The shift toward sustainable agricultural machinery has highlighted diesel and CNG/bio-CNG dual-fuel systems as promising solutions. Yet, standardized methods for assessing their fuel economy (FE) and performance under real-world operating conditions remain underdeveloped.
This study introduces an event-based duty cycle synthesis to bridge this gap. Field data encompassing ploughing, rotavating, sowing, transportation, and idling were systematically acquired using CAN bus interfaces and dataloggers, capturing engine load, wheel torque, throttle dynamics, and fuel consumption. A high-fidelity duty cycle was subsequently devised, integrating the frequency, duration, and load intensity of field operations.
This cycle was replicated in a controlled laboratory environment via a chassis dynamometer and engine-in-the-loop (EiL) platform, enabling precise, repeatable evaluation of dual-fuel (diesel-CNG) and pure diesel modes. Key metrics—including brake-specific fuel consumption (BSFC), indicated thermal efficiency, transient power response, and gas substitution ratio (GSR) were analyzed. The lab-to-field alignment validated the method’s efficacy in replicating real-world conditions while ensuring scalability.
By harmonizing field heterogeneity with lab precision, this approach establishes a novel benchmark for dual-fuel tractor assessment, offering insights into optimizing fuel flexibility and operational efficiency in agriculture.