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By building on mature internal combustion engine (ICE) hardware combined with dedicated hydrogen (H2) technology, the H2-ICE has excellent potential to accelerate CO2 reduction. H2-ICE concepts can therefore contribute to realizing the climate targets in an acceptable timeframe. In the landscape of H2-ICE combustion concepts, High Pressure Direct Injection (HPDI™) is an attractive option considering its high thermal efficiency, wide load range and its applicability to on-road as well as off-road heavy-duty equipment. Still, H2-HPDI is characterized by diffusion combustion, giving rise to significant NOx emissions. In this paper, the potential of H2-HPDI toward compliance with future emissions legislation is explored on a 1.8L single-cylinder research engine. With tests on multiple load-speed points, Exhaust Gas Recirculation (EGR) was shown to be an effective measure for reducing engine-out NOx, although at the cost of a few efficiency points.

Lean-burn hydrogen internal combustion engines are a good option for future transportation solutions since they do not emit carbon-dioxide and unburned hydro-carbons, and the emissions of nitric-oxides (NOx) can be kept low. However, under lean-burn conditions the combustion duration increases, and the combustion stability decreases, leading to a reduced thermal efficiency. Turbulent jet ignition (TJI) can be used to extend the lean-burn limit, while decreasing the combustion duration and improving combustion stability. The objective of this paper is to investigate the feasibility of a passive pre-chamber TJI system on a heavy-duty hydrogen engine under lean-burn conditions using CFD modelling. The studied concept is mono-fuel, port-fuel injected, and spark ignited in the pre-chamber. The overall design of the pre-chamber is discussed and the effect of design parameters on the engine performance are studied.

Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great interest for the transport sector, since they combine high fuel efficiency with last mile full-electric driving. We focus on a specific use case: PHEV operation through future Zero Emission (ZE) zones of cities. As a first step towards self-learning control, we introduce a novel, adaptive supervisory controller that combines modular energy and emission management (MEEM) and deals with varying constraints and system uncertainty. This optimal control strategy is based on Pontryagin’s Minimum Principle and maximizes overall energy efficiency.

A model-based control approach is proposed to give proper reference for the feed-forward combustion control of Partially Pre-mixed Combustion (PPC) engines. The current study presents a simplified first principal model, which has been developed to provide a base estimation of the ignition properties. This model is used to describe the behavior of a single-cylinder heavy-duty diesel engine fueled with a mix of bio-butanol and n-heptane (80vol% bio-butanol and 20 vol% n-heptane). The model has been validated at 8 bar gross Indicated Mean Effective Pressure (gIMEP) in PPC mode. Inlet temperature and pressure have been varied to test the model capabilities. First the experiments were conducted to generate reference points with BH80 under PPC conditions. And then CFD simulations were conducted to give initial parameter set up, e.g. fuel distribution, zone dividing, for the multi-zone model.

At present, the hydrogen combustion engine has gained renewed interest from the heavy-duty internal combustion engine (ICE) industry as an enabler for fast decarbonization of well-to-wheel emissions and reinforced by the vast commitment of key stakeholders to establish a green hydrogen infrastructure. Past studies have often focused on partial substitution of the primary hydrocarbon fuel by hydrogen in spark ignition and compression ignition engines. Studied 100% hydrogen combustion engines are dominantly of the premixed spark ignition type using port fuel hydrogen injection. In this study, a wider look at other hydrogen ICE concepts has been taken that may bear high potential to overcome some of the limitations of using hydrogen for high power applications. The studied concepts vary from port injection to direct injection of hydrogen and from spark ignition to compression ignition.

Reactivity Controlled Compression Ignition (RCCI) combines very high thermal efficiencies with ultra-low engine out NOx and PM emissions. Moreover, it enables the use of a wide range of fuels. As this pre-mixed combustion concept relies on controlled auto-ignition, closed-loop combustion control is essential to guarantee safe and stable operation under varying operating conditions. This work presents a coordinated air-fuel path controller for RCCI operation in a multi-cylinder heavy-duty engine. This is an essential step towards real-world application. Up to now, transient RCCI studies focused on individual cylinder control of the fuel path only. A systematic, model-based approach is followed to design a multivariable RCCI controller. Using the Frequency Response Function (FRF) method, linear models are identified for the air path and for the combustion process in the individual cylinders.

Existing gasoline DI injection equipment has been modified to generate single hole pulsed gas jets. Injection experiments have been performed at combinations of 3 different pressure ratios (2 of which supercritical) respectively 3 different hole geometries (i.e. length to diameter ratios). Injection was into a pressure chamber with optical access. Injection pressures and injector hole geometry were selected to be representative of current and near-future DI natural gas engines. Each injector hole design has been characterized by measuring its discharge coefficient for different Re-levels. Transient jets produced by these injectors have been visualized using planar laser sheet Mie scattering (PLMS). For this the injected gas was seeded with small oil droplets. The corresponding flow field was measured using particle image velocimetry (PIV) laser diagnostics.

This study was conducted to resolve discrepancies and fill in gaps in the biomechanical impact response of the human abdomen to frontal impact loading. Three types of abdominal loading were studied: rigid-bar impacts, seatbelt loading, and close-proximity (out-of-position) airbag deployments. Eleven rigid-bar free-back tests were performed into the mid and upper abdomens of unembalmed instrumented human cadavers using nominal impact speeds of 6 and 9 m/s. Seven fixed-back rigid-bar tests were also conducted at 3, 6, and 9 m/s using one cadaver to examine the effects of body mass, spinal flexion, and repeated testing. Load-penetration corridors were developed and compared to those previously established by other researchers. Six seatbelt tests were conducted using three cadavers and a peak-loading rate of 3 m/s. The seatbelt loading tests were designed to maximize belt/abdomen interaction and were not necessarily representative of real-world crashes.

This study continued the biomechanical investigations of forearm fractures caused by direct loading of steering-wheel airbags during the early stages of deployment. Twenty-four static deployments of driver airbags were conducted into the forearms of unembalmed whole cadavers using a range of airbags, including airbags that are depowered as allowed by the new federal requirements for frontal impact testing. In general, the depowered airbags showed a reduction in incidence and severity of forearm fractures compared to the pre-depowered airbags tested. Data from these twenty-four tests were combined with results from previous studies to develop a refined empirical model for fracture occurrence based on Average Distal Forearm Speed (ADFS), and a revised value for fifty-percent probability of forearm-bone fracture of 10.5 m/s. Bone mineral content, which is directly related to forearm tolerance, was found to be linearly related to arm mass.