Abstract

This dissertation investigates the real-world emissions and fuel consumption of two-wheeler applications, addressing the significant discrepancies between laboratory and actual driving conditions. The study emphasizes the necessity for accurate measurement methodologies to evaluate gaseous emissions and fuel efficiency in real driving scenarios. A comprehensive methodology was developed, incorporating on-road testing and chassis dynamometer measurements, to assess the emissions of motorcycles under various driving conditions. Key findings reveal that real-world emissions often exceed regulatory limits, highlighting the inadequacies of current testing protocols. Additionally, a simulation model utilizing artificial neural networks was created to predict emissions and fuel economy based on driving dynamics. The results indicate that driver behavior and traffic conditions substantially influence emissions, suggesting that future regulations should consider these factors for more accurate assessments. This research contributes to the understanding of two-wheeler emissions and provides a framework for improving regulatory standards, ultimately supporting global efforts to reduce harmful emissions and enhance environmental sustainability.

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