Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

In this paper, a methodology based on data-driven models is developed to predict the NOx emissions of an internal combustion engine using, as inputs, a set of ECU channels representing the main engine actuations. Several regressors derived from the machine learning and deep learning algorithms are tested and compared in terms of prediction accuracy and computational efficiency to assess the most suitable for the aim of this work. Six Real Driving Emission (RDE) cycles performed at the roll bench were used for the model training, while another two RDE cycles and a steady-state map of NOx emissions were used to test the model under dynamic and stationary conditions, respectively. The models considered include Polynomial Regressor (PR), Support Vector Regressor (SVR), Random Forest Regressor (RF), Light Gradient Boosting Regressor (LightGBR) and Feed-Forward Neural Network (ANN). Ensemble methods such as Random Forest and LightGBR proved to have similar performances in terms of prediction accuracy, with LightGBR requiring a much lower training time. Afterwards, LightGBR predictions are compared with experimental NOx measurements in steady-state conditions and during two RDE cycles. Coefficient of determination (R2), normalized root mean squared error (nRMSE) and mean average percentage error (MAPE) are the main metrics used. The NOx emissions predicted by the LightGBR show good coherence with the experimental test set, both with the steady-state NOx map (R2 = 0.91 and MAPE = 6.42%) and with the RDE cycles (R2 = 0.95 and nRMSE = 0.04)

automotive turbochargers power estimation based on speed fluctuation analysis

Turbocharging technology will play a crucial role in the near future as a way to meet the requirements for pollutant emissions and fuel consumption reduction. However, optimal turbocharger control is still an issue, especially for downsized engines fitted with a low number of cylinders. As a matter of fact, automotive turbochargers are characterized by wide operating range and unsteady gas flow through the turbine, while only steady flow maps are usually provided by the manufacturer. In addition, in passenger cars applications, real-time turbocharger optimal control is even more difficult because of the lack of information about pressure/temperature in turbine upstream/downstream circuits and turbocharger rotational speed. In order to overcome these unknowns, this work presents a methodology for instantaneous turbocharger rotational speed determination through a proper processing of the signal coming from one accelerometer mounted on the compressor diffuser, or one microphone facing the compressor. The presented approach can be used to evaluate both turbocharger speed mean value and the amplitude of turbocharger speed fluctuations caused by the pulsating gas flow in turbine upstream and downstream circuits. Once turbocharger speed has been determined, it can be used to estimate power delivered by the turbine. The whole estimation algorithm has been developed and validated for a light duty turbocharged Common-Rail Diesel engine mounted in a test cell. However, the developed methodology is general and can be applied to different turbochargers, both for Spark Ignited and Diesel applications. © 2015 Published by Elsevier Ltd

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

The European Community's recent decision to suspend the marketing of cars with conventional fossil-fueled internal combustion engines from 2035 requires new solutions, based on carbon-neutral technologies, that ensure equivalent performances in terms of reliability, trip autonomy, refueling times and end-of-life disposal of components compared to those of current gasoline or diesel cars. The use of bio-fuels and hydrogen, which can be obtained by renewable energy sources, coupled with high-efficiency combustion methodologies might allow to reach the carbon neutrality of transports (net-zero carbon dioxide emissions) even using the well-known internal combustion engine technology. Bearing this in mind, experiments were carried out on compression ignited engines running on gasoline (GCI) with a high thermal efficiency which, in the future, could be easily adapted to run on a bio-fuel. Despite the well-reported benefits of GCI engines in terms of efficiency and pollutant emissions, combustion instability hinders the diffusion of these engines for industrial applications. A possible solution to stabilize GCI combustion is the use of multiple injections strategies, typically composed by 2 early injected fuel jests followed by the main injection. The heat released by the combustion of the earlier fuel jets allows to reduce the ignition delay of the main injection, directly affecting both delivered torque and center of combustion. As a result, to properly manage GCI engines, a stable and reliable combustion of the pre-injections is mandatory. In this paper, an estimation methodology of the start of combustion (SOC) position, based on the analysis of the signal coming from an accelerometer sensor mounted on the engine block, is presented (the optimal sensor positioning is also discussed). A strong correlation between the SOC calculated from the accelerometer and that obtained from the analysis of the rate of heat release (RoHR) was identified. As a result, the estimated SOC could be used to feedback an adaptive closed-loop combustion control algorithm, suitable to improve the stability of the whole combustion process

automatic calibration of control parameters based on merit function spectral analysis

Abstract The number of actuations influencing the combustion is increasing, and, as a consequence, the calibration of control parameters is becoming challenging. One of the most effective factors influencing performance and efficiency is the combustion phasing: for gasoline engines control variables such as Spark Advance (SA), Air-to-Fuel Ratio (AFR), Variable Valve Timing (VVT), Exhaust Gas Recirculation (EGR) are mostly used to set the combustion phasing. The optimal control setting can be chosen according to a cost function, taking into account performance indicators, such as Indicated Mean Effective Pressure (IMEP), Brake Specific Fuel Consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature, or knock intensity. The paper proposes the use of the extremum seeking approach during the calibration process. The main idea consists in changing the values of each control parameter at the same time, identifying its effect on the monitored cost function, allowing to shift automatically the control setting towards the optimum solution throughout the calibration procedure. Obviously, the nodal point is to establish how the various control parameters affect the monitored cost function and to determine the direction of the required variation, in order to approach the optimum. This task is carried out by means of a spectral analysis of the cost function: each control variable is varied according to a sine wave, thus its effect on the cost function can be determined by evaluating the amplitude of the Fast Fourier Transform (FFT) of the cost function, for the given excitation frequency. The FFT amplitude is representative of the cost function sensitivity to the control variable variations, while the phase can be used to assess the direction of the variation that must be applied to the control settings in order to approach the optimum configuration. Each control parameter is excited with a different frequency, thus it is possible to recognize the effect of a single parameter by analyzing the spectrum of the cost function for the given excitation frequency. The methodology has been applied to data referring to a PFI engine, trying to maximize IMEP, while limiting the knock intensity and exhaust gas temperature, using SA and AFR as control variables. The approach proved to be efficient in reaching the optimum control setting, showing that the optimal setting can be achieved rapidly and consistently

On the emission reduction through the application of an electrically heated catalyst to a diesel vehicle

Exhaust emissions from diesel engine powered vehicles are considerably high during cold start and warm‐up, because of the poor catalyst performance due to the insufficient catalyst temperature. The controlled heat injection allowed by electrically heated catalysts can effectively reduce the catalyst light‐off time with relatively moderate fuel penalty. This paper compares the exhaust temperature and emissions of a case study diesel vehicle in cold and warm start conditions, and proposes two electrically heated catalyst control strategies, which are evaluated in terms of emission reduction and energy consumption with different target temperature settings. In addition, a new performance indicator, that is, the specific emission reduction, is used to evaluate the after‐treatment system and associated thermal management. For the worldwide harmonized light vehicle test cycle, the results without electrically heated catalyst show that from both cold and warm start conditions a large amount of operating points of the engine is located in the region of partial catalyst light off. Moreover, emissions, especially in terms of carbon monoxide and hydrocarbon, significantly decrease with the electrically heated catalyst implementation, for example, by at least 50% from cold start; however, they still tend to be rather substantial when the fuel is re‐injected after the engine cutoff phases. The exhaust temperature is lower than the target values in the sections of the driving cycle in which the electrically heated catalyst power is saturated according to the maximum level allowed by the device. The carbon dioxide penalty brought by the electrically heated catalyst ranges from 3.93% to 6.65% and from 6.49% to 9.35% for warm and cold start conditions, respectively

Accurate Visuomotor Control below the Perceptual Threshold of Size Discrimination

Background: Human resolution for object size is typically determined by psychophysical methods that are based on conscious perception. In contrast, grasping of the same objects might be less conscious. It is suggested that grasping is mediated by mechanisms other than those mediating conscious perception. In this study, we compared the visual resolution for object size of the visuomotor and the perceptual system. Methodology/Principal Findings: In Experiment 1, participants discriminated the size of pairs of objects once through perceptual judgments and once by grasping movements toward the objects. Notably, the actual size differences were set below the Just Noticeable Difference (JND). We found that grasping trajectories reflected the actual size differences between the objects regardless of the JND. This pattern was observed even in trials in which the perceptual judgments were erroneous. The results of an additional control experiment showed that these findings were not confounded by task demands. Participants were not aware, therefore, that their size discrimination via grasp was veridical. Conclusions/Significance: We conclude that human resolution is not fully tapped by perceptually determined thresholds

Analysis of epidermal growth factor receptor expression as a predictive factor for response to gefitinib (‘Iressa’, ZD1839) in non-small-cell lung cancer

Gefitinib ('Iressa', ZD1839) is an orally active epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that has demonstrated antitumour activity and favourable tolerability in Phase II studies. We investigated whether EGFR expression levels could predict for response to gefitinib in patients with advanced non-small-cell lung cancer (NSCLC), who received gefitinib (250 mg day(-1)) as part of a worldwide compassionate-use programme. Tissue samples were analysed by immunohistochemistry to assess membrane EGFR immunoreactivity. Of 147 patients enrolled in our institution, 50 patients were evaluable for assessment of both clinical response and EGFR expression. The objective tumour response rate was 10% and disease control was achieved in 50% of patients. Although high EGFR expression was more common in squamous-cell carcinomas than adenocarcinomas, all objective responses were observed in patients with adenocarcinoma. Response and disease control with gefitinib were not associated with high EGFR expression. Overall, median survival was 4 months, and the 1-year survival rate was 18%. Strong EGFR staining correlated with shorter survival time for all patients. Gefitinib demonstrated promising clinical activity in this group of patients with NSCLC. These results have also shown that EGFR expression is not a significant predictive factor for response to gefitinib

Differential modulation of corticospinal excitability during haptic sensing of 2-D patterns vs. textures

<p>Abstract</p> <p>Background</p> <p>Recently, we showed a selective enhancement in corticospinal excitability when participants actively discriminated raised 2-D symbols with the index finger. This extra-facilitation likely reflected activation in the premotor and dorsal prefrontal cortices modulating motor cortical activity during attention to haptic sensing. However, this parieto-frontal network appears to be finely modulated depending upon whether haptic sensing is directed towards material or geometric properties. To examine this issue, we contrasted changes in corticospinal excitability when young adults (n = 18) were engaged in either a roughness discrimination on two gratings with different spatial periods, or a 2-D pattern discrimination of the relative offset in the alignment of a row of small circles in the upward or downward direction.</p> <p>Results</p> <p>A significant effect of task conditions was detected on motor evoked potential amplitudes, reflecting the observation that corticospinal facilitation was, on average, ~18% greater in the pattern discrimination than in the roughness discrimination.</p> <p>Conclusions</p> <p>This differential modulation of corticospinal excitability during haptic sensing of 2-D patterns vs. roughness is consistent with the existence of preferred activation of a visuo-haptic cortical dorsal stream network including frontal motor areas during spatial vs. intensive processing of surface properties in the haptic system.</p

Fix Your Eyes in the Space You Could Reach: Neurons in the Macaque Medial Parietal Cortex Prefer Gaze Positions in Peripersonal Space

Interacting in the peripersonal space requires coordinated arm and eye movements to visual targets in depth. In primates, the medial posterior parietal cortex (PPC) represents a crucial node in the process of visual-to-motor signal transformations. The medial PPC area V6A is a key region engaged in the control of these processes because it jointly processes visual information, eye position and arm movement related signals. However, to date, there is no evidence in the medial PPC of spatial encoding in three dimensions. Here, using single neuron recordings in behaving macaques, we studied the neural signals related to binocular eye position in a task that required the monkeys to perform saccades and fixate targets at different locations in peripersonal and extrapersonal space. A significant proportion of neurons were modulated by both gaze direction and depth, i.e., by the location of the foveated target in 3D space. The population activity of these neurons displayed a strong preference for peripersonal space in a time interval around the saccade that preceded fixation and during fixation as well. This preference for targets within reaching distance during both target capturing and fixation suggests that binocular eye position signals are implemented functionally in V6A to support its role in reaching and grasping

Grasping Kinematics from the Perspective of the Individual Digits: A Modelling Study

Grasping is a prototype of human motor coordination. Nevertheless, it is not known what determines the typical movement patterns of grasping. One way to approach this issue is by building models. We developed a model based on the movements of the individual digits. In our model the following objectives were taken into account for each digit: move smoothly to the preselected goal position on the object without hitting other surfaces, arrive at about the same time as the other digit and never move too far from the other digit. These objectives were implemented by regarding the tips of the digits as point masses with a spring between them, each attracted to its goal position and repelled from objects' surfaces. Their movements were damped. Using a single set of parameters, our model can reproduce a wider variety of experimental findings than any previous model of grasping. Apart from reproducing known effects (even the angles under which digits approach trapezoidal objects' surfaces, which no other model can explain), our model predicted that the increase in maximum grip aperture with object size should be greater for blocks than for cylinders. A survey of the literature shows that this is indeed how humans behave. The model can also adequately predict how single digit pointing movements are made. This supports the idea that grasping kinematics follow from the movements of the individual digits