An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx

Giovannardi, Emanuele; Brusa, Alessandro; Petrone, Boris; Cavina, Nicolò; Corti, Enrico; Barichello, Massimo

doi:10.1109/metroautomotive57488.2023.10219122

Cited by 3 publications

References 12 publications

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The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

Brusa,

Shethia,

Petrone

et al. 2024

Energies

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The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

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The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

Brusa,

Shethia,

Petrone

et al. 2024

Energies

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AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle

Giovannardi,

Brusa,

Petrone

et al. 2024

SAE Int. J. Engines

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<div>This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy. Furthermore, the study investigates two sensor setups: a standard configuration and an optimized one, which incorporates an additional lambda probe in the exhaust line after the main catalyst. The results indicate a significant enhancement in performance for NOx and CO estimations with the introduction of the third lambda probe, while HC results remain relatively unchanged. Additionally, the AI model is tested on two different electronic control unit (ECU) software calibrations, yielding excellent results in both cases. This suggests that machine learning models are robust to control software variation and can be used to optimize software calibrations in a virtual environment, reducing the reliance on extensive experimental testing. Moreover, the AI model’s performance demonstrates compatibility with real-time implementation. In conclusion, this work establishes the viability and efficiency of AI techniques in accurately estimating tailpipe emissions from an engine in an industrial context. The study showcases the potential for AI to contribute to emission estimation and optimization processes, offering a promising pathway for an innovative industrial practice.</div>

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Development of an Automatic Pipeline for Data Analysis and Pre-Processing for Data Driven-Based Engine Emission Modeling in a Real Industrial Application

Petrone,

Giovannardi,

Brusa

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement. The proposed pre-processing pipeline shows an improvement in terms of accuracy, demonstrating the utility of using large training data which cover a wide set of vehicle maneuvers. Thus, custom designed tests are performed for dataset enrichment, allowing the model to predict non-conventional conditions of aftertreatment systems inefficiency. Real case applications of the proposed model are exposed, such as emission estimation in non-measurable conditions, virtual assessment of the impact of new control strategy calibration on emissions, alignment of emission measurements with all other vehicle signals. Finally, a Principal Component Analysis-based algorithm is developed, to assess the epistemic uncertainty of the model and the prediction reliability during inference.</div></div>

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An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx

Cited by 3 publications

References 12 publications

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle

Development of an Automatic Pipeline for Data Analysis and Pre-Processing for Data Driven-Based Engine Emission Modeling in a Real Industrial Application

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