Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods

Mądziel, Maksymilian

doi:10.1007/s11356-023-31022-5

Cited by 7 publications

(4 citation statements)

References 81 publications

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“…Another example of a study that also uses artificial intelligence methods for the prediction of CO 2 is [49]. This paper investigates CO 2 emission modelling using data from the portable emission measurement system (PEMS) and artificial intelligence methods.…”

Section: Discussionmentioning

confidence: 99%

Modelling CO2 Emissions from Vehicles Fuelled with Compressed Natural Gas Based on On-Road and Chassis Dynamometer Tests

Mądziel

2024

Energies

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In response to increasingly stringent global environmental policies, this study addresses the pressing need for accurate prediction models of CO2 emissions from vehicles powered by alternative fuels, such as compressed natural gas (CNG). Through experimentation and modelling, one of the pioneering CO2 emission models specifically designed for CNG-powered vehicles is presented. Using data from chassis dynamometer tests and road assessments conducted with a portable emission measurement system (PEMS), the study employs the XGBoost technique within the Optuna Python programming language framework. The validation of the models produced impressive results, with R2 values of 0.9 and 0.7 and RMSE values of 0.49 and 0.71 for chassis dynamometer and road test data, respectively. The robustness and precision of these models offer invaluable information to transportation decision-makers engaged in environmental analyses and policymaking for urban areas, facilitating informed strategies to mitigate vehicular emissions and foster sustainable transportation practices.

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Section: Discussionmentioning

confidence: 99%

Modelling CO2 Emissions from Vehicles Fuelled with Compressed Natural Gas Based on On-Road and Chassis Dynamometer Tests

Mądziel

2024

Energies

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“…Another example of a study that also uses artificial intelligence methods for the prediction of CO2 is [44]. This paper investigates CO2 emission modelling using data from the portable emission measurement system (PEMS) and artificial intelligence methods.…”

Section: Discussionmentioning

confidence: 99%

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Mądziel

2024

Preprint

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Contemporary global policy, driven by concern for the environment, imposes increasingly stringent goals for reducing CO2 emissions. Therefore, there is an urgent need to develop effective models that allow an accurate prediction of CO2 emissions from vehicles powered by alternative fuels such as CNG. This article presents the process of creating one of the first models of CO2 emission for a vehicle powered by CNG. Emission modelling is based on data obtained from chassis dynamometer tests and road tests using the portable emission measurement system (PEMS). CO2 emission modelling was conducted in Python programming language using the Optuna framework for the XGBoost technique. The models obtained were validated, with indicators R2 0.9 and RMSE 0.49 for data from chassis dynamometer tests, and R2 0.7, RMSE 0.71 for road test data. This work has the potential to be used by transportation decision makers involved in environmental analyses and policymaking for urban areas.

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“…It is the first study to specifically address the modeling of NOx and THC emissions from such vehicles. The only similar work is [61]. This article presents a novel methodology to measure and model CO 2 emissions in vehicles equipped with start-stop technology.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights

Mądziel

2024

Energies

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Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start–stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine’s stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.

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Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods

Cited by 7 publications

References 81 publications

Modelling CO2 Emissions from Vehicles Fuelled with Compressed Natural Gas Based on On-Road and Chassis Dynamometer Tests

Modelling CO2 Emissions from Vehicles Fuelled with Compressed Natural Gas Based on On-Road and Chassis Dynamometer Tests

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Quantifying Emissions in Vehicles Equipped with Energy-Saving Start–Stop Technology: THC and NOx Modeling Insights

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