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Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

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.

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 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.

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.

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.

Monitoring and preserving state-of-health of high-voltage battery packs in electrified road vehicles currently represents an open and growing research topic. When predicting high-voltage battery lifetime, most current literature assumes a uniform temperature distribution among the different cells of the pack. Nevertheless, temperature has been demonstrated having a key impact on cell lifetime, and different cells of the same battery pack typically exhibit different temperature profiles over time, e.g. due to their position within the pack. Following these considerations, this paper aims at assessing the effect of temperature distribution on the predicted lifetime of cells belonging to the same battery pack. To this end, a throughput-based numerical cell ageing model is firstly selected due to its reasonable compromise between accuracy and computational 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.

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.

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.

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.

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.

An innovative 0D predictive combustion model for the simulation of the HRR (heat release rate) in DI diesel engines was assessed and implemented in a 1D fluid-dynamic commercial code for the simulation of a Fiat heavy duty diesel engine equipped with a Variable Geometry Turbocharger system, in the frame of the CORE (CO2 reduction for long distance transport) Collaborative Project of the European Community, VII FP. The 0D combustion approach starts from the calculation of the injection rate profile on the basis of the injected fuel quantities and on the injection parameters, such as the start of injection and the energizing time, taking the injector opening and closure delays into account. The injection rate profile in turn allows the released chemical energy to be estimated. The approach assumes that HRR is proportional to the energy associated with the accumulated fuel mass in the combustion chamber.

An experimental investigation was performed on a turbocharged spark-ignition 4-cylinder production engine fuelled with natural gas and with two blends of natural gas and hydrogen (15% and 25% in volume of H₂). The engine was purposely designed to give optimal performance when running on CNG. The first part of the experimental campaign was carried out at MBT timing under stoichiometric conditions: load sweeps at constant engine speed and speed sweeps at constant load were performed. Afterwards, spark advance sweeps and relative air/fuel ratio sweeps were acquired at constant engine speed and load. The three fuels were compared in terms of performance (fuel conversion efficiency, brake specific fuel consumption, brake specific energy consumption and indicated mean effective pressure) and brake specific emissions (THC, NOx, CO).

The present work has the aim of developing a semi-empirical correlation to estimate the NO₂/NOx ratio as a function of significant engine operating variables in a modern EURO VI diesel engine. The experimental data used in the present study were acquired at the dynamic test bench of ICEAL-PT (Internal Combustion Engine Advanced Laboratory at the Politecnico di Torino), in the frame of a research activity on the optimization of a General Motors Euro VI prototype 1.6-liter diesel engine equipped with a single-stage variable geometry turbine and a solenoid Common Rail system. The experimental tests were conducted over the whole engine map. A preliminary analysis was carried out to evaluate the uncertainty of the experimental acquired data and the NO₂/NOx ratio.

The burned-gas propagation process has been characterized in two bi-fuel engines by means of a combustion diagnostic tool resulting from the integration of an original multizone heat-release model with a CAD procedure for the burned-gas front geometry simulation. Burned-gas mean expansion speed ub, mean gas speed ug and burning velocity Sb were computed as functions of crank angle and burned-gas radius for a wide range of engine speeds (n = 2000-5500 rpm), loads (bmep = 200-790 kPa), relative air-fuel ratios (RAFR = 0.80-1.60) and spark advances (SA ranging from 8 deg retard to 8 deg advance from MBT), under both gasoline and CNG operations. Finally, the influence of intake runner and combustion chamber geometries on flame propagation process was investigated. Main results show that Sb is generally comparable for the engine running on both gasoline and CNG, at the same engine speed and load, under stoichiometric and MBT operations.

The combustion propagation and burned-gas expansion processes in a bi-fuel CNG SI engine were characterized by applying a newly developed diagnostic tool, in order to better understand how these processes are related to the fuel composition, to the engine operating variables as well as to the exhaust emissions. The diagnostic tool is based on an original multizone heat-release model that is coupled with a CADmodel of the burned-gas containing surface for the computation of the burning speed and the burned-gas mean expansion velocity. Furthermore, the thermal and prompt NO sub-models, embedded in the diagnostic code, were employed to study the effects ofNO formation mechanisms and thermodynamic parameters on nitric oxide emissions.

The present work concerns the study of the potentialities of high-boost small-displacement C.R. (Common Rail) diesel engines where the compressor and the expander are mechanically disengaged for the purpose of power cogeneration from the exhaust gas. This objective can be achieved by means of advanced concept electrical devices capable of delivering the energy produced by the expander either to the drivetrain transmission or to the even more power-demanding auxiliary equipment of both the engine and the vehicle. The performance of a small-displacement boosted diesel engine with a common-rail injection system has been predicted by means of a computational code obtained by integrating different in-house non-commercial codes that simulate the intake, combustion and exhaust processes. The model validation has been carried out by means of the experimental data obtained at Fiat Research Center on a commercial small-displacement C.R. turbocharged diesel engine.

A production SI engine originally designed at Fiat Auto to operate with unleaded gasoline was converted to run on natural gas. To that end, in addition to designing and building the CNG fuel plant, it was necessary to replace the multipoint electronic module for injection-duration and ignition-timing control with an ECM designed to obtain multipoint sequential injection. The engine was modified so as to work either with gasoline or natural gas. For the present investigation, however, the engine configuration was not optimized for running on methane, in order to compare the performance of the engine operated by the two different fuels with the same compression ratio. In fact, the engine is also interesting as a dual-fuel engine because of its relatively high compression ratio ≈10.5 that is almost suitable for CNG operation. The engine had the main features of being a multivalve, fast-burn pent-roof chamber engine with a variable intake-system geometry.

A contribution has been given to the thermodynamics approach usually used for analyzing the combustion process in IC engines on the basis of cylinder pressure data reduction. A survey of heat release type combustion models and of their calibration methods has first been carried out with specific attention paid to the bulk gas-wall heat transfer correlations used. Experimental results have given evidence that most of these correlations are incapable of predicting the phase shift occurring between the gas-wall temperature difference and the heat transfer during the engine compression and expansion strokes, owing to the transient properties of the fluid directly in contact with the wall. This work develops and applies a refined procedure for heat release analysis of cylinder pressure data including the unsteadiness effects of the convective heat transfer process.