This paper presents the development of second-bysecond vehicle emission models under hot stabilised settings and variable speed, acceleration, air-tofuel ratio and torque conditions using Australian fleet data. The models were developed using 27 passenger and light commercial vehicles with more than 64 500 instantaneous observations obtained from dynamometer tests. Both neural network and regression analysis models were developed to predict fuel consumption and pollutant emissions at different levels of speed, acceleration, air-to-fuel ratio and torque. The results showed that modelling individual vehicle emissions provides superior results to an aggregate modelling approach in which a 'characteristic' vehicle is used to represent dissimilar vehicle populations. The best performing models from the two modelling approaches showed high degrees of correlation between predicted and actual data (e.g. 96%-98% accuracy for prediction of fuel consumption, 85%-87% accuracy for prediction of hydrocarbons, 70%-90% accuracy for prediction of carbon monoxide, and 88%-97% accuracy for prediction of nitrogen oxides (NOx)), depending on the type of passenger and light commercial vehicles being modelled. Finally, the paper highlights some of the advantages and shortcomings of the two modelling approaches when considering a universal model to predict multiple emissions outputs. The models developed in this study will provide researchers and practitioners with a better understanding of the environmental impacts resulting from a wide range of transport schemes in the Australian context.
Vehicle emission models using Australian fleet data
Road & transport research ; 24 , 1
2015
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB: | 135/7005/1710 | |
BKL: | 55.84 Straßenverkehr |
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