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Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

The role of numerical simulations in the development of innovative and sustainable powertrains is constantly growing thanks to their capabilities to significantly reduce the calibration efforts and to point out potential synergies among different technologies. In such a framework, this paper describes the development of a fully physical 1D-CFD engine model to support the calibration of the highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. The availability of a reliable simulation platform is essential to effectively exploit the combination of the several features introduced to achieve the project target of 47% peak gross indicated efficiency, such as SwumbleTM in-cylinder charge motion, Miller cycle combined with high Compression Ratio (CR), lean mixture exploiting cooled low pressure Exhaust Gas Recirculation (EGR) and electrified turbocharging.

The continuous tightening of CO2 emission targets along with the introduction of Real Driving Emissions (RDE) tests make Water Injection (WI) one of the most promising solutions to improve efficiency, enhance performance and reduce emissions of turbocharged high-performance Spark Ignition engines. This technology, by reducing local in-cylinder mixture temperature, enables higher compression ratios, optimal spark timing and stoichiometric combustion over the entire engine operating range. This research activity, therefore, aims to assess the benefits in terms of CO2 emission reduction of a Port Water Injection (PWI) system integrated in a Downsized Turbocharged Direct Injection Spark Ignition (T-DISI) Engine. In this regard, a 1D-CFD model of the engine capable to predict the impact of the water content on both the combustion process and the knock likelihood was firstly developed.

In European Union (EU), new heavy-duty vehicles are simulated with the Vehicle Energy Consumption calculation TOol (VECTO) to certify their fuel consumption and CO2 emissions. VECTO will also be used to certify vehicles with hybrid-electric powertrains in all topological configurations from P0 to P4 parallel systems and series hybrids. A development version of VECTO able to simulate these configurations is already available and was used for this study. The study team collected measurement data from a specific P2 hybrid lorry, instrumented with wheel torque sensors, current and voltage sensors, fuel flow sensor and a PEMS device. The vehicle was tested on the chassis dyno and on the road, and a representative model was created in VECTO. The regional delivery certification cycle was simulated in VECTO in charge sustaining and full electric mode.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

European Union's (EU) Climate Law sets a legally binding target of net-zero greenhouse gas emissions by 2050. EU identified hydrogen technologies as a priority introducing hydrogen-powered propulsion systems into the market. Even though the new registrations of fuel cell (FC) passenger cars increased by 41% in 2020 in Europe, the research community faces a lack of public and independent available data regarding the performance and energy efficiency of state-of-the-art FC electric vehicles. This study introduces a tailored methodology to characterise the different powertrain components and analyse the energy flow for a Fuel Cell Electric Vehicle (FCEV) already available on the market. Experimental data are gathered over different driving conditions, including standard driving cycles such as WLTP and US06 tests performed in a laboratory.

Battery Electric Vehicle (BEV) sales have been spiking up due to a series of factors: zero tailpipe emissions, wider model availability, increased customer acceptance, reduced purchase price, improved performance and range. The latter is a crucial factor the consumers consider when purchasing a BEV, and it largely depends on how the vehicle operates (e.g. average speed), traffic, ambient conditions, and battery size. When driven on the roads, the actual range of BEVs can be significantly smaller than the certified value obtained from laboratory testing at standard conditions. To understand the factors influencing vehicle range in real-world operation, the study team performed on-road tests on three production passenger vehicles currently available in the European market. The measured quantities, including vehicle signals from OBD/UDS, were used to quantify the vehicle energy consumption.

Passenger and light-duty vehicles have a high, and steadily increasing, greenhouse gas emissions footprint. Industry and regulators put effort into new, efficient propulsion configurations to reduce carbon dioxide (CO2) emissions from the transport sector. Energy savings are highly impacted not only by the driving style and needs of the driver, but also by the energy mix used during a trip, making the vehicle efficiency benchmarking increasingly complex. A potential way to curb the vehicle energy demand is by minimising the losses due to factors opposing the forward movement, such as vehicle inertia, tyre deformation, drivetrain, and vehicle air-drag. These losses are included in the vehicle road loads. In the present study, we derive representative road load values by employing open access vehicle information and combining physical and statistical methods. These values are then compared to the ones declared by the manufacturer, which are derived by physical coast down tests.

The use of Ducted Fuel Injection (DFI) for attenuating soot formation throughout mixing-controlled diesel combustion has been demonstrated impressively effective both experimentally and numerically. However, the last research studies have highlighted the need for tailored engine calibration and duct geometry optimization for the full exploitation of the technology potential. Nevertheless, the research gap on the response of DFI combustion to the main engine operating parameters has still to be fully covered. Previous research analysis has been focused on numerical soot-targeted duct geometry optimization in constant-volume vessel conditions. Starting from the optimized duct design, the herein study aims to analyze the influence of several engine operating parameters (i.e. rail pressure, air density, oxygen concentration) on DFI combustion, having free spray results as a reference.

New heavy-duty vehicles are simulated with the Vehicle Energy Consumption Calculation Tool (VECTO) to certify their fuel consumption and CO2 emissions in the European Union. The vehicle manufacturer runs the simulation tool and requires the vehicle components' characteristics to simulate the vehicle over standardized mission profiles. The detailed component characteristics required to run the tool are not always publicly available. In this work, a simplified model was developed to predict the fuel consumption and CO2 emissions of heavy buses over the VECTO mission profiles. It requires only the basic vehicle properties as input, such as the air drag, tire rolling resistance, mass, drivetrain efficiency and auxiliary power use. The model was derived from detailed VECTO simulations of numerous variants of a diesel high floor bus, a diesel low floor bus and a CNG low floor bus.

In the framework of an increasing demand for a more sustainable mobility, where the fuel consumption reduction is a key driver for the development of innovative internal combustion engines, Turbulent Jet Ignition (TJI) represents one of the most promising solutions to improve the thermal efficiency. However, details concerning turbulent jet assisted combustion are still to be fully captured, and therefore the design and the calibration of efficient TJI systems require the support of reliable simulation tools that can provide additional information not accessible through experiments. To this aim, an experimental investigation combined with a 3D-CFD study was performed to analyze the TJI combustion characteristics in a single-cylinder spark-ignition (SI) engine. Firstly, the model was validated against experiments considering stoichiometric mixture at 3000 rpm, wide open throttle operating conditions.

Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data.

The European Union road transport CO2 emissions regulation foresees mandatory targets for passenger vehicles. However, several studies have shown that there is a divergence between official and real-world values that could range up to 40% compared to the NEDC reference value. The introduction of the Worldwide Harmonized Test Protocol (WLTP) limited this divergence, but it is uncertain whether it can adequately address the problem, particularly considering future evolutions of vehicle technology. In order to address this issue, the recent EU CO2-standards regulation introduces the monitoring of on-road fuel consumption and subsequently CO2 emissions by utilizing On-Board Fuel Consumption Meters (OBFCM). In the near future, all vehicles should provide instantaneous and lifetime-cumulative fuel consumption signals at the diagnostics port. Currently, the fuel consumption signal is not always available.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm.

Waste heat recovery based on an Organic Rankine Cycle is a technology proposed for the reduction of the fuel consumption of heavy-duty vehicles. This technology is currently not simulated by VECTO, the tool used in Europe to certify the fuel consumption and CO2 emissions of new heavy-duty vehicles. In this work, a class 5 lorry equipped with a prototype Organic Rankine Cycle system is tested on the chassis dyno during steady state and transient driving cycles, with the waste heat recovery enabled and disabled. The waste heat recovery system enabled a brake specific fuel consumption reduction of 3.1% over the World Harmonized Vehicle Cycle, 2.5% during the official EU Regional Delivery Cycle, and up to 6.5% at certain engine operating points during the fuel consumption mapping cycle. A model of the vehicle was created in VECTO based on the experimental data. The fuel consumption map of the engine with and without the Organic Rankine Cycle was derived from the steady-state experiments.

A vital element of any vehicle-certification test is the use of representative values for the vehicle resistance forces. In most certification procedures, including the WLTP recently adopted by the EU, the latter is achieved mainly through coast down tests. Subsequently, the resistance values measured are used for setting up the chassis-dyno resistances applied during the laboratory measurements. These reference values are obtained under controlled conditions, while a series of corrections are applied to make the test procedure more repeatable and reproducible. In real driving, the actual vehicle road loads are influenced by a series of factors leading to a divergence between the certified fuel consumption values, and the real-world ones. An approach of calculating representative road loads during on-road tests can help to obtain a more unobstructed view of vehicle efficiency and, when needed, confirm the officially declared road loads.

The high level of electric power available on a Hybrid Electric Vehicle (HEV) enables the introduction of electrical auxiliaries in addition or in substitution to the ones currently available on a conventional powertrain. Among these auxiliaries, electric Superchargers (eSC) for the improvement of the vehicle performance or electrically heated catalysts for the reduction of the light-off time of the after-treatment may dramatically affect the Energy Management System (EMS) of an HEV. Moreover, since these devices are only fluid-dynamically, but not mechanically, linked to the powertrain, they are traditionally neglected in the optimization of the powersplit between internal combustion engine and electric machines by the EMS. The aim of the current work is the development of an EMS that is able to consider in real time the overall electric energy consumption of the entire powertrain.