Automotive diesel engine transient operation: modeling, optimization and control

Traditionally, the study of internal combustion engines operation has focused on the steady-state performance. However, the daily driving schedule of automotive engines is inherently related to unsteady conditions. There are various operating conditions experienced by (diesel) engines that can be classified as transient. Besides the variation of the engine operating point, in terms of engine speed and torque, also the warm up phase can be considered as a transient condition. Chapter 2 has to do with this thermal transient condition; more precisely the main issue is the performance of a Selective Catalytic Reduction (SCR) system during cold start and warm up phases of the engine. The proposal of the underlying work is to investigate and identify optimal exhaust line heating strategies, to provide a fast activation of the catalytic reactions on SCR. Chapters 3 and 4 focus the attention on the dynamic behavior of the engine, when considering typical driving conditions. The common approach to dynamic optimization involves the solution of a single optimal-control problem. However, this approach requires the availability of models that are valid throughout the whole engine operating range and actuator ranges. In addition, the result of the optimization is meaningful only if the model is very accurate. Chapter 3 proposes a methodology to circumvent those demanding requirements: an iteration between transient measurements to refine a purpose-built model and a dynamic optimization which is constrained to the model validity region. Moreover all numerical methods required to implement this procedure are presented. Chapter 4 proposes an approach to derive a transient feedforward control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient.

Studio e sviluppo di un motore a 2 tempi diesel veloce di nuova concezione per applicazioni automobilistiche

The Department of Mechanical and Civil Engineering (DIMeC) of the University of Modena and Reggio Emilia is developing a new type of small capacity HSDI 2-Stroke Diesel engine (called HSD2), featuring a specifically designed combustion system, aimed to reduce weight, size and manufacturing costs, and improving pollutant emissions at partial load. The present work is focused on the analysis of the combustion and the scavenging process, investigated by means of a version of the KIVA-3V code customized by the University of Chalmers and modified by DIMeC. The customization of the KIVA-3V code includes a detailed combustion chemistry approach, coupled with a comprehensive oxidation mechanism for diesel oil surrogate and the modeling of turbulence/chemistry interaction through the PaSR (Partially Stirred Reactor) model. A four stroke automobile Diesel engine featuring a very close bore size is taken as a reference, for both the numerical models calibration and for a comparison with the 2-Stroke engine. Analysis is carried out trough a comparison between HSD2 and FIAT 1300 MultiJet in several operating conditions, at full and partial load. Such a comparison clearly demonstrates the effectiveness of the two stroke concept in terms of emissions reduction and high power density. However, HSD2 is still a virtual engine, and experimental results are needed to assume the reliability of numerical results.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Sviluppo di sistemi per l'analisi della combustione in tempo reale per motori endotermici alternativi

I moderni motori a combustione interna diventano sempre più complessi L'introduzione della normativa antinquinamento EURO VI richiederà una significativa riduzione degli inquinanti allo scarico. La maggiore criticità è rappresentata dalla riduzione degli NOx per i motori Diesel da aggiungersi a quelle già in vigore con le precedenti normative. Tipicamente la messa a punto di una nuova motorizzazione prevede una serie di test specifici al banco prova. Il numero sempre maggiore di parametri di controllo della combustione, sorti come conseguenza della maggior complessità meccanica del motore stesso, causa un aumento esponenziale delle prove da eseguire per caratterizzare l'intero sistema. L'obiettivo di questo progetto di dottorato è quello di realizzare un sistema di analisi della combustione in tempo reale in cui siano implementati diversi algoritmi non ancora presenti nelle centraline moderne. Tutto questo facendo particolare attenzione alla scelta dell'hardware su cui implementare gli algoritmi di analisi. Creando una piattaforma di Rapid Control Prototyping (RCP) che sfrutti la maggior parte dei sensori presenti in vettura di serie; che sia in grado di abbreviare i tempi e i costi della sperimentazione sui motopropulsori, riducendo la necessità di effettuare analisi a posteriori, su dati precedentemente acquisiti, a fronte di una maggior quantità di calcoli effettuati in tempo reale. La soluzione proposta garantisce l'aggiornabilità, la possibilità di mantenere al massimo livello tecnologico la piattaforma di calcolo, allontanandone l'obsolescenza e i costi di sostituzione. Questa proprietà si traduce nella necessità di mantenere la compatibilità tra hardware e software di generazioni differenti, rendendo possibile la sostituzione di quei componenti che limitano le prestazioni senza riprogettare il software.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

Two stroke cycle, novel combustion concepts and electrification for a new generation of internal combustion engines

The pursuit of decarbonization and increased efficiency in internal combustion engines (ICE) is crucial for reducing pollution in the mobility sector. While electrification is a long-term goal, ICE still has a role to play if coupled with innovative technologies. This research project explores various solutions to enhance ICE efficiency and reduce emissions, including Low Temperature Combustion (LTC), Dual fuel combustion with diesel and natural gas, and hydrogen integration. LTC methods like Dual fuel and Reactivity Controlled Compression Ignition (RCCI) show promise in lowering emissions such as NOx, soot, and CO2. Dual fuel Diesel-Natural Gas with hydrogen addition demonstrates improved efficiency, especially at low loads. RCCI Diesel-Gasoline engines offer increased Brake Thermal Efficiency (BTE) compared to standard diesel engines while reducing specific NOx emissions. The study compares 2-Stroke and 4-Stroke engine layouts, optimizing scavenging systems for both aircraft and vehicle applications. CFD analysis enhances specific power output while addressing injection challenges to prevent exhaust short circuits. Additionally, piston bowl shape optimization in Diesel engines running on Dual fuel (Diesel-Biogas) aims to reduce NOx emissions and enhance thermal efficiency. Unconventional 2-Stroke architectures, such as reverse loop scavenged with valves for high-performance cars, opposed piston engines for electricity generation, and small loop scavenged engines for scooters, are also explored. These innovations, alongside ultra-lean hydrogen combustion, offer diverse pathways toward achieving climate neutrality in the transport sector.