Real Driving Emission (RDE) testing for motorcycles presents unique challenges due to the motorcycle’s lightweight construction, limited mounting space, and sensitivity to added mass and aerodynamic drag. Full-functional automotive Portable Emission Measurement Systems (PEMS), while highly accurate, are often impractical for two-wheelers as their weight and size can alter driving resistances, fuel consumption, and emission profiles, but also complicate installation and probably effect the drivability of the vehicle. To address these limitations, lightweight alternatives such as Mini-PEMS and ultralightweight alternatives such as Sensor-based Emission Measurement Systems (SEMS) offer compact, low-power solutions tailored for small vehicles. SEMS are typically equipped with lower cost sensors and low-tech gas conditioning systems compared to PEMS. Due to this these systems may not meet regulatory homologation requirements. Nevertheless, they provide justifiable accuracy for many real-world applications.
This paper explores the working principles and sensor technologies used in Mini-PEMS and SEMS, highlighting key trade-offs between size reduction, energy efficiency, and measurement precision. Mini-PEMS reduce complexity by employing analyzers with a minimalized conditioning system. SEMS, moreover, leverages smart sensor integration to deliver real-time emission assessments with minimal impact on vehicle dynamics.
To assess their reliability, Mini-PEMS and SEMS are evaluated against laboratory-, homologation-grade equipment under controlled conditions. Accuracy analyses reveal specific limitations, but also demonstrate that these systems provide sufficiently robust data for many practical applications.
By balancing accuracy with real-world feasibility, Mini-PEMS and SEMS offer a viable path for emission testing in scenarios where full-scale PEMS are impractical. Their adoption could expand the scope of RDE assessment, particularly for low-powered two-wheelers, ultimately supporting more accessible and widespread emission monitoring.