AI Summary & Key Takeaways
AI InsightMoreover, the presented result establishes a solid foundation for accurate performance evaluationof small-sized passenger cars in Nanjing and cities in the similar traffic situation.
📌 3 Key Takeaways
Özet
Driving cycle is an important concept in quantifying vehicle emissions, and it is expected to adequately representactual driving patterns of vehicle so that to obtain reliable estimates of vehicle emission. However, there isincreasing concern about the gap between the actual driving conditions and standard driving cycles used forvarious vehicle certification and regulatory authorities. In this paper, the local driving cycle (LDC) for smallsizedpassenger cars in Nanjing is obtained by statistical combination of micro-trips by principle componentanalysis and clustering analysis method, then compared with the existing driving cycles of other cities in China.Significant differences have been observed between the driving cycles, emphasizing the need for the constructionof LDCs for different cities. The developed LDC is simulated on chassis dynamometer to analyze the impact ofspeed and acceleration on emission rates for different pollutants. Meanwhile, the emission factors and rates of asmall-sized passenger car in four driving patterns (acceleration, deceleration, cruising, idling) is obtained in LDCand other standard driving cycles for comparison purposes. The result reveals that the LDC is more appropriatefor accurate estimation of vehicle exhaust emissions of small-sized passenger cars in Nanjing than standarddriving cycles. Moreover, the presented result establishes a solid foundation for accurate performance evaluationof small-sized passenger cars in Nanjing and cities in the similar traffic situation.