Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Mądziel, Maksymilian

doi:10.3390/en16196928

Cited by 8 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each cluster, three models were assessed: a neural network multilayer perceptron (MLP) model, a nonlinear regression (NLR) model, and a look-up table (LT) approach. Artificial intelligence methods have been applied to model the energy usage of electric vehicles [64][65][66]. This particular study [64] outlines the methodology used to develop a model for electric vehicle (EV) energy consumption, allowing for rapid result generation and the creation of energy maps.…”

Section: Discussionmentioning

confidence: 99%

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

Mądziel

2024

Energies

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Mądziel

2024

Energies

View full text Add to dashboard Cite

show abstract

“…However, concerning the analysis of the available literature, it is worth noting that the number of studies focussing on emissions modelling, especially using artificial intelligence methods, is increasing. There are several studies that address emissions modelling for vehicles powered by gasoline [32,33], diesel [34,35], LPG [36,37] and hybrid combinations thereof [38].…”

Section: Introductionmentioning

confidence: 99%

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

Mądziel

2024

Energies

View full text Add to dashboard Cite

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.

show abstract

“…However, concerning the analysis of the available literature, it is worth noting that the number of studies focussing on emissions modelling, especially using artificial intelligence methods, is increasing. There are several studies that address emissions modelling for vehicles powered by gasoline [30,31], diesel [32,33], LPG [34] and hybrid combinations thereof [35].…”

Section: Introductionmentioning

confidence: 99%

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

Mądziel

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Cited by 8 publications

References 57 publications

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

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

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

Contact Info

Product

Resources

About