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

Giovannardi, Emanuele; Brusa, Alessandro; Petrone, Boris; Cavina, Nicolò; Tonelli, Roberto; Kitsopanidis, Ioannis

doi:10.4271/03-17-04-0029

Cited by 3 publications

References 29 publications

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Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

Mądziel

2024

Energies

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In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO2, CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R2: 0.76), while polynomial regression excelled for CO2 (R2: 0.93). For warm engines, polynomial regression performed best for CO2 (R2: 0.95), and gradient boosting delivered results for THC (R2: 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

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Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

Mądziel

2024

Energies

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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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Neural-Network-Based Modeling of SCR Systems for Emission Simulation: A Comprehensive Approach

Sapio,

Aglietti,

Ferreri

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Selective Catalytic Reduction (SCR) systems are crucial for automotive emissions control, as they are essential to comply with stringent emissions regulations. Model-based SCR controls are used to minimize NOx emissions in a broad range of real-word driving scenarios, constantly adapting the urea injection to diverse load and temperature operating conditions, also accounting for different catalyst ageing status. In this framework, Neural Networks (NN) based models offer a promising alternative to reduced-order physical models or map-based controls.</div><div class="htmlview paragraph">This study introduces a hybrid modeling approach for SCR systems, leveraging the integration of machine learning techniques with detailed physics-based models. A high fidelity 1D-CFD plant model of a SCR catalyst, previously calibrated on experimental data, was used as digital twin of the real component. A standardized simulation protocol was defined to virtually characterize the SCR thermal and chemical behavior under the full range of operating conditions typically covered during the real operation of the system. The generated dataset, including hard-to-measure physical quantities such as the catalyst wall temperature and the ammonia storage, was used to train and validate the neural network models. In particular, Recurrent Neural Networks (RNN) were used to mimic catalyst wall temperature and ammonia storage temporal trends, while Feed Forward Neural Networks (FFNN) were applied to model SCR outlet temperature and species concentrations such as NOx and NH3.</div><div class="htmlview paragraph">The NN-based SCR model predictive capabilities were assessed against experimental driving cycles data. The results show that the NN model is able to accurately capture the non-linear characteristics of the system behavior, even under the highly transient conditions typical of real-driving scenarios, thus confirming the reliability of the proposed methodology.</div></div>

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

Cited by 3 publications

References 29 publications

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

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

Neural-Network-Based Modeling of SCR Systems for Emission Simulation: A Comprehensive Approach

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