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Particulate Matter (PM) filters are becoming a standard component of Diesel engines exhaust aftertreatment devices to comply with the forthcoming engine emission regulations. However, cost reduction and durability are still critical issues in particular for the integration of the PM-filter with other components of the after-treatment system (e.g. pre-turbo-catalyst, close-coupled-catalyst, PM-filter, SCR). To respect functional (available temperature and gas composition) and space restraints, very complex shapes may result from the design causing tortuous flow patterns and influencing the flow distribution into the PM-filter. Uneven soot distributions in the filter may cause a non-homogeneous development of filter regeneration, leading to failures, for example due to the occurrence of large temperature gradients during the oxidation of soot deposits.

Computational Fluid Dynamics (CFD) surely has played a fundamental role in the design of the bodies and shapes of both commercial and racing vehicles in the last decades. This circumstance was mainly due to the connected substantial improvement in the design timings and to the possibility of producing numerous flow field and surface data that are difficult to obtain from a physical experimental method. Such a local analysis leads to a further understanding of the interactions of components with the overall aerodynamics. The development of wing performances, with respect to racing vehicles, has to deal with very short times but also with a very detailed description of the physics occurring. Starting from these constraints, a coupling procedure was developed by the joining of a CFD code with a Finite Element Method (FEM) structural code to better evaluate the aerodynamic performance of the wing deformed under the fluid dynamic loads.

Emission control efficiency and limited fuel consumption penalty and are the main design factors driving the development of engine-after-treatment exhaust systems according to both ACEA/DOE targets and continental regulations. The particulate-filter is certainly a critical technology to this aim as usually presents very high pm reduction efficiencies (even more than 90% on a mass basis depending on soot loading) leading however to a back pressure increase and eventually to an appreciable fuel consumption penalty. Nevertheless, it is in general discussion that health hazard related to particulate depends primarily on total number of emitted particles rather than on mass. The partial-flow-filter has been recently developed presenting lower reduction efficiencies on a mass basis but also a reduced penalty on fuel consumption.

Metal based Diesel Particulate Filters (PM-TRAPs) could represent a short time solution to face with particulate (and NOx) emissions with a small influence on CO2 emission. In fact, the operation principle of the PM-TRAP, based on fluid dynamical behavior of exhaust flow in “ad hoc” shaped geometries, allows to separate the particle content of exhaust-gases but needs to be carefully assessed to optimize filter performances. In this paper a mixed numerical and experimental procedure has been developed; it allows to finely tune the design parameters which can be used to achieve pre-defined targets in terms of particulate matter and fuel consumption. By adopting the previously declared procedure, a PM-TRAP “optimal” geometry has been chosen. Its performance has been verified with respect to experimental data. Results are encouraging and suggest further development of the system.

Diesel particulate filters (DPFs) are recognized as the most efficient technology for particulate matter (PM) reduction, with filtration efficiencies in excess of 90%. Design guidelines for DPFs typically are: high removal efficiency, low pressure drop, high durability and capacity to resist high temperature excursions during regeneration events. The collected mass inside the trap needs to be periodically oxidized to regenerate the DPF. Thus, an in-depth understanding of filtration and regeneration mechanisms, together with the ability of predicting actual DPF conditions, could play a key role in optimizing the duration and number of regeneration events in case of active DPFs. Thus, the correct estimation of soot loading during operation is imperative for effectively controlling the whole engine-DPF assembly and simultaneously avoidingany system failure due to a malfunctioning DPF. A viable way to solve this problem is to use DPF models.

The correct simulation of combustion process allows to better face several SI engines design problems, not only for innovative mixture formation concepts (stratified or ultra-lean charge), but for traditional homogeneous mixture as well. Even though many commercial codes are able to describe the complex 3-D non reacting fluid dynamics in ICE, the simulation of high turbulent flame propagation does not seem to be a completely solved problem yet. In this work a comparison between two different turbulent combustion models (a characteristic time based one by Abraham and Reitz [2, 15, 16] and a flamelet based one by Cant and AbuOrf [4, 20]) has been performed using KIVA-3V code to assess simulation reliability. Models predictive capabilities have been tested with reference to specific data acquired at the engine test bench of Tor Vergata Mechanical Engineering Department on a Fiat Punto 1242 cc 8 valves SI engine over a wide range of operating conditions.

Conversion from diesel to dual fuel (diesel and natural gas) operation may represent an attractive retrofit technique to get a better PM-NOx trade-off in a diesel engine, with no major modifications of the original design. In the proposed paper, an Euro 2 heavy duty diesel engine, converted for dual fuelling, has been studied and tested to reduce pollutant emissions. Throttled stoichiometric with EGR and lean burn technologies have been selected as control strategies. A mixed experimental-numerical approach has been utilized to analyze the engine behavior by varying key operating conditions such as throttling, natural gas/diesel oil percentage and EGR. The model, based on a 3D approach, has been used mainly to understand the evolution of the distribution of the most important parameters in the combustion chamber.

The international community is making significant efforts to face climate changes related to substantial greenhouse gas (GHG) emissions. Among all the sectors, transport is responsible for almost a quarter of global GHG emissions, 72% of which is imputable to road vehicles. It’s also expected that, without significant measures, these emissions may grow at a faster rate than other sectors. Furthermore, rising fuel costs and availability concerns have made the electrification of road transportation an attractive option to reduce oil dependency. However, this solution produces an electricity demand increase, causing significant overload conditions that could affect the reliability of the distribution sector.

The development of catalytic converter systems for automotive applications is, to a great extent, related to monolith catalyst support materials and design. In this paper improvements of converter channels fluid-dynamics aiming to enhance pollutant conversion in all the engine operating conditions are investigated with respect to the role of channel cross-section shape on mass and heat transfer processes. The performances of different channel sections, characteristic of ceramic and metallic monoliths, have been compared by two strategies (respectively equal cell density and equal hydraulic diameter). The results have been examined in terms of mass conversion efficiency, thermal behavior and single channel backpressure for coated and non coated single channels. 3D numerical simulations have been used as an analysis tool to give a detailed insight of in-channel phenomena. Classical shapes have been analyzed and their relative performances are reported.

The use of biodiesels is an effective way to limit greenhouse emissions and partly limit the dependence on fossil primary sources. Biodiesel fuels also show interesting features in terms of PM-NOx emissions trade-off that appears more favorable toward an optimized control of the Diesel Particulate Filter (DPF). In fact, the DPF, which is the assessed aftertreatment technology to reduce PM emissions below the limits, suffers from fuel consumption penalization or excessive exhaust system backpressure, as a function of the frequency of the regeneration process. On the other side, issues such as the impact of the higher ash content of biodiesel on the DPF performance have also to be better understood. In the given scenario, an experimental study on a DEUTZ 4L off-road Diesel engine coupled to a DOC-DPF (Diesel Oxidation Catalyst-Diesel Particulate Filter) system is proposed in this paper.

Gasoline Direct Injection (GDI) is a leading technology for Spark Ignition (SI) engines: control of the injection process is a key to design the engine properly. The aim of this paper is a numerical investigation of the gasoline injection and the resulting development of plumes from an 8-hole Spray G injector into a quiescent chamber. A LES approach has been used to represent with high accuracy the mixing process between the injected fuel and the surrounding mixture. A Lagrangian approach is employed to model the liquid spray. The fuel, considered as a surrogate of gasoline, is the iso-octane which is injected into the high-pressure vessel filled with nitrogen. The numerical results have been compared against experimental data realized in the optical chamber. To reveal the geometry of plumes two different imaging techniques have been used in a quasi-simultaneous mode: Mie-scattering for the liquid phase and schlieren for the gaseous one.

The use of biodiesel has been widely accepted as an effective solution to reduce greenhouse emissions. The high potential of biodiesel in terms of PM emission reduction may represent an additional motivation for its wide use. This potential is related to the oxygenated nature of biodiesel, as well as its lower PAH and S, which leads, in general, to lower PM emissions as well as equal or slightly higher NOx emissions. According to these observations a different behavior of the Aftertreatment System (AS), especially as far as control issues of the Diesel Particulate Filter are concerned is also expected. The competition with the food sector is currently under debate, thus, besides second generation biofuels (e.g. from algae), the transesterification of Waste Cooking Oil (WCO) is another option, however needing further insight.

Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption. Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors. One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.

The design of compact and efficient Diesel Oxidation Catalysts (DOC) is primarily important to comply with emission regulations not increasing engine fuel consumption at the same time. To design DOCs, Sherwood number correlations are typically used to calculate mass transfer by varying operating conditions in terms of catalyst volume, active area and mass flow rate. To that aim, Sherwood number trend over channel length has been extensively studied during last decades. However, Sherwood number correlations are highly dependent on channel geometry, and on the possible presence of special structures (such as blades, fins or bumps). These modifications, which characterize the latest developments in substrate technology, allow to improve mass transfer performance and require a special characterization.

Lean burn natural gas engines have high potential in terms of efficiency and NOx emissions in comparison with stoichiometric natural gas engines, and much lower particulate emissions than diesel engines. They are a promising solution to meet the increasingly stringent exhaust emission targets for both light and heavy-duty engines. Partially Stratified-Charge (PSC) is a novel concept which was conceived by prof. Evans (University of British Columbia, Vancouver). This technique allows to further limit pollutant emissions and improve efficiency of an otherwise standard spark-ignition engine fuelled by natural gas, operating with lean air-fuel ratio. The potential of the PSC technique lies in the control of load without throttling by further extending the lean flammability limit.

Hydrogen technologies have been widely recognized as effective means to reduce Greenhouse Gases emissions, a crucial issue to target a Carbon-free world aimed by the European Green Deal. Within the road transport sector, electric vehicles with a hybrid powertrain, including battery packs and hydrogen Fuel Cells (FCs), are gaining importance owing to their adaptability to a wide variety of applications, high driving mileages and short refueling times. The control strategy is crucial to achieve a proper management of the energy flows, to maximize energy efficiency and maximize components durability and state of health. This work is focused on the design of an integrated Energy Management Strategy (EMS), whose aim is to minimize the hydrogen consumption, by operating the FC mainly in the high efficiency region while the battery pack works according to a charge sustaining mode. The proposed EMS is composed of a control algorithm and a supervisor.

Hydrogen technologies are among the main candidates to reduce carbon emissions in the automotive transport sector. Among the innovative solutions, Electric Vehicles (EVs) featuring hybrid powertrains, combining battery packs and hydrogen Fuel Cell (FC) stacks, are gaining prominence in our pursuit of sustainability objectives. Nonetheless, realizing the full potential of these hybrid vehicles hinges on the implementation of efficient Energy Management Strategies (EMS). In this study, we present an integrated EMS approach to achieve extended driving ranges and reduced energy consumption. This is achieved primarily by operating the FC within its high-efficiency range, while ensuring that the battery packs operate in a charge-sustaining mode. The EMS is crafted through an adaptive algorithm that takes into account various driving conditions to establish the most suitable sub-optimal control strategy.

According to European Union strategies, hydrogen technologies have a significant potential for the decarbonization of the automotive sector. Fuel Cells are considered a highly sustainable alternative to internal combustion engines for hybrid powertrain solutions. Since experimental tests on real prototypes are extremely costly in terms of time and resources, they represent a limit to the development rapidity of such complex vehicles. Consequently, simulation models are gaining further importance for their intrinsic time- and cost-saving characteristics, while their predictive capability is crucial. Accordingly, the development of the so-called “digital twins” able to accurately represent the real-time digital counterpart of a physical system has become an important research issue.

In the face of the pressing climate crisis, a pivotal shift towards sustainability is imperative, particularly in the transportation sector, which contributed to nearly 22% of global Greenhouse Gas emissions in 2021. In this context, diversifying energy sources becomes paramount to prevent the collapse of sustainable infrastructure and harness the advantages of various technologies, such as Fuel Cell (FC) Hybrid Electric Vehicles. These vehicles feature powertrains comprising hydrogen FC stacks and battery packs, offering extended mileage, swift refueling times, and rapid dynamic responses. However, realizing these benefits hinges upon the adoption of a rigorously validated simulation platform capable of accurately forecasting vehicle performance across diverse design configurations and efficient Energy Management Strategies. Our study introduces a comprehensive microcar hybrid prototype model, encompassing all subsystems and auxiliaries.

Gasoline Full Hybrid Electric Vehicles (FHEVs) are considered among good candidates as cost-effective solution to comply with upcoming emissions legislation. However, several studies have highlighted that frequent start-and-stops worsen the hydrocarbon tailpipe emissions, especially when the light-off temperature of the three-way catalyst (TWC) has not been reached. In fact, strategies only addressing the minimization of fuel consumption tend to delay engine activation and hence TWC warming, especially during urban driving. Goal of the present research is therefore to develop an on-line powertrain management strategy accounting also for TWC temperature, in order to reduce the time needed to reach TWC light-off temperature. A catalyst model is incorporated into the model of the powertrain where torque-split is performed by an adaptive equivalent consumption minimization strategy (a-ECMS).