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The recent advance in the development of various hybrid vehicle technologies comes along with the need of establishing optimal energy management strategies, in order to minimize both fuel economy and pollutant emissions, while taking into account an increasing number of state and control variables, depending on the adopted hybrid architecture. One of the objectives of this research was to establish benchmarking performance, in terms of fuel economy, for real time on-board management strategies, such as ECMS (Equivalent Consumption Minimization Strategy), whose structure has been implemented in a SIMULINK model for different hybrid vehicle concepts.

This paper presents an optimization algorithm, based on discrete dynamic programming, that aims to find the optimal control inputs both for energy and thermal management control strategies of a Plug-in Hybrid Electric Vehicle, in order to minimize the energy consumption over a given driving mission. The chosen vehicle has a complex P1-P4 architecture, with two electrical machines on the front axle and an additional one directly coupled with the engine, on the rear axle. In the first section, the algorithm structure is presented, including the cost-function definition, the disturbances, the state variables and the control variables chosen for the optimal control problem formulation. The second section reports the simplified quasi-static analytical model of the powertrain, which has been used for backward optimization. For this purpose, only the vehicle longitudinal dynamics have been considered.

Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed.

This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model.

In order to reduce both polluting emissions and fuel costs, many countries allow mixing ethanol to gasoline either in fixed percentages or in variable percentages. The resulting fuel is labeled E10 or E22, where the number specifies the ethanol percentage. This operation significantly changes way the stoichiometric value, which is the air-to-fuel mass ratio theoretically needed to completely burn the mixture. Ethanol concentration must be correctly estimated by the Engine Management System to optimally control exhaust emissions, fuel economy and engine performance. In fact, correct fuel quality recognition allows estimating the actual stoichiometric value, thus allowing the catalyst system to operate at maximum efficiency in any engine working point. Moreover, also other essential engine control functions should be adapted in real time by taking into account the quality of the fuel that is being used.

The recent search for extremely efficient spark-ignition engines has implied a great increase of in-cylinder pressure and temperature levels, and knocking combustion mode has become one of the most relevant limiting factors. This paper reports the main results of a specific project carried out as part of a wider research activity, aimed at modelling and real-time controlling knock-induced damage on aluminum forged pistons. The paper shows how the main damage mechanisms (erosion, plastic deformation, surface roughness, hardness reduction) have been identified and isolated, and how the corresponding symptoms may be measured and quantified. The second part of the work then concentrates on understanding how knocking combustion characteristics affect the level of induced damage, and which parameters are mainly responsible for piston failure.

This paper presents simulation and experimental results of the effects of intake water injection on the main combustion parameters of a turbo-charged, direct injection spark ignition engine. Water injection is more and more considered as a viable technology to further increase specific output power of modern spark ignition engines, enabling extreme downsizing concepts and the associated efficiency increase benefits. The paper initially presents the main results of a one-dimensional simulation analysis carried out to highlight the key parameters (injection position, water-to-fuel ratio and water temperature) and their effects on combustion (in-cylinder and exhaust temperature reduction and knock tendency suppression). The main results of such study have then been used to design and conduct preliminary experimental tests on a prototype direct-injection, turbocharged spark ignition engine, modified to incorporate a new multi-point water injection system in the intake runners.

Pre-ignition combustions are extremely harmful and undesired, but the recent search for extremely efficient spark-ignition engines has implied a great increase of the in-cylinder pressure and temperature levels, forcing engine operation to conditions that may trigger this type of anomalous combustion much more frequently. For this reason, an accurate on-board diagnosis system is required to adopt protective measures, preventing engine damage. Ion current signal provides relevant information about the combustion process, and it results in a good compromise between cost, durability and information quality (signal to noise ratio levels). The GDI turbocharged engine used for this study was equipped with a production ion current sensing system, while in-cylinder pressure sensors were installed for research purposes, to better understand the pre-ignition phenomenon characteristics, and to support the development of an on-board diagnostic system solely based on ion current measurements.

The paper presents the development of a methodology for detecting the misfire event using the time-frequency analysis of the instantaneous engine speed signal. The diagnosis of this type of malfunctioning operating condition is enforced by OBD requirements over the whole operating range of the engine, and many different approaches have been developed in the past in order to solve this problem. The novel approach presented here is based on the observation that the misfire causes an impulsive lack of torque acting on the engine crankshaft, and thus it causes the excitation of damped torsional vibrations at frequencies characteristic of the system under study. In order to enlighten the presence of this torsional vibration (and therefore detect the misfire event), information contained in the instantaneous crankshaft speed fluctuations have been processed in the time-frequency domain.

This paper shows the main results of a research activity carried out in order to investigate the impact of different hybridization concepts on vehicle fuel economy during standard homologation cycles (NEDC, FTP75, US Highway, Artemis). Comparative analysis between a standard passenger vehicle and three different hybrid solutions based on the same vehicle platform is presented. The following parallel hybrid powertrain solutions were investigated: Hybrid Electric Vehicle (HEV) solution (three different levels of hybridization are investigated with respect to different Electric Motor Generator size and battery storage/power capacity), High Speed Flywheel (HSF) system described as a fully integrated mechanical (kinetic) hybrid solution based on the quite innovative approach, and hydraulic hybrid system (HHV). In order to perform a fare analysis between different hybrid systems, analysis is also carried out for equal system storage capacities.

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment have given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Lamborghini has recently invested in the development of a hybrid super sport car, due to performance and comfort reasons. Aventador series gearbox is an Independent Shift Rod gearbox with a single clutch and during gear shifts, as all the single clutch gearbox do, it generates a torque gap. To avoid the additional weight of a Dual Clutch Transmission, a 48V Electric Motor has been connected to the wheels, in a P3 configuration, to fill the torque gap, and to habilitate regenerative braking and electric boost functions.

This paper shows the influence of different battery charge management strategies on the fuel economy of a hybrid parallel axle-split vehicle in a real driving scenario, for a vehicle control system that has the additional possibility to split the torque between front and rear axles. The first section regards the validation of a self-developed Model in the Loop (MiL) environment of a P1-P4 plug-in hybrid electric car, using experimental data of a New European Driving Cycle test. In its original version, which is implemented on-board the vehicle, the energy management supervisor implements a heuristic, or rule-based, Energy Management Strategy (EMS). During this project, a different EMS has been developed, consisting of a sub-optimal control scheme called Equivalent Consumption Minimization Strategy (ECMS), explained in detail in the second section.

In order to reduce emissions and improve fuel economy, most recent SI engines are equipped with several subsystems that deeply influence the combustion process. For example, the quality and the quantity of the fluid within the cylinder at Intake Valve Closing may be controlled by acting on Variable Valve Timing systems, external EGR, variable geometry intake systems, and of course on the throttle. On the other hand, tumble/swirl components of the intake flow may be influenced by acting on specifically designed devices. To achieve maximum efficiency, the Spark Advance (SA) controller should therefore compensate for different combustion speeds, in order to control cylinder pressure peak (or 50% mass fraction burnt) position at a constant value.

New gasoline engine design is highly influenced by CO2 and emission limits defined by legislations, the demand for real conditions fuel economy, higher torque, higher specific power and lower cost. To reach the requirements coming from the end-users and legislations, especially for SI engines, several technologies are available, such as downsizing, including turbocharging in combination with direct injection. These technologies allow to solve the main issues of gasoline engines in terms of efficiency and performance which are knocking, part-load losses, and thermal stress at high power conditions. Moreover, other possibilities are under evaluation to allow further steps of enhancement for the even more challenging requirements. However, the benefits and costs given by the mix of these technologies must be accurately evaluated by means of objective tools and procedures in order to choose among the best alternatives.

While the Diesel Particulate Filter (DPF) is actually a quasi-standard equipment in the European Diesel passenger cars market, an interesting solution to fulfill NOx emission limits for the next EU 6 legislation is the application of a Selective Catalytic Reduction (SCR) system on the exhaust line, to drastically reduce NOx emissions. In this context, one of the main issues is the performance of the SCR system during cold start and warm up phases of the engine. The exhaust temperature is too low to allow thermal activation of the reactor and, consequently, to promote high conversion efficiency and significant NOx concentration reduction. This is increasingly evident the smaller the engine displacement, because of its lower exhaust system temperature (reduced gross power while producing the same net power, i.e., higher efficiency).

The paper presents possible solutions for developing fast and reliable turbocharger models, to be used mainly for control applications. This issue is of particular interest today for SI engines since, due to the search for consistent CO2 reduction, extreme downsizing concepts require highly boosted air charge solutions to compensate for power and torque de-rating. For engines presenting at least four in-line cylinders, twin-entry turbines offer the ability of maximizing the overall energy conversion efficiency, and therefore such solutions are actually widely adopted. This work presents a critical review of the most promising (and recent) modeling approaches for automotive turbochargers, highlighting the main open issues especially in the field of turbine models, and proposing possible improvements.