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Ammonia has emerged as a promising carbon-free alternative fuel for internal combustion engines (ICE), particularly in large-bore engine applications. However, integrating ammonia into conventional engines presents challenges, prompting the exploration of innovative combustion strategies like dual-fuel combustion. Nitrous oxide (N2O) emissions have emerged as a significant obstacle to the widespread adoption of ammonia in ICE. Various studies suggest that combining exhaust gas recirculation (EGR) with adjustments in inlet temperature and diesel injection timing can effectively mitigate nitrogen oxides (NOx) emissions across diverse operating conditions in dual-fuel diesel engines.

Hydrogen engines are currently considered as a viable solution to preserve the internal combustion engine as a power unit for vehicle propulsion. In particular, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations due to the reduced emission levels and high thermodynamic efficiency. This strategy is suitable for the purpose of passenger car applications and cannot be tailored in the field of high performance engine, where the air mass delivered would require oversized turbocharging systems or more complex charging solutions. For this reason, the range of stoichiometric feeding condition is explored in the high performance engine, leading to the consequent issue of abatement of pollutant emissions. In this work a 1D model will be applied to the modeling of a V8 engine fueled with DI of hydrogen. The engine has been derived by a gasoline configuration and adapted to hydrogen in such a way to keep the same performance.

Given the growing interest in improving the efficiency of the bus fleet in public transportation systems, this paper presents an analysis of the energy consumption of a battery electric bus. During the experimental campaign, a battery electric bus was loaded using sand payloads to simulate the passenger load on board and followed another bus during regular service. Data related to the energy consumed by various bus utilities were published on the vehicle’s CAN network using the FMS standard and sampled at a frequency of 1 Hz. The collected experimental data were initially analyzed on a daily basis and then on a per-route basis. The results reveal the breakdown of energy consumption among various utilities over the course of each day of the experiment, highlighting those responsible for the highest energy consumption.

Polymer electrolyte membrane (PEM) fuel cells will play a crucial role in the decarbonization of the transport sector, in particular for heavy duty applications. However, performance and durability of PEMFC stacks is still a concern especially when operated under high power density conditions, as required in order to improve the compactness and to reduce the cost of the system. In this context, the optimization of the geometry of hydrogen and air distributors represents a key factor to improve the distribution of the reactants on the active surface, in order to guarantee a proper water management and avoiding membrane dehydration.

Roundabouts are intersections at which automated cars seem currently not performing sufficiently well. Actually, sometimes, they get stuck and the traffic flow is seriously reduced. To overcome this problem a V2N-N2V (vehicle-to-network-network-to-vehicle) communication scheme is proposed. Cars communicate via 5G with an edge computer. A cooperative machine-learning algorithm orchestrates the traffic. Automated cars are instructed to accelerate or decelerate with the triple aim of improving the traffic flow into the roundabout, keeping safety constraints, and providing comfort for passengers on board of automated vehicles. In the roundabout, both automated cars and human-driven cars run. The roundabout scenario has been simulated by SUMO. Additionally, the scenario has been reconstructed into a dynamic driving simulator, with a real human driver in a virtual reality environment. The aim was to check the human perception of traffic flow, driving safety and driving comfort.

The abatement of carbon dioxide and pollutant emissions on motorbike spark-ignition (SI) engines is a challenging task, considering the small size, the low cost and the high power-to-weight ratio required by the market for such powertrain. In this context, the passive pre-chamber (PPC) technology is an attractive solution. The combustion duration can be reduced by igniting the air-fuel mixture inside a small volume connected to the cylinder, unfolding the way to high engine efficiencies without penalization of the peak performance. Moreover, no injectors are needed inside the PPC, guaranteeing a cheap and fast retrofitting of the existing fleet. In this work, a 3D computational fluid dynamics (CFD) investigation is carried out over an experimental configuration of motorbike SI engine, operated at fixed operating conditions with both traditional and PPC configurations.

Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map). KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine.

Lithium-ion batteries have a well-documented failure tendency under abuse conditions with a significant release of gases and heat. This failure originated from the decomposition reactions within the battery’s electrochemical components, resulting in gas generation and increased internal pressure. To optimize battery safety, it is crucial to understand their behaviors when subjected to abuse conditions. The 18650 format cell incorporates a vent mechanism within a crimped cap to relieve pressure and mitigate the risk of rupture. However, cell venting introduces additional safety concerns associated with flammable gases and liquid electrolyte that flow into the environment. Experiments were performed with two venting caps with well-known geometries to quantify key parameters in describing the external dynamic flow of battery venting and to validate a CFD model.

The target of the upcoming automotive emission regulations is to promote a fast transition to near-zero emission vehicles. As such, the range of ambient and operating conditions tested in the homologation cycles is broadening. In this context, the proposed work aims to thoroughly investigate the potential of post-oxidation phenomena in reducing the light-off time of a conventional three-way catalyst. The study is carried out on a turbocharged four-cylinder gasoline engine by means of experimental and numerical activities. Post oxidation is achieved through the oxidation of unburned fuel in the exhaust line, exploiting a rich combustion and a secondary air injection dedicated strategy. The CFD methodology consists of two different approaches: the former relies on a full-engine mesh, the latter on a detailed analysis of the chemical reactions occurring in the exhaust line.

Air pollution is a significant environmental issue, and exhaust emissions from internal combustion engines are one of the primary sources of harmful pollutants. The transportation sector, which includes road vehicles, contributes to a large share of these emissions. In Europe, the latest emission legislation (Euro 7) proposes more stringent limits and testing conditions for vehicle emissions. To meet these limits, the automotive industry is actively developing innovative exhaust emission-control technologies. With the growing prevalence of electrification, internal combustion engines are subject to continuous variations in load and engine speed, including phases where the engine is switched off. The result is an operating condition characterized by successive cold starts. In this context, the challenge in coping with the emission limits is to minimize the light-off time and prevent fast light-out conditions during idling or city driving.

The need to control global warming by regulating automotive emission levels has led to a lot of changes in the policies of different countries globally, specifically the United States (US) and the European Union (EU). More recently, the governments have been strongly pushing the integration of Electric Vehicles (EVs) in the market to replace the conventional Internal Combustion Engine (ICE) vehicles for CO₂ emissions reduction, with the enforcement of 50% EV sales by 2030 in the US and complete 100% by 2035 in the EU for passenger cars. However, these policies are misleading by considering EVs as zero emission vehicles, as there is no such technology yet available that has zero emissions during its lifecycle. During the manufacturing phase, any vehicle produced gives out emissions, with EVs emitting even higher than the conventional ICE vehicles with their battery manufacturing.

Thermal runaway is a critical safety concern in lithium-ion battery systems, emphasising the necessity to comprehend its behaviour in various modular setups. This research compares thermal runaway propagation in different modular configurations of lithium-ion batteries by analysing parameters such as cell spacing and distribution, application of phase change materials (PCMs), and implementing insulating materials. The study at the module level includes experimental validation and employs a comprehensive model considering heat transfer due to electrical performance and thermal runaway phenomena. It aims to identify the most effective modular configuration for mitigating thermal runaway risks and enhancing battery safety. The findings provide valuable insights into the design and operation of modular lithium-ion battery systems, guiding engineers and researchers in implementing best practices to improve safety and performance across various applications.

The increasing awareness on the harmful effects on the environment of traditional Internal Combustion Engines (ICE) is driving the industry toward cleaner powertrain technologies such as battery-driven Electric Vehicles. Nonetheless, the high energy density of Li-Ion batteries can cause strong exothermic reactions under certain conditions that can lead to catastrophic results, called Thermal Runaway (TR). Hence, a strong effort is being placed on understanding this phenomena and increase battery safety. Specifically, the vented gases and their ignition can cause the propagation of this phenomenon to adjancent batteries in a pack. In this work, Computational Fluid Dynamics (CFD) are employed to predict this venting process in a LG18650 cylindrical battery. The ejection of the generated gases was considered to analyze its dispersion in the surrounding volume through a Reynolds-Averaged Navier-Stokes (RANS) approach.

The reduction of the catalyst light-off time at the engine cold start represents a key factor for the pollutant emissions control from vehicles tested on homologation cycles and real drive conditions. The adoption of heating strategies to increase the temperature of the catalytic substrate in the early phase of the engine start is regarded as a promising solution. The present study focuses on the application of electrical heated catalyst (EHC) in an after-treatment line for a spark-ignition gasoline engine. The analysis is carried out by means of an advanced CFD framework, which includes the modeling of catalytic reactions in the substrates and accounts for the thermal evolution of all the components included in the after-treatment system.

By analyzing the dynamic distortion in all body closure openings in a complete vehicle, a better understanding of the body characteristics can be achieved compared to traditional static load cases such as static torsional body stiffness. This is particularly relevant for non-traditional vehicle layouts and electric vehicle architectures. The body response is measured with the so-called Multi Stethoscope (MSS) when driving a vehicle on a rough pavé road (cobble stone). The MSS is measuring the distortion in each opening in two diagonals. During the virtual development, the distortion is described by the relative displacement in diagonal direction in time domain using a modal transient analysis. The results are shown as Opening Distortion Fingerprint ODF and used as assessment criteria within Solidity and Perceived Quality. By applying the Principal Component Analysis (PCA) on the time history of the distortion, a Dominant Distortion Pattern (DDP) can be identified.