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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.

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.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

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.

Magneto-Rheological (MR) Fluid started to be used for industrial applications in the last 20 years, and, from that moment on, innovative uses have been evaluated for different applications to exploit its characteristic of changing yield stress as a function of the magnetic field applied. Because of the complexity of the behavior of the MR fluid, it is necessary to perform lots of simulations, combining multi-physical software capable of evaluating all the material’s characteristics. The paper proposes a strategy capable of quickly verifying the feasibility of an innovative MR system, considering a sufficient accuracy of the approximation, able to easily verify the principal criticalities of the innovative applications concerning the MR fluid main electromagnetic and fluid-dynamic capabilities.

The ongoing global demand for greater energy efficiency plays an essential role in the automotive industry, as the focus is moving from ICEs to hybrid (HEVs) and electric (EVs) vehicles. New virtual methodologies are necessary to reduce the development effort of these technologies. In this context, the thermal management of the vehicle high voltage battery pack is becoming increasingly important, with significant impact on the vehicle’s range in different environmental scenarios. In this paper, an advanced method is proposed to compute 3D temperature distribution of the cells of a high voltage battery pack for Plug-in Hybrid (PHEV) or full electric (EV) applications. The thermal FE model of a complete PHEV vehicle was integrated with an electrical NTG equivalent circuit model of the HV battery to compute the heat loads of the cells.

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.

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.

In vehicle dynamics, simple and fast vehicle models are required, especially in the framework of real-time simulations and autonomous driving software. Therefore, a trade-off between accuracy and simulation speed must be pursued by selecting the appropriate level of detail and the corresponding simplifying assumptions based on the specific purpose of the simulation. The aim of this study is to develop a methodology for map and parameter estimation from multibody simulation results, to be used for simplified vehicle modelling focused on handling performance. In this paper, maneuvers, algorithms and results of the parameter estimation are reported, together with their integration in single track models with increasing complexity and fidelity. The agreement between the multibody model, used as reference, and four single track models is analyzed and discussed through the evaluation of the correlation index.

As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation.

The aim of this paper is to study in deep the peculiar test-rigs and experimental procedures adopted to the fulfilment of the principal requirements of automotive steel wheels, in particular regarding fatigue damaging. In the discussion, the standard requirements, the OEM specifications and the dimensional and geometric tolerances are approached. As result of an increasingly necessity to improve the performance of the components, innovative virtual test benches are presented. Differently from their traditional precursors, virtual test-rigs give an extended view of the physical behaviour of the component as the possibility to monitor stress-strain distribution in deep. In the first section, the state of the art and the specifications are listed. Secondly, the adopted hardware test-rigs as the experimental tests are described in detail. In the third one, proposed virtual test-rig is discussed.

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools.

Vehicle dynamics is a fundamental part of vehicle performance. It combines functional requirements (i.e. road safety) with emotional content (“fun to drive”, “comfort”): this balance is what characterizes the car manufacturer (OEM) driving DNA. To reach the customer requirements on Ride & Handling, integration of CAE and testing is mandatory. Beside of cutting costs and time, simulation helps to break down vehicle requirements to component level. On chassis, the damper is the most important component, contributing to define the character of the vehicle, and it is defined late, during tuning, mainly by experienced drivers. Usually 1D lookup tables Force vs. Velocity, generated from tests like the standard VDA, are not able to describe the full behavior of the damper: different dampers display the same Force vs. Velocity curve but they can give different feeling to the driver.

The complexity of automotive market, the request of new gearbox layout able to improve the efficiency of a vehicle and the requirement of quick and effective design of gearboxes push the designers to seek new technologies, new layouts, new solutions. The typical development of a gearbox requires a lot of time and engineers' effort and it often implies a lot of time to define the right layout. The idea of developing a "paradigm" able to guide the designer through the design process seems to be effective. Starting from the experience of a code called "Engine Paradigm" where such idea was firstly implemented, the authors propose in the present paper the development of a code able to suggest a first attempt design of a gearbox. The "Gearbox Paradigm" code requires few data introduction, as torque, power, number of gears, some geometrical constraints such as the axes gap the gearbox layout, and the code elaborates a proposal of CAD design of a gearbox.

Nowadays, the 48 V vehicle architecture seems to be the perfect bridge between the 12 V system and the costly High Voltage (HV) electrification towards the crucial goal of CO2 and pollutants emissions reduction in combination with enhanced performance. However, this approach leads to an increased complexity in the interaction between different sub-systems targeting the optimization of the Energy Management System (EMS). Therefore, it becomes essential to perform a preliminary hardware assessment, exploring the interactions between the different components and quantifying the cost vs benefit trade-off. To this purpose, an integrated experimental/numerical methodology has been adopted: a comprehensive map-based Hybrid Electric Vehicle (HEV) model has been built, allowing the simulation of a variety of hybrid architectures, including both HV and 48 V systems.

Automotive manufacturers are facing stronger and stronger pressure to optimize all aspects related to fuel consumption of cars, and aerodynamic drag makes no exception, due to increasing government enforcing rules for the reduction of the emissions and the increasing influence of aerodynamic performance on fuel consumption with WLTC and RDE driving cycles. Nowadays, CFD simulation is a common tool across automotive industries for the assessment and the optimization of vehicle resistance in the design phase. The full power of these numerical methods of studying many design variants in advance of experimental testing, however, can be fully exploited when coupled with optimization techniques, always keeping into account constraints and aesthetical demands. On the other hand, a massive use of CFD optimization can lead to unaffordable computational efforts or a limitation of the design exploration space.

Tire inflation pressure has a relevant impact on fuel consumption and tire wear, and therefore affects both CO2 emissions and the total cost of ownership (TCO). The latter is extremely important in the case of commercial vehicles, where the cost of fuel is responsible for about 30% of the TCO. A possible advanced central tire inflation system, which is able to inflate and deflate tires autonomously, as part of a smart energy management system and as an active safety device, has been studied. This system allows misuse due to underinflation to be avoided and adapts the tires to the current working conditions of the vehicle. For instance, the tire pressure can be adapted according to the carried load or during tire warm-up. An on board software is able to evaluate the working conditions of the vehicle and select the tire pressure that minimizes the energy expense, the TCO, or the braking distance, according to a multi-objective optimization strategy.

Modern vehicles have several active systems on board such as the Electronic Stability Control. Many of these systems require knowledge of vehicle states such as sideslip angle and yaw rate for feedback control. Sideslip angle cannot be measured with the standard sensors present in a vehicle, but it can be measured by very expensive and large optical sensors. As a result, state observers have been used to estimate sideslip angle of vehicles. The current state of the art does not present an algorithm which can robustly estimate the sideslip angle for vehicles with all-wheel drive. A deep learning network based sideslip angle observer is presented in this article for robust estimation of vehicle sideslip angle. The observer takes in the inputs from all the on board sensors present in a vehicle and it gives out an estimate of the sideslip angle. The observer is tested extensively using data which are obtained from proving grounds in high tire-road friction coefficient conditions.

Brake systems represent important components for passenger cars since they are strictly related to vehicle safety: Anti-lock Braking Systems (ABS) and Electronic Stability Control (ESC) are the most well-known examples. The paper is focused on the characterization of the braking hydraulic plant and on the design of a pressure following control strategy. This strategy is aimed at pursuing performances and/or comfort objectives beyond the typical safety task. The low-level logic (focus of the paper) consists of a Feedforward and Proportional Integral controller. A Hardware In the Loop (HIL) braking test bench is adopted for pressure controller validation by providing some realistic reference pressure histories evaluated by a high-level controller. Results prove that innovative control strategies can be applied to conventional braking systems for achieving targets not limited to braking issues, i.e., comfort or NVH tasks.

A model-based approach to control BMEP (Brake Mean Effective Pressure) and NOx emissions has been developed and assessed on a FPT F1C 3.0L Euro VI diesel engine for heavy-duty applications. The controller is based on a zero-dimensional real-time combustion model, which is capable of simulating the HRR (heat release rate), in-cylinder pressure, BMEP and NOx engine-out levels. The real-time combustion model has been realized by integrating and improving previously developed simulation tools. A new discretization scheme has been developed for the model equations, in order to reduce the accuracy loss when the computational step is increased. This has allowed the required computational time to be reduced to a great extent.