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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 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.

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.

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.

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.

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.

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.