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Over the past years, several efforts have been made to reduce greenhouse gas emissions coming from the transport sector. Due to the highly efficient CO2-free combustion and low manufacturing costs, Hydrogen Internal Combustion engines (H2ICEs) are considered one of the most promising solutions for the future of medium and heavy duty vehicles. However, the combustion of an air-hydrogen mixture presents challenges related to the production of nitrogen oxides (NOx) and high knock tendency, mainly due to the chemical characteristics of the fuel. Although these problems can be mitigated by the use of a lean mixture, which is also useful to increase the combustion efficiency, the presence of excess air reduces exhaust temperatures and, consequently, the enthalpy content in the exhaust would be limited, leading to a reduced boosting capability. Therefore, a proper control of mixture preparation and combustion phasing is mandatory to limit NOx emissions, avoid abnormal combustions, and maximize efficiency without performance limitations. This paper focuses on the design of a dedicated control strategy for H2ICEs. Starting from a previously validated 1-D engine model operated with hydrogen, a 0-D Artificial Neural Network (ANN) - based engine model has been designed and calibrated. By using the obtained fast running ANN-based model, an innovative torque-based engine controller has been developed and both engine and controller models have been tested covering different torque profiles. The results show good accuracy within a range of ±5% on producing the requested torque by controlling the centre of combustion. [Display omitted] • Hydrogen represents an optimal solution for the abatement of CO2 emissions related to the transport sector. • Hydrogen combustion is prone to abnormal combustions and NOx emissions. • Dedicated control strategies are needed to achieve an efficient, clean and reliable H2 combustion. • Model in the Loop approach aimed at testing the proposed control strategy. • Artificial neural network – based fast running engine model enabled testing the control system in relevant running conditions. [ABSTRACT FROM AUTHOR]

The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO2 and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of a more suitable fuel injection system and the control of the combustion process are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated, allowing the identification of a "sweet spot" in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40%, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMSs) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allows targeting H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and aftertreatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains. [ABSTRACT FROM AUTHOR]