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The current trend towards an increasing electrification of road vehicles brings to life a whole series of unprecedent design issues. Among these, the ageing process that affects the lifetime of lithium-ion based energy storage systems is of particular importance since it turns out to be extremely sensitive to the variation of battery operating conditions normally occurring especially in hybrid electric vehicles (HEVs). This paper aims at analyzing the impact of operating conditions on the predicted lifetime of a parallel-through-the-road plug-in HEV battery both from thermal and ageing perspectives. The retained HEV powertrain architecture is presented first and modeled, and the related energy management system is implemented. Dedicated numerical models are also discussed for the high-voltage battery pack that allow predicting its thermal behavior and cyclic ageing.

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.

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.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

Gearbox behavior is strictly affected by gears, shaft, bearings and casing stiffnesses. As a matter of fact, their contribution to gear dynamics is fundamental for mechanical transmissions design. In this paper a semi-analytical model developed for the estimation of the dynamic behavior of two mating gears is presented and tested on two case studies. Starting with the estimation of the Static Transmission Error, intended as the difference between the theoretical and actual angular position between the two mating gears, the dynamic behavior of the mating elements is estimated by means of a Dynamic Model. The Dynamic Model takes into account the gears, the contact between teeth exchanging loads and the other mechanical elements reduced by means of a DOF reduction technique. Based on the block-oriented approach, Dynamic Model allows the user to easily manage the complexity of the system with further or less elements by adding or removing DOFs.

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.

One of the key technologies for the improvement of the diesel engine thermal efficiency is the reduction of the engine heat transfer through the thermal insulation of the combustion chamber. This paper presents a numerical investigation on the effects of the combustion chamber insulation on the heat transfer, thermal efficiency and exhaust temperatures of a 1.6 l passenger car, turbo-charged diesel engine. First, the complete insulation of the engine components, like pistons, liner, firedeck and valves, has been simulated. This analysis has showed that the piston is the component with the greatest potential for the in-cylinder heat transfer reduction and for Brake Specific Fuel Consumption (BSFC) reduction, followed by firedeck, liner and valves. Afterwards, the study has been focused on the impact of different piston Thermal Barrier Coatings (TBCs) on heat transfer, performance and wall temperatures.

Nowadays, injection rate shaping and multi-pilot events can help to improve fuel efficiency, combustion noise and pollutant emissions in diesel engine, providing high flexibility in the shape of the injection that allows combustion process control. Different strategies can be used in order to obtain the required flexibility in the rate, such as very close pilot injections with almost zero Dwell Time or boot shaped injections with optional pilot injections. Modern Common-Rail Fuel Injection Systems (FIS) should be able to provide these innovative patterns to control the combustion phases intensity for optimal tradeoff between fuel consumption and emission levels.

The paper deals with the design and manufacturing of a McPherson suspension arm made from short glass fiber reinforced polyamide (PA66). The design of the arm and the design of the molds have been made jointly. According to Industry 4.0 paradigms, a full digitalization of both the product and process has been performed. Since the mechanical behavior of the suspension arm strongly depends on constraints which are difficult to be modelled, a simpler structure with well-defined mechanical constraints has been developed. By means of such simple structure, the model for the behavior of the material has been validated. Since the suspension arm is a hybrid structure, the associated simple structure is hybrid as well, featuring a metal sheet with over-molded polymer. The issues referring to material flow, material to material contact, weld lines, fatigue strength, high and low temperature behavior, creep, dynamic strength have been investigated on the simple structure.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

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 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.

When developing a new engine control strategy, some of the important issues are cost, resource minimization, and quality improvement. This paper outlines how a model based approach was used to develop an engine control strategy for an Extended Range Electric Vehicle (EREV). The outlined approach allowed the development team to minimize the required number of experiments and to complete much of the control development and calibration before implementing the control strategy in the vehicle. It will be shown how models of different fidelity, from map-based models, to mean value models, to 1-D gas dynamics models were generated and used to develop the engine control system. The application of real time capable models for Hardware-in-the-Loop testing will also be shown.

The sustainable use of energy and the reduction of pollutant emissions are main concerns of the automotive industry. In this context, Hybrid Electric Vehicles (HEVs) offer significant improvements in the efficiency of the propulsion system and allow advanced strategies to reduce pollutant and noise emissions. The paper presents the results of a simulation study that addresses the minimization of fuel consumption, NOx emissions and combustion noise of a medium-size passenger car. Such a vehicle has a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. The simulation reproduces real-driver behavior through a dynamic modeling approach and actuates an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). Typical characteristics of parallel hybrid technologies, such as Stop&Start, regenerative braking and electric power assistance, are implemented via an operating strategy that is based on the reduction of total losses.

Sustainable and energy-efficient consumption is a main concern in contemporary society. Driven by more stringent international requirements, automobile manufacturers have shifted the focus of development into new technologies such as Hybrid Electric Vehicles (HEVs). These powertrains offer significant improvements in the efficiency of the propulsion system compared to conventional vehicles, but they also lead to higher complexities in the design process and in the control strategy. In order to obtain an optimum powertrain configuration, each component has to be laid out considering the best powertrain efficiency. With such a perspective, a simulation study was performed for the purpose of minimizing well-to-wheel CO2 emissions of a city car through electrification. Three different innovative systems, a Series Hybrid Electric Vehicle (SHEV), a Mixed Hybrid Electric Vehicle (MHEV) and a Battery Electric Vehicle (BEV) were compared to a conventional one.

Developing of innovative technologies and materials to meet the requirements of environmental legislation on vehicle emissions has paramount importance for researchers and industries. Therefore, improvement of engine efficiency and fuel saving of modern internal combustion engines (ICEs) is one of the key factors, together with the weight reduction. Thermoplastic composite materials might be one of the alternative materials to be employed to produce engine components to achieve these goals as their properties can be engineered to meet application requirements. Unidirectional carbon fiber reinforced PolyEtherImide (CF/PEI) thermoplastic composite is used to design engine connecting rod and wrist pin, applying commercial engine data and geometries. The current study is focused on some elements of the crank mechanism as the weight reduction of these elements affects not only the curb weight of the engine but the overall structure.

This paper presents the design and experimental validation of a localization method for autonomous driving. The investigated method proposes and compares the application of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to the sensor fusion of onboard data streaming from a Global Positioning System (GPS) sensor and an Inertial Navigation System (INS). In the paper, the design of the hardware layout and the proposed software architecture is presented. The method is experimentally validated in real time by using a properly instrumented all-wheel-drive electric racing vehicle and a compact Sport Utility Vehicle (SUV). The proposed algorithm is deployed on a high-performance computing platform with an embedded Graphical Processing Unit that is mounted on board the considered vehicles.

Urban mobility represents one of the most critical global challenges nowadays. Several options regarding design and power sources technologies were recently proposed; among which electric and hybrid vehicles are quite successful to meet the increasingly restrictive environmental targets. This significant goal may affect the perceived vehicle comfort and drivability, especially in everyday urban scenarios. The purpose of this paper is to carry out a comparison in terms of comfort between vehicles belonging to different categories, but all designed for urban mobility: an electric 2-passenger quadricycle used during the demonstration phase of the European project STEVE, an internal combustion engine 2-passenger car (Smart Fortwo), an electric 4-passenger car (Bolloré Bluecar) and an internal combustion engine 4-passenger car (Fiat 500). Leading European car-sharing services use the last three car models.

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.