Innovative torque-based control strategy for hydrogen internal combustion engine.

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]