Efficient real driving emissions calibration of automotive powertrains under operating uncertainties

Wasserburger, Alexander; Didcock, Nico; Hametner, Christoph

doi:10.1080/0305215x.2021.1989589

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…The process automation framework developed here can be applied to properly handle these kinds of disturbances and to operate the entire plant in an optimal and efficient way. A next step to improve the developed method lies in the data-driven modeling of the disturbances using generalized Gaussian mixture distributions, see [52].…”

Section: Discussionmentioning

confidence: 99%

Efficient Sensitivity-Based Cooperation Concept for Hierarchical Multilayer Process Automation of Steam-Powered Plants

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Efficient Sensitivity-Based Cooperation Concept for Hierarchical Multilayer Process Automation of Steam-Powered Plants

et al. 2022

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“…For instance, Isermann et al outline an approach utilizing optimization algorithms in an offline simulation for base calibration focusing on emissions and fuel consumption [43]. Wasserburger et al propose a methodology in [44,45] for generating test cycles from engine-operating points and using these as input values for an optimization algorithm that adjusts the calibration of specific functions to optimize vehicle emissions. Moreover, offline powertrain models use nearest neighbor clustering algorithms to frontload the engine base calibration in [46], and a methodology for model-based smooth calibration is presented in [47].…”

Section: State-of-the-art-novel Methods For Vehicle Calibrationmentioning

confidence: 99%

Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration

Krysmon,

Pischinger,

Claßen

et al. 2024

Future Transportation

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Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated.

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A Stochastic Approach to the Analysis of Pollutant Emissions and Fuel Consumption of Motor Vehicles Driven in Real Traffic

Andrych-Zalewska,

Chłopek,

Lasocki

et al. 2024

Applied Sciences

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In real road traffic, combustion engines of motor vehicles operate in dynamic conditions. Under such conditions, significant time variability in the values describing engine operations is observed, especially in terms of rotational speed and torque. Therefore, it is possible to model such conditions as probabilistic and to treat the properties of combustion engines in these conditions as stochastic processes. This paper presents a stochastic approach to the analysis of pollutant emission and fuel consumption test results of a motor vehicle driven in real traffic conditions. The empirical data were obtained from tests conducted on a car with a spark-ignition engine equipped with mobile on-board measuring equipment. The scope of the investigations covered the domains of time, frequency and process values. In the time domain, statistical characteristics of the processes were analyzed to explore potential correlations between them. In the frequency domain, the power spectral density of the processes was determined. In the process values domain, the emphasis was placed on examining the probability density of processes. A large diversification of the determined characteristics was found, in particular for vehicle velocity, engine operating states and the processes of pollutant emissions and fuel consumption. For practical reasons, the results of the correlation studies were particularly valuable, as they enabled assessment of the effects of taking action to reduce emissions of various pollutants.

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Efficient real driving emissions calibration of automotive powertrains under operating uncertainties

Cited by 6 publications

References 25 publications

Efficient Sensitivity-Based Cooperation Concept for Hierarchical Multilayer Process Automation of Steam-Powered Plants

Efficient Sensitivity-Based Cooperation Concept for Hierarchical Multilayer Process Automation of Steam-Powered Plants

Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration

A Stochastic Approach to the Analysis of Pollutant Emissions and Fuel Consumption of Motor Vehicles Driven in Real Traffic

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