1,721,020 research outputs found
Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction
Full text linkThe energy management of a Hybrid Electric Vehicle (HEV) is a global optimization problem, and its optimal solution inevitably entails knowing the entire mission profile. The exploitation of Vehicle-to-Everything (V2X) connectivity can pave the way for reliable short-term vehicle speed predictions. As a result, the capabilities of conventional energy management strategies can be enhanced by integrating the predicted vehicle speed into the powertrain control strategy. Therefore, in this paper, an innovative Adaptation algorithm uses the predicted speed profile for an Equivalent Consumption Minimization Strategy (A-V2X-ECMS). Driving pattern identification is employed to adapt the equivalence factor of the ECMS when a change in the driving patterns occurs, or when the State of Charge (SoC) experiences a high deviation from the target value. A Principal Component Analysis (PCA) was performed on several energetic indices to select the ones that predominate in characterizing the different driving patterns. Long Short-Term Memory (LSTM) deep neural networks were trained to choose the optimal value of the equivalence factor for a specific sequence of data (i.e., speed, acceleration, power, and initial SoC). The potentialities of the innovative A-V2X-ECMS were assessed, through numerical simulation, on a diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. A virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against a wide database of experimental data. The simulations proved that the proposed approach achieves results much closer to optimal than the conventional energy management strategies taken as a reference
Physicochemical and mutagenic analysis of particulate matter emissions from an automotive diesel engine fuelled with fossil and biofuel blends
No full textIn this paper, a complete characterization of diesel Particulate Matter (PM) in terms of mass, chemical composition, particles number, size distribution and mutagenic potential was carried out. As a test case, a Euro 5 1.3L passenger car diesel engine fueled with Ultra Low Sulphur Diesel (ULSD) and high-percentage blends (B30 − 30% vol.) of ULSD and biofuels (Rapeseed Methyl Ester, RME, and Hydrotreated Vegetables Oil, HVO) was considered. In addition, PM gravimetric analysis was compared with soot measurements carried out by means of standard laboratory equipment (e.g. smokemeters), showing a good correlation at medium and high load operating points. On the contrary, at low loads, as confirmed by thermo-gravimetric analysis, smokemeter measurements underestimated the Soluble Organic Fraction (SOF) fraction of PM, especially when for RME (B30). Then, a detailed investigation on Particles Number (PN) and size distribution was carried out, running the engine with the same fuel matrix. The results showed that adopting biofuel blends, both RME (B30) and HVO (B30), produces negligible differences in particles number distributions with respect to standard diesel. Finally, the analysis of the mutagenic potential of PM samples highlighted an increased genotoxicity for HVO (B30) and a decreased genotoxicity for RME (B30) with respect to ULSD
Numerical investigation of 48 V electrification potential in terms of fuel economy and vehicle performance for a lambda-1 gasoline passenger car
Full text linkReal Driving Emissions (RDE) regulations require the adoption of stoichiometric operation across the entire engine map for downsized turbocharged gasoline engines, which have been so far generally exploiting spark timing retard and mixture enrichment for knock mitigation. However, stoichiometric operation has a detrimental effect on engine and vehicle performances if no countermeasures are taken, such as alternative approaches for knock mitigation, as the exploitation of Miller cycle and/or powertrain electrification to improve vehicle acceleration performance. This research activity aims, therefore, to assess the potential of 48 V electrification and of the adoption of Miller cycle for a downsized and stoichiometric turbocharged gasoline engine. An integrated vehicle and powertrain model was developed for a reference passenger car, equipped with a EU5 gasoline turbocharged engine. Afterwards, two different 48 V electrified powertrain concepts, one featuring a Belt Starter Generator (BSG) mild-hybrid architecture, the other featuring, in addition to the BSG, a Miller cycle engine combined with an e-supercharger were developed and investigated. Vehicle performances were evaluated both in terms of elasticity maneuvers and of CO2 emissions for type approval and RDE driving cycles. Numerical simulations highlighted potential improvements up to 16% CO2 reduction on RDE driving cycle of a 48 V electrified vehicle featuring a high efficiency powertrain with respect to a EU5 engine and more than 10% of transient performance improvement
A Methodology for the Reverse Engineering of the Energy Management Strategy of a Plug -In Hybrid Electric Vehicle for Virtual Test Rig Development
Full text linkNowadays, the need for more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables. Furthermore, the impact that Vehicle-to-Infrastructure (V2I) connections have on the control law is assessed on several tests performing the same real-world route with the vehicle navigation system alternatively switched on and off. Finally, a virtual test rig of the tested vehicle, developed in the GT- SUITE environment, is used to validate the set of extracted rules against the experimental data. An error of about 1-2% on the prediction of the vehicle CO2 emissions and good matching of the State of Charge (SoC) profile in both Charge Depleting (CD) and Charge Sustaining (CS) phases prove the effectiveness of the proposed methodology
Corporate Governance Reforms, Interlocking Directorship and Company Performance in Italy
No full textWe analyze the effects of corporate governance reforms on interlocking directorship (ID), and we assess the relationship between interlocking directorships and company performance for the main Italian firms listed on the Italian stock exchange over 1998-2007. We use a unique dataset that includes corporate governance variables related to the board size, interlocking directorships and variables related to companies’ performances. The network analysis showed only some effectiveness of these reforms in slightly dispersing the web of companies. Using a diff-in-diff approach, we then find in the period considered a slight reduction in the returns of those companies where interlocking directorships were used the most, which confirms our assumption on the perverse effect of ID on company performance in a context prone to shareholder expropriation such as the Italian one
Supercar Hybridization: A Synergic Path to Reduce Fuel Consumption and Improve Performance
No full textThe trend towards powertrain electrification is expected to grow significantly in the next future also for super-cars. The aim of this paper is therefore to assess, through numerical simulation, the impact on both fuel economy and performance of different 48 Volt mild hybrid architectures for a high-performance sport car featuring a Turbocharged Direct Injection Spark Ignition (TDISI) engine. In particular the hybrid functionalities of both a P0 (Belt Alternator Starter - BAS) and a P2 (Flywheel Alternator Starter - FAS) architecture were investigated and optimized for this kind of application through a global optimization algorithm. The analysis pointed out CO2 emission reductions potential of about 6% and 25% on NEDC, 7% and 28% on WLTC for P0 and P2 respectively. From the performance perspective, a 10% reduction in the time-to-torque was highlighted for both architectures in a load step maneuver at 2000 RPM constant speed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
