Enhancing the accuracy of engine calibration through a computer aided calibration algorithm

Abstract This paper addresses a novel Computer Aided Calibration software developed by the authors to overcome a critical issue of the traditional calibration process: improve the calibration accuracy. The algorithm includes some innovative features aimed at error minimization through a complete parametric analysis of a target ECU functions. Therefore, it is possible to assess if further quantities that are not considered as calibration parameters within the current ECU function model actually affect the quantity estimated by the function itself. If so, a more accurate physical model can be implemented within the ECU function to increase the accuracy of the calibration process

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Dupilumab in the treatment of severe uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP): A multicentric observational Phase IV real-life study (DUPIREAL)

Background Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with significant morbidity and reduced health-related quality of life. Findings from clinical trials have demonstrated the effectiveness of dupilumab in CRSwNP, although real-world evidence is still limited. Methods This Phase IV real-life, observational, multicenter study assessed the effectiveness and safety of dupilumab in patients with severe uncontrolled CRSwNP (n = 648) over the first year of treatment. We collected data at baseline and after 1, 3, 6, 9, and 12 months of follow-up. We focused on nasal polyps score (NPS), symptoms, and olfactory function. We stratified outcomes by comorbidities, previous surgery, and adherence to intranasal corticosteroids, and examined the success rates based on current guidelines, as well as potential predictors of response at each timepoint. Results We observed a significant decrease in NPS from a median value of 6 (IQR 5–6) at baseline to 1.0 (IQR 0.0–2.0) at 12 months (p < .001), and a significant decrease in Sino-Nasal Outcomes Test-22 (SNOT-22) from a median score of 58 (IQR 49–70) at baseline to 11 (IQR 6–21; p < .001) at 12 months. Sniffin' Sticks scores showed a significant increase over 12 months (p < .001) compared to baseline. The results were unaffected by concomitant diseases, number of previous surgeries, and adherence to topical steroids, except for minor differences in rapidity of action. An excellent-moderate response was observed in 96.9% of patients at 12 months based on EPOS 2020 criteria. Conclusions Our findings from this large-scale real-life study support the effectiveness of dupilumab as an add-on therapy in patients with severe uncontrolled CRSwNP in reducing polyp size and improving the quality of life, severity of symptoms, nasal congestion, and smell

Burden of disease attributable to risk factors in European countries: a scoping literature review

Objectives: Within the framework of the burden of disease (BoD) approach, disease, and injury burden estimates attributable to risk factors are a useful guide for policy formulation and priority setting in disease prevention. Considering the important differences in methods, and their impact on burden estimates, we conducted a scoping literature review to: (1) map the BoD assessments including risk factors performed across Europe, and (2) identify the methodological choices in comparative risk assessment (CRA) and risk assessment methods. Methods: We searched multiple literature databases, including grey literature websites, and targeted public health agencies' websites. Results: A total of 113 studies were included in the synthesis and further divided into independent BoD assessments (54 studies) and studies linked to the Global Burden of Disease (59 papers). Our results showed that the methods used to perform CRA varied substantially across independent European BoD studies. While there were some methodological choices that were more common than others, we did not observe patterns in terms of country, year, or risk factor. Each methodological choice can affect the comparability of estimates between and within countries and/or risk factors since they might significantly influence the quantification of the attributable burden. From our analysis, we observed that the use of CRA was less common for some types of risk factors and outcomes. These included environmental and occupational risk factors, which are more likely to use bottom-up approaches for health outcomes where disease envelopes may not be available. Conclusions: Our review also highlighted misreporting, the lack of uncertainty analysis, and the under-investigation of causal relationships in BoD studies. Development and use of guidelines for performing and reporting BoD studies will help understand differences, and avoid misinterpretations thus improving comparability among estimates.info:eu-repo/semantics/publishedVersio

Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

The electronic control has an increasingly important role in the evolution of the internal combustion engine (ICE) and the vehicle. Research in the automotive sector, in this historical period, is dictated by three main guidelines: reducing polluting emissions and fuel consumption while maintaining high performance. The Electronic Control Unit (ECU) has made it possible, complicating the engine both in terms of architecture and in terms of strategies, controlling, through simplified functions, physical phenomena in an ever more precise way. The ECU functions are experimentally calibrated, reducing the error between the quantity estimated by the function and the experimental quantity over the entire operating range of the engine, developing extensive experimental campaigns. The calibration process of the ECU functions is one of the longest and most expensive processes in the development of a new vehicle. Some lines of research have been explored to reduce the experimental tests to be carried out on the test bench. The use of neural networks (NN) has proven to be effective, leading to a reduction in experimental tests from 40 to 60%. Another methodology consists in the use of 1D/0D Thermo-fluid dynamic models of the ICE. These models are used as virtual test benches and through them it is possible to carry out the experimental campaigns necessary for the calibration of the control unit functions. At the real test bench, only the few experimental tests necessary for the validation of the model must be carried out. One of the simplifications that is usually made in the 1D/0D ICE models consists in assigning a single intake and exhaust valve lift, without taking into account the effect of the engine speed on the valve lift in early intake valve closure (EIVC) mode for engines equipped with VVA. This phenomenon has a not negligible effect on engine performance, especially at high engine speeds. In the case of engine models equipped with VVA, the valve lift cannot be imposed, since it is unique for each closing angle at each engine speed. Indeed, in order to assign the correct valve lift for a given engine speed and EIVC, numerous experimental tests should be carried out, making vain the beneficial effects of the method. In this work, the authors propose the use of a 0D/1D CFD model of the entire electro-hydraulic valvetrain VVA module, coupled with 1D lumped mass for reproducing the linear displacements of the intake valve, and for simulating the interactions between flow and mechanical systems of the solenoid hydro-mechanical valve. Thus, model simulations allow to predict the valve lift in all the necessary conditions in the experimental campaigns for the calibration of the control unit functions. Starting from geometric valvetrain data, the model has been validated with a parametric analysis of some variables on which there was greater uncertainty, by comparing the valve lift obtained by the model with the experimental ones in certain engine speeds. Subsequently, the authors have obtained the valve lifts in conditions not used for model validation, comparing them with their respective experimental lifts. The model has proven to be sensitive to the effect of the variation of the engine speed, reproducing the valve lift with a low error. In this way it is possible to reduce the experimental effort aimed to the calibration process considering that the virtual experimental campaign has proven to be reliable

Assessment of Battery–Supercapacitor Topologies of an Electric Vehicle under Real Driving Conditions

Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies

A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings Through Regression Techniques and Neural Networks

In the last few years, the automotive industry had to face three main challenges: compliance with more severe pollutant emission limits, better engine performance in terms of torque and drivability and simultaneous demand for a significant reduction in fuel consumption. These conflicting goals have driven the evolution of automotive engines. In particular, the achievement of these mandatory aims, together with the increasingly stringent requirements for carbon dioxide reduction, led to the development of highly complex engine architectures needed to perform advanced operating strategies. Therefore, Variable Valve Actuation (VVA), Exhaust Gas Recirculation (EGR), Gasoline Direct Injection (GDI), turbocharging, powertrain hybridization and other solutions have gradually and widely been introduced into modern internal combustion engines, enhancing the possibilities of achieving the required goals. However, none of the improvements would have been possible without the contextual development of electronics. In fact, that solutions have highly increased the complexity of engine control and management because of the degrees of freedom available for the engine regulation, thus resulting in a long calibration time. In particular, base calibration is the most onerous phase of the engine control, both in terms of experimental and computational effort and costs. This paper addresses some critical issues concerning the calibration of control parameters through the use of a specific Model-Based Computer Aided Calibration algorithm developed by the authors to automate the calibration process and minimize calibration errors. The proposed methodology is also based on the use of neural networks (NN). In particular, starting from a reduced number of experimental data, NN provide a detailed engine data sheets used as input to the actual calibration process itself. The proposed algorithm provides optimal portability and reduced calibration time. The research also highlights how the developed methodology could be useful to identify possible enhancements for specific ECU engine models that can improve the accuracy of the calibration process by using more detailed physically based functions. The results of the proposed research clearly highlight how, in engine control, more accurate physical modeling may lead to promising results and better performance, ultimately enhancing the accuracy, time, experimental effort and cost savings of the calibration process

Volumetric efficiency estimation based on neural networks to reduce the experimental effort in engine base calibration

Over the last decades, internal combustion engines have undergone a continuous evolution to achieve better performance, lower pollutant emissions and reduced fuel consumption. This evolution involved changes in the engine architecture needed to perform advanced management strategies. Therefore, Variable Valve Actuation, Exhaust Gas Recirculation, Gasoline Direct Injection, turbocharging and powertrain hybridization have widely equipped modern internal combustion engines. However, the effective management of a such complex system is due to the contemporaneous development of the on-board Engine electronic Control Unit. In fact, the additional degrees of freedom available for the engine regulation highly increased the complexity of engine control and management, resulting in a very expensive and long calibration process. For this reason, this study proposes an effective methodology based on the use of Neural Networks to overcome some critical issues concerning the calibration of engine control parameters. NN are adopted to provide a detailed engine data sheet starting from a reduced number of experimental data. To verify the potential of the proposed methodology, this detailed data set is subsequently used as input to a specific Computer Aided Calibration algorithm developed by the authors and the achievable calibration performance are evaluated. In particular, the calibration performance was assessed with reference to a specific ECU function in this paper. The research clearly demonstrates the effectiveness of the proposed approach since the calibration performance falls within acceptable limits even after a 60% cut of the experimental data usually acquired for calibration purposes, highlighting how the use of neural networks can allow a significant reduction of the experimental effort along with its related times and costs

Reduction of the experimental effort in engine calibration by using neural networks and 1D engine simulation

In this study, two effective methodologies are proposed to overcome some critical issues concerning the base calibration of engine control parameters. Specifically, Neural Networks and 1D CFD simulation were alternatively adopted to reliably calibrate specific ECU functions starting from a reduced number of experimental data. The calibration performance fall within acceptable limits even when significant cuts are made to the experimental data usually acquired for calibration purposes, demonstrating that the proposed methodologies can be useful to significantly reduce the dynamometer tests and their related times and costs