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.

Engine performance and efficiency are largely influenced by combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle; the most effective control parameter used by electronic control units to optimize the combustion process for spark ignition engines is spark advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring brake mean effective pressure, indicated mean effective pressure (IMEP), and brake specific fuel consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation; the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally, other elements such as engine or components aging, and disturbances like air-to-fuel ratio or air, water, and oil temperature variations could affect the tests results; this facet can be very significant for racing engine testing. This paper presents a novel approach to SA mapping with the objective of improving the performance analysis robustness while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% mass fraction burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes; the distribution of a typical knock-sensitive parameter (maximum amplitude of pressure oscillations) is related to that of $CHRNET$ (net cumulative heat release), determining a robust knock index. A knock limiter controller can then be added in order to restrict the SA range to safe values. The methodology can be implemented in real time combustion controllers; the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.

One of the most effective factors influencing performance, efficiency, and pollutant emissions of internal combustion engines is the combustion phasing: in gasoline engines electronic control units (ECUs) manage the spark advance (SA) in order to set the optimal combustion phase. Combustion control is assuming a crucial role in reducing engine tailpipe emissions and maximizing performance. The number of actuations influencing the combustion is increasing, and as a consequence, the calibration of control parameters is becoming challenging. One of the most effective factors influencing performance and efficiency is the combustion phasing: for gasoline engines, control variables such as SA, air-to-fuel ratio (AFR), variable valve timing (VVT), and exhaust gas recirculation (EGR) are mostly used to set the combustion phasing. The optimal control setting can be chosen according to a target function (cost or merit function), taking into account performance indicators, such as indicated mean effective pressure (IMEP), brake-specific fuel consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature or knock intensity. Many different approaches can be used to reach the best calibration settings: design of experiment (DOE) is a common option when many parameters influence the results, but other methodologies are in use: some of them are based on the knowledge of the controlled system behavior by means of models that are identified during the calibration process. The paper proposes the use of a different concept, based on the extremum seeking approach. The main idea consists in changing the values of each control parameter at the same time, identifying its effect on the monitored target function, and allowing to shift automatically the control setting towards the optimum solution throughout the calibration procedure. An original technique for the recognition of control parameters variations effect on the target function is introduced, based on spectral analysis. The methodology has been applied to data referring to different engines and operating conditions, using IMEP, exhaust temperature, and knock intensity for the definition of the target function and using SA and AFR as control variables. The approach proved to be efficient in reaching the optimum control setting, showing that the optimal setting can be achieved rapidly and consistently.

This work is focused on the development and validation of a spark advance controller, based on a piston “damage” model and a predictive knock model. The algorithm represents an integrated and innovative way to manage both the knock intensity and combustion phase. It is characterized by a model-based open-loop algorithm with the capability of calculating with high accuracy the spark timing that achieves the desired piston damage in a certain period, for knock-limited engine operating conditions. Otherwise, it targets the maximum efficiency combustion phase. Such controller is primarily thought to be utilized under conditions in which feedback is not needed. In this paper, the main models and the structure of the open-loop controller are described and validated. The controller is implemented in a rapid control prototyping device and validated reproducing real driving maneuvers at the engine test bench. Results of the online validation process are presented at the end of the paper.