovative supercharging system. The compressor and turbine will be mechanically decoupled so as to independently optimize their operation, avoiding turbo lag and maximizing energy recovery by completely eliminating the waste-gate valve. This, in turns, requires changing the turbine so as to have a flattest possible efficiency/load curve. Therefore, an ad-hoc designed turbine will be implemented in the decoupled configuration, to be used to drive an electrical generator and produce electrical energy for charging the battery. This study presents a preliminary assessment of the potential of a turbo-compounded system for a 1L turbocharged gasoline engine for a small city car. To this aim, a one-dimensional dynamic model of the engine has been built in GT-Suite and has been calibrated and validated by means of experimental data obtained on a dynamometer, both in steady state and dynamic conditions. In particular, the model has been calibrated by means of experimental data obtained in stationary conditions and its robustness has then been verified through experimental data obtained under transient conditions. The model also includes data retrieved from the characterization of the existing turbine and compressor, while a new performance map for the turbine has been designed to better exploit the potential of the components’ decoupling. Results include the estimation of energy recovery potential of such a solution. Under the implementation of a straightforward control strategy, which runs both compressor and turbine at the same speed, the system is able to achieve a 60.57% increase in energy recovered from the exhaust gasses in the turbine. Afterwards, an attempt was made to limit the minimum turbine speed to 45000 rpm and simultaneously decrease the instantaneous speed by 3000 rpm compared to the compressor, attaining a further increase of 1.7% in the energy recovered by the turbine.

The control of internal combustion engines is becoming increasingly challenging to the customer’s requirements for growing performance and ever-stringent emission regulations. Therefore, significant computational efforts are required to manage the large amount of data coming from the field for engine optimization, leading to increased operating times and costs. Machine-learning techniques are being increasingly used in the automotive field as virtual sensors, fault detection systems, and performance-optimization applications for their real-time and low-cost implementation. Among them, the combination of long short-term memory (LSTM) together with one-dimensional convolutional neural networks (1DCNN), i.e., LSTM + 1DCNN, has proved to be a promising tool for signal analysis. The architecture exploits the CNN characteristic to combine feature classification and extraction, creating a single adaptive learning body with the ability of LSTM to follow the sequential nature of sensor measurements over time. The current research focus is on evaluating the possibility of integrating virtual sensors into the on-board control system. Specifically, the primary objective is to assess and harness the potential of advanced machine-learning technologies to replace physical sensors. In realizing this goal, the present work establishes the first step by evaluating the forecasting performance of a LSTM + 1DCNN architecture. Experimental data coming from a three-cylinder spark-ignition engine under different operating conditions are used to predict the engine’s in-cylinder pressure traces. Since using in-cylinder pressure transducers in road cars is not economically viable, adopting advanced machine-learning technologies becomes crucial to avoid structural modifications while preserving engine integrity. The results show that LSTM + 1DCNN is particularly suited for the prediction of signals characterized by a higher variability. In particular, it consistently outperforms other architectures utilized for comparative purposes, achieving average error percentages below 2%. As cycle-to-cycle variability increases, LSTM + 1DCNN reaches average error percentages below 1.5%, demonstrating the architecture’s potential for replacing physical sensors.

To carry out increasingly sophisticated checks, which comply with international regulations and stringent constraints, on-board computational systems are called upon to manipulate a growing number of variables, provided by an ever-increasing number of real and virtual sensors. The optimization phase of an ICE passes through the control of these numerous variables, which often exhibit rapidly changing trends over time. On the one hand, the amount of data to be processed, with narrow cyclical frequencies, entails ever more powerful computational equipment. On the other hand, computational strategies and techniques are required which allow actuation times that are useful for timely and optimized control. In the automotive industry, the ‘machine learning’ approach is becoming one the most used approaches to perform forecasting activities with reduced computational effort, due to both its cost-effectiveness and its simple and compact structure. In the present work, the nonlinear dynamic system we address is related to the torque estimation of an ICE through a nonlinear autoregressive with exogenous inputs (NARX) approach. Preliminary activities were performed to optimize the neural network in terms of neurons, hidden layers, and the number of input parameters to be assessed. A Shapley sensitivity analysis allowed quantification of the impact of each variable on the target prediction, and therefore, a reduction in the amount of data to be processed by the architecture. In all cases analyzed, the optimized structure was able to achieve average percentage errors on the target prediction that were always lower than a critical threshold of 10%. In particular, when the dataset was augmented or the analyzed cases merged, the architecture achieved average prediction errors of about 1%, highlighting its remarkable ability to reproduce the target with fidelity.