This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (DP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine DP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine DP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine DP and low fresh air flowrates, while the second mode is driven by high engine DP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.
This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.
Particulate matter spikes occurring during transient engine operation have important health implications. This paper investigates the root cause of particulate matter spikes in modern electronically controlled diesel engines that impose strict fuel-Oxygen ratio limits during the turbocharger lag period. It is proposed that these spikes can be significantly reduced by improved estimation of transient charge flow through the engine. Through transient data analysis and with the aid of transient data based empirical models, it has been shown that the fuel-Oxygen ratio restrictions imposed by contemporary engine controllers are ineffective during transients because of temporary but large differences between exhaust and intake manifold pressures during aggressive transients resulting in inaccurate volumetric efficiency and charge flow estimation. Steady state experiments with artificially generated high engine manifold pressure differentials have been conducted to support this hypothesis. The engine manifold pressure differential hypothesis is a consequence of previous investigations to explain the baffling inability of empirical data based models to predict the magnitudes of transient particulate matter spikes. Accurate volumetric efficiency estimation during transients can make the fuel-Oxygen ratio limits more effective at reducing opacity spikes. It would also make model based transient calibration more useful by increasing the accuracy of particulate matter models and by directing any dynamic optimization process to mould calibratable surfaces to minimize engine manifold pressure differential spikes. Fuel efficiency benefits due to lower pumping losses during transients and lower regeneration penalties would also result.
A new modelling technique has been developed to aid steady state diesel engine calibration by accurately predicting engine system response and emissions at steady state operating conditions. This new modelling technique, referred to as the nearest neighbour multivariate localized regression (NNMLR) in this work, is built upon the particular localized regression technique for multiple independent variables developed by M. C. Sharp et al. at Cummins Inc., Columbus and referred to as the multivariate localized regression (MLR) technique in this work. Among other advantages, MLR has been demonstrated to generalize better than other similar data-driven empirical modelling techniques such as global regression. Although MLR has been proven and tested, it is computationally expensive which makes it unwieldy with current optimization schemes, particularly random search methods; NNMLR is significantly faster than MLR. Additionally, the primary localization parameter which remains fixed in MLR over the entire dataset is allowed to vary over the dataset in NNMLR. Therefore, in NNMLR it is the dataset that decides the degree of localization. This variable degree of localization is unique to each response being modelled and can change with data density and with the complexity of the relationship between the dependent and independent variables. This greater degree of localization makes NNMLR slightly more accurate than the current MLR scheme. The motivation for developing NNMLR was to reduce optimization routine runtimes while maintaining accuracy at MLR levels or even improving on these levels. The other modelling approach that promises similar or better accuracy levels and model runtimes is neural networks. Neural networks are increasingly being applied over a wide range of applications, hence they were examined for their suitability for modelling engine responses. Their robustness to noise, training data requirements, and ability to extrapolate were compared with localized and global regression. In the following sections, after background that describes the MLR technique, the algorithm for NNMLR is described. Some heuristics are unavoidable and these have been discussed and justified. Results have been presented for many datasets of different sizes over different sets of dependent variables, comparing MLR, NNMLR, global regression, and neural networks. In addition to performance over standard engine responses, test functions have been used to create pseudo-responses, so that noise can be added in a controlled manner. Accuracy and robustness of MLR, NNMLR, global regression, and neural networks over different levels of noisy data have been studied. The study shows how neural networks work better than other approaches for steady state calibration in terms of mean accuracy, maximum error, robustness to noise, training data size, ability to handle non-linearities as well as ability to extrapolate. However, localized regression (MLR and NNMLR) produces comparable performance and the reasons why a regression-based approac...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.