Abstract
The serially-correlated nature of engine operation is overlooked in the vehicular fuel and emission modeling literature. Furthermore, enabling the calibration and use of time-series models for instrument-independent eco-driving applications requires reliable forecast aggregation procedures. To this end, an ensemble time-series machine-learning methodology is developed using data collected through extensive field experiments on a fleet of 35 vehicles. Among other results, it is found that Long Short-Term Memory (LSTM) architecture is the best fit for capturing the dynamic and lagged effects of speed, acceleration, and grade on fuel and emission rates. The developed vehicle-specific ensembles outperformed state-of-the-practice benchmark models by a significant margin and the category-specific models outscored the vehicle-specific sub-models by an average margin of 6%. The results qualify the developed ensembles to work as representatives for vehicle categories and allows them to be utilized in both eco-driving services as well as environmental assessment modules.
Highlights
Training meso- and micro-scale models for estimating vehicular Fuel Consumption Rate (FCR) and Emission Rates (ER) is a fundamental step towards developing reliable eco-driving assistance services
Many of them depend on Internal Engine Variables (IEV) to achieve acceptable levels of accuracy [1,2,3,4,5,6,7], which eliminates their applicability in instrument-independent eco-driving services or integration with traffic simulation models
As the second part of a series of studies, we started with the validation of predictions by Environmental Protection Agency (EPA)’s comprehensive emissions model, MOVES [13], we introduced a cascaded machinelearning methodology for FCR estimation using large amounts of data collected through on-road measurements [17]
Summary
Training meso- and micro-scale models for estimating vehicular Fuel Consumption Rate (FCR) and Emission Rates (ER) is a fundamental step towards developing reliable eco-driving assistance services. Such models could be used as a part of environmental-assessment modules in the existing traffic simulation software. The micro-scale models focus on understanding instantaneous correlations between the state of the vehicle and the fuel and emission rates. They provide richer information for transportation environmental analysis. The existing micro-scale fuel and emission models suffer from five major issues. Many of them depend on Internal Engine Variables (IEV) to achieve acceptable levels of accuracy [1,2,3,4,5,6,7], which eliminates their applicability in instrument-independent eco-driving services or integration with traffic simulation models
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