Development of an Adaptive Efficient Thermal/Electric Skipping Control Strategy Applied to a Parallel Plug-in Hybrid Electric Vehicle

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy-ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications

Potential of hydrogen addition to natural gas or ammonia as a solution towards low- or zero-carbon fuel for the supply of a small turbocharged SI engine

Nowadays there is an increasing interest in carbon-free fuels such as ammonia and hydrogen. Those fuels, on one hand, allow to drastically reduce CO2 emissions, helping to comply with the increasingly stringent emission regulations, and, on the other hand, could lead to possible advantages in performances if blended with conventional fuels. In this regard, this work focuses on the 1D numerical study of an internal combustion engine supplied with different fuels: pure gasoline, and blends of methane-hydrogen and ammonia-hydrogen. The analyses are carried out with reference to a downsized turbocharged two-cylinder engine working in an operating point representative of engine operations along WLTC, namely 1800 rpm and 9.4 bar of BMEP. To evaluate the potential of methane-hydrogen and ammonia-hydrogen blends, a parametric study is performed. The varied parameters are air/fuel proportions (from 1 up to 2) and the hydrogen fraction over the total fuel. Hydrogen volume percentages up to 60% are considered both in the case of methane-hydrogen and ammonia-hydrogen blends. Model predictive capabilities are enhanced through a refined treatment of the laminar flame speed and chemistry of the end gas to improve the description of the combustion process and knock phenomenon, respectively. After the model validation under pure gasoline supply, numerical analyses allowed to estimate the benefits and drawbacks of considered alternative fuels in terms of efficiency, carbon monoxide, and pollutant emissions

Effects of Pre-Lift Intake Valve Strategies on the Performance of a DISI VVA Turbocharged Engine at Part and Full Load Operation☆

Abstract In the paper, the potentialities offered by an advanced valve lift design are numerically analyzed. In particular, the study is carried out by a 1D approach and regards the characterization of a VVA strategy named " pre-lift" applied to a downsized turbocharged four-cylinder engine. The pre-lift consists of a small, almost constant lift of the intake valve during the exhaust stroke, so to increase the valves overlapping. The results show a benefit on the fuel economy and on the gas-dynamic noise at part load and a substantial increase in the delivered torque at full load, while preserving the fuel consumption

Efficient Thermal Electric Skipping Strategy Applied to the Control of Series/Parallel Hybrid Powertrain

The optimal control of hybrid powertrains represents one of the most challenging tasks for the compliance with the legislation concerning CO2 and pollutant emission of vehicles. Most common off-line optimization strategies (Pontryagin minimum principle-PMP-or dynamic programming) allow to identify the optimal control along a predefined driving mission at the expense of a quite relevant computational effort. On-line strategies, suitable for on-vehicle implementation, involve a certain performance degradation depending on their degree of simplification and computational effort. In this work, a simplified control strategy is presented, where the conventional power-split logics, typical of the above-mentioned strategies, is here replaced with an alternative utilization of the thermal and electric units for the vehicle driving (Efficient Thermal Electric Skipping Strategy-ETESS). The choice between the units is realized at each time and is based on the comparison between the effective fuel rate of the thermal engine and an equivalent fuel rate related to the electrical power consumption. The equivalent fuel rate in a pure electric driving is associated to a combination of brake specific fuel consumption of the thermal engine, and electro-mechanical efficiencies along the driveline. The ETESS is applied for the simulation of segment C hybrid vehicle, equipped with a thermal engine and two electric units (motor and generator). The methodology is tested along regulatory driving cycles (WLTP, Artemis) and RDE, with different powertrain variants. Numerical results underline that the proposed approach performs very close to most common control strategies (consumed fuel per kilometer higher than PMP of about 1% on average). The main advantage is a reduced computational effort (decrease of 99% on average). The ETESS is straightforwardly adapted for an on-line implementation, through the introduction of an adaptative factor, preserving the computational effort and the fuel economy

Is your EPL attractive? Classification of publications through download statistics

Here we consider the download statistics of EPL publications. We find that papers in the journal are characterised by fast accumulations of downloads during the first couple of months after publication, followed by slower rates thereafter, behaviour which can be represented by a model with predictive power. We also find that individual papers can be classified in various ways, allowing us to compare categories for open-access and non-open-access papers. For example, for the latter publications, which comprise the bulk of EPL papers, a small proportion (2%) display intense bursts of download activity, possibly following an extended period of less remarkable behaviour. About 18% have an especially high degree of attractiveness over and above what is typical for the journal. One can also classify the ageing of attractiveness by examining download half-lives. Approximately 18% have strong interest initially, waning in time. A further 20% exhibit "delayed recognition" with relatively late spurs in download activity. Although open-access papers enjoy more downloads on average, the proportions falling into each category are similar.Comment: 6 pages, 8 figures, accepted for publication in EP

Employment of an auto-regressive model for knock detection supported by 1D and 3D analyses

In this work, experimental data, carried out on a twin-cylinder turbocharged engine at full load operations and referred to a spark advance of borderline knock, are used to characterize the effects of cyclic dispersion on knock phenomena. 200 consecutive incylinder pressure signals are processed through a refined Auto-Regressive Moving Average (ARMA) mathematical technique, adopted to define the percentage of knocking cycles, through a prefixed threshold level. The heuristic method used for the threshold selection is then verified by 1D and 3D analyses. In particular, a 1D model, properly accounting for cycle-by-cycle variations, and coupled to a reduced kinetic sub-model, is used to reproduce the measured cycles, in terms of statistical distribution of a theoretical knock index. In addition, few individual cycles, representative of the whole dataset, are selected in a single operating condition in order to perform a more detailed knock analysis by means of a 3D CFD approach, coupled to a tabulated chemistry technique for auto-ignition modeling. Outcomes of 1D and 3D models are compared to the ARMA results and a substantial coherence of the numerical and experimental results is demonstrated. The integrated 1D and 3D analyses can hence help in supporting the choice of the experimental threshold level for knock identification, following a more standardized theoretical approach

1D numerical and experimental investigations of an ultralean pre-chamber engine

In recent years, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations. With this solution, in fact, it is possible to simultaneously reduce NOx raw emissions and fuel consumption due to decreased heat losses, higher thermodynamic efficiency, and enhanced knock resistance. However, the real applicability of this technique is strongly limited by the increase in cyclic variation and the occurrence of misfire, which are typical for the combustion of homogeneous lean air/fuel mixtures. The employment of a Pre-Chamber (PC), in which the combustion begins before proceeding in the main combustion chamber, has already shown the capability of significantly extending the lean-burn limit. In this work, the potential of an ultralean PC SI engine for a decisive improvement of the thermal efficiency is presented by means of numerical and experimental analyses. The SI engine is experimentally investigated with and without the employment of the PC with the aim to analyze the real gain of this innovative combustion system. For both configurations, the engine is tested at various speeds, loads, and air-fuel ratios. A commercial gasoline fuel is directly injected into the Main Chamber (MC), while the PC is fed in a passive or active mode. Compressed Natural Gas (CNG) or Hydrogen (H2) is used in the actual case. A 1D model of the engine under study is implemented in a commercial modeling framework and is integrated with “in-house developed” sub-models for the simulation of the combustion and turbulence phenomena occurring in this unconventional engine. The numerical approach proves to reproduce the experimental data with good accuracy, without requiring any case-dependent tuning of the model constants. Both the numerical and experimental results show an improvement of the indicated thermal efficiency of the active PC, compared to the conventional ignition device, especially at high loads and low speeds. The injection of H2 into the PC leads to a significant benefit only with very lean mixtures. With the passive fueling of the PC, the lean-burn limit is less extended, with the consequent lower improvement potential for thermal efficiency

Biomarkers to personalize the treatment of rheumatoid arthritis: Focus on autoantibodies and pharmacogenetics

Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management

SEM tomography for the investigation of hybrid structures

The morphological investigation at the micrometric scale of a graphene - ZnO nanorods hybrid structure is performed by scanning electron microscopy. When operated in the scanning-transmission imaging mode, the detection strategy allows implementation of a tomographic approach to recover the three dimensional spatial arrangement of the sample constituents. This tomographic approach complements the serial-sectioning imaging methods and is suitable for thin, self-standing specimens