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Feed-forward low-throughput models have been developed to predict MFB50 and to control SOI in order to achieve a specific MFB50 target for diesel engines. The models have been assessed on a GMPT-E Euro 5 diesel engine, installed at the dynamic test bench at ICEAL-PT (Internal Combustion Engine Advanced Laboratory at the Politecnico di Torino) and applied to both steady state and transient engine operating conditions. MFB50 indicates the crank angle at which 50% of the fuel mass fraction has burned, and is currently used extensively in control algorithms to optimize combustion phasing in diesel engines in real-time. MFB50 is generally used in closed-loop combustion control applications, where it is calculated by the engine control unit, cycle-by-cycle and cylinder by-cylinder, on the basis of the measured in-cylinder pressure trace, and is adjusted in order to reduce the fuel consumption, combustion noise and engine-out emissions.

An unsupervised machine-learning technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption.

A quasi-dimensional multizone combustion model, that was previously developed by the authors, has been refined and applied for the analysis of combustion and emission formation in a EURO V diesel engine equipped with a piezo indirect-acting injection system. The model is based on the integration of the predictive non-stationary variable-profile 1D spray model recently presented by Musculus and Kattke, with a diagnostic multizone thermodynamic model specifically developed by the authors. The multizone approach has been developed starting from the Dec conceptual scheme, and is based on the identification of several homogeneous zones in the combustion chamber, to which mass and energy conservation laws have been applied: an unburned gas zone, made up of air, EGR (Exhaust Gas Recirculation) and residual gas, several fuel/unburned gas mixture zones, premixed combustion burned gas zones and diffusive combustion burned gas zones.

An innovative quasi-dimensional multizone combustion model for the spray formation, combustion and emission formation analysis in DI diesel engines was assessed and applied to an optical single cylinder engine. The model, which has been recently presented by the authors, integrates a predictive non stationary 1D spray model developed by the Sandia National Laboratory, with a diagnostic multizone thermodynamic model. The 1D spray model is capable of predicting the equivalence ratio of the fuel during the mixing process, as well as the spray penetration. The multizone approach is based on the application of the mass and energy conservation laws to several homogeneous zones identified in the combustion chamber. A specific submodel is also implemented to simulate the dilution of the burned gases. Soot formation is modeled by an expression which derives from Kitamura et al.'s results, in which an explicit dependence on the local equivalence ratio is considered.

This works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called ego-vehicle with other vehicles and infrastructure using V2V and/or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them.

Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS.

A new predictive zero-dimensional low-throughput combustion model has been applied to both PCCI (Premixed Charge Compression Ignition) and conventional diesel engines to simulate HRR (Heat Release Rate) and in-cylinder pressure traces on the basis of the injection rate. The model enables one to estimate the injection rate profile by means of the injection parameters that are available from the engine ECU (Electronic Control Unit), i.e., SOI (Start Of main Injection), ET (Energizing Time), DT (Dwell Time) and injected fuel quantities, taking the injector NOD (Nozzle Opening Delay) and NCD (Nozzle Closure Delay) into account. An accumulated fuel mass approach has been applied to estimate Qch (released chemical energy), from which the main combustion parameters that are of interest for combustion control in IC engines, such as, SOC (Start Of Combustion), MFB50 (50% of Mass Fraction Burned) have been derived.

The potential of internal EGR (iEGR) and external EGR (eEGR) in reducing the engine-out NOx emissions in a heavy-duty diesel engine has been investigated by means of a refined 1D fluid-dynamic engine model developed in the GT-Power environment. The engine is equipped with Variable Valve Actuation (VVA) and Variable Geometry Turbocharger (VGT) systems. The activity was carried out in the frame of the CORE Collaborative Project of the European Community, VII FP. The engine model integrates an innovative 0D predictive combustion algorithm for the simulation of the HRR (heat release rate) based on the accumulated fuel mass approach and a multi-zone thermodynamic model for the simulation of the in-cylinder temperatures. NOx emissions are calculated by means of the Zeldovich thermal and prompt mechanisms.

Pressure-based and model-based techniques for the control of MFB50 (crank angle at which 50% of the fuel mass fraction has burned) have been developed, assessed and tested by means of rapid prototyping (RP) on a FPT F1C 3.0L Euro VI diesel engine. The pressure-based technique requires the utilization of a pressure transducer for each cylinder. The transducers are used to perform the instantaneous measurement of the in-cylinder pressure, in order to derive its corresponding burned mass fraction and the actual value of MFB50. It essentially consists of a closed-loop approach, which is based on a cycle-by-cycle and cylinder-to-cylinder correction of the start of injection of the main pulse (SOImain), in order to achieve the desired target of MFB50 for each cylinder.

Natural gas is a promising alternative fuel for internal combustion engine application due to its low carbon content and high knock resistance. Performance of natural gas engines is further improved if direct injection, high turbocharger boost level, and variable valve actuation (VVA) are adopted. Also, relevant efficiency benefits can be obtained through downsizing. However, mixture quality resulting from direct gas injection has proven to be problematic. This work aims at developing a mono-fuel small-displacement turbocharged compressed natural gas engine with side-mounted direct injector and advanced VVA system. An injector configuration was designed in order to enhance the overall engine tumble and thus overcome low penetration.

In diesel engines the optimization of engine-out emissions, combustion noise and fuel consumption requires the experimental investigation of the effects of different injection strategies as well as of a large number of engine operating variables, such as scheduling of pilot and after pulses, rail pressure, EGR rate and swirl level. Due to the high number of testing conditions involved full factorial approaches are not viable, whereas Design of Experiment techniques have demonstrated to be a valid methodology. However, the results obtained with such techniques require a subsequent critical analysis, so as to investigate the cause and effect relationships between the set of engine operating variables and the combustion process characteristics that affect pollutant formation, noise of combustion and engine efficiency.

The present paper is concerned with part of the work performed by Renault, IFPEN and Politecnico di Torino within a research project founded by the European Commission. The project has been focused on the development of a dedicated CNG engine featuring a 25% decrease in fuel consumption with respect to an equivalent Diesel engine with the same performance targets. To that end, different technologies were implemented and optimized in the engine, namely, direct injection, variable valve timing, LP EGR with advanced turbocharging, and diluted combustion. With specific reference to diluted combustion, it is rather well established for gasoline engines whereas it still poses several critical issues for CNG ones, mainly due to the lower exhaust temperatures. Moreover, dilution is accompanied by a decrease in the laminar burning speed of the unburned mixture and this generally leads to a detriment in combustion efficiency and stability.

Sustainable mobility has become a major issue for internal combustion engines and has led to increasing research efforts in the field of alternative fuels, such as bio-fuel, CNG and hydrogen addition, as well as into engine design and control optimization. To that end, a thorough control of the air-to-fuel ratio appears to be mandatory in SI engine in order to meet the even more stringent thresholds set by the current regulations. The accuracy of the air/fuel mixture highly depends on the injection system dynamic behavior and to its coupling to the engine fluid-dynamic. Thus, a sound investigation into the mixing process can only be achieved provided that a proper analysis of the injection rail and of the injectors is carried out. The present paper carries out a numerical investigation into the fluid dynamic behavior of a commercial CNG injection system by means of a 0D-1D code.

Reducing computational time has become a critical issue in recent years, particularly in the transportation field, where the complexity of scenarios demands lightweight controllers to run large simulations and gather results to study different behaviors. This study proposes two novel formulations of the Optimal Control Problem (OCP) for the Energy Management System of a Plug-in Hybrid Electric Vehicle (PHEV) and compares their performance with a benchmark found in the literature. Dynamic Programming was chosen as the optimization algorithm to solve the OCP in a Matlab environment, using the DynaProg toolbox. The objective is to address the optimality of the fuel economy solution and computational time. In order to improve the computational efficiency of the algorithm, an existing formulation from the literature was modified, which originally utilized three control inputs.

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency.

Direct injection (DI) of natural gas (NG) at high pressure conditions has emerged as a high-potential strategy for improving SI engine performance. Besides, DI allows an increase in the fuel economy, due to the possibility of a significant engine dethrottling at partial load. The high-pressure gas injection can also increase the turbulence level of mixture and thus the overall fuel-air mixing. Since direct NG injection is an emerging technology, there is a lack of experience on the optimum configuration of the injection system and the associated combustion chamber design. In the last few years, some numerical investigations of gas injection have been made, mainly oriented at the development of reliable numerical investigation tools. The present paper is concerned with the development and application of a numerical Star-CD based model for the investigation of the direct NG injection process from a poppet-valve injector into a bowl-piston engine combustion chamber.

This paper presents the results of part of the research activity carried out by the Politecnico di Torino and AVL List GmbH as part of the European Community InGAS Collaborative Project. The work was aimed at developing a combustion system for a mono-fuel turbocharged CNG engine, with specific focus on performance, fuel economy and emissions. A numerical and experimental analysis of the jet development and mixture formation in an optically accessible, single cylinder engine is presented in the paper. The experimental investigations were performed at the AVL laboratories by means of the planar laser-induced fluorescence technique, and revealed a cycle-to-cycle jet shape variability that depended, amongst others, on the injector characteristics and in-cylinder backpressure. Moreover, the mixing mechanism had to be optimized over a wide range of operating conditions, under both stratified lean and homogeneous stoichiometric modes.

New methodologies have been developed to optimize EGR rate and injection timing in diesel engines, with the aim of minimizing fuel consumption (FC) and NOx engine-out emissions. The approach entails the application of a recently developed control-oriented engine model, which includes the simulation of the heat release rate, of the in-cylinder pressure and brake torque, as well as of the NOx emission levels. The engine model was coupled with a C-class vehicle model, in order to derive the engine speed and torque demand for several driving cycles, including the NEDC, FTP, AUDC, ARDC and AMDC. The optimization process was based on the minimization of a target function, which takes into account FC and NOx emission levels. The selected control variables of the problem are the injection timing of the main pulse and the position of the EGR valve, which have been considered as the most influential engine parameters on both fuel consumption and NOx emissions.

This paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power. First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range. A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.