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Hydrogen has been identified as a promising decarbonization fuel in internal combustion engine (ICE) applications in many areas including heavy-duty on- and off-road, power-generation, marine, etc. Hydrogen ICEs can achieve high power density and very low tailpipe emissions. However, there are challenges; designing systems for a gaseous fuel with its own specific mixing, burn rate and combustion control needs, which can differ from legacy products. The primary pollutant of concern for Hydrogen ICEs is NOx which can be addressed by running the engine at very lean equivalence ratios and the use of Exhaust Gas Recirculation (EGR). Computation Fluid Dynamics (CFD) is a valuable tool to model the combustion characteristics under different conditions, as presented in SAE-2023-01-0197 [1], which can also be used to predict thermal loading.

For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.