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Emission regulations are becoming tighter, and Real Driving Emissions (RDE) is proposed as a testing cycle for evaluating modern engine emissions under a wide operation range. For this reason, engine manufacturers have been developing a method to effectively assess engine performances and emissions under a wide range of transient conditions. Transient engine performances can be evaluated efficiently by applying time-series data created by chirp signals. However, when the time-series data produced by the chirp signal are used directly, the engine hardware may damage, and emission performances deteriorate drastically. It is therefore essential to develop a method to avoid these undesirable operating conditions. This work aims to develop an algorithm to avoid such unrealistic operation conditions for engine performance evaluation. A virtual diesel engine (VDE) model is developed based on a four-cylinder engine using GT-POWER software.

NOx emissions from diesel passenger vehicles affect the atmospheric environment. It is difficult to evaluate the NOx emissions influenced by environmental conditions such as humidity and temperature, traffic conditions, driving patterns, etc. In the authors’ previous study, real-driving experiments were performed on city and highway routes using a diesel passenger car with only an exhaust gas recirculation system. A statistical prediction model of NOx emissions was considered for simple estimations in the real world using instantaneous vehicle data measured by the portable emissions measurement system and global positioning system. The prediction model consisted of explanatory variables, such as velocity, acceleration, road gradient, and position of transmission gear. Using the explanatory variables, NOx emissions on the city and highway routes was well predicted using a diesel vehicle without NOx reduction devices.