Modeling Transit Bus Fuel Consumption on the Basis of Cycle Properties

Abstract

ABSTRACT A method exists to predict heavy-duty vehicle fuel economy and emissions over an “unseen” cycle or during unseen on-road activity on the basis of fuel consumption and emissions data from measured chassis dynamometer test cycles and properties (statistical parameters) of those cycles. No regression is required for the method, which relies solely on the linear association of vehicle performance with cycle properties. This method has been advanced and examined using previously published heavy-duty truck data gathered using the West Virginia University heavy-duty chassis dynamometer with the trucks exercised over limited test cycles. In this study, data were available from a Washington Metropolitan Area Transit Authority emission testing program conducted in 2006. Chassis dynamometer data from two conventional diesel buses, two compressed natural gas buses, and one hybrid diesel bus were evaluated using an expanded driving cycle set of 16 or 17 different driving cycles. Cycle properties and vehicle fuel consumption measurements from three baseline cycles were selected to generate a linear model and then to predict unseen fuel consumption over the remaining 13 or 14 cycles. Average velocity, average positive acceleration, and number of stops per distance were found to be the desired cycle properties for use in the model. The methodology allowed for the prediction of fuel consumption with an average error of 8.5% from vehicles operating on a diverse set of chassis dynamometer cycles on the basis of relatively few experimental measurements. It was found that the data used for prediction should be acquired from a set that must include an idle cycle along with a relatively slow transient cycle and a relatively high speed cycle. The method was also applied to oxides of nitrogen prediction and was found to have less predictive capability than for fuel consumption with an average error of 20.4%. IMPLICATIONS It is unclear what direction the next major regulation will take, but greenhouse gas emissions are at the forefront of concern for mobile power sources. From a regulatory perspective for inventory purposes or from that of a fleet owner for reducing costs, a robust but simple model for estimation of fuel consumption is desired. This study developed a modeling methodology to predict the fuel consumption of a vehicle exercised over unseen cycles using limited chassis dynamometer data, reducing heavy-duty chassis dynamometer costs. This approach may also help to compare emissions or fuel economy between vehicles that were not tested over the same set of driving cycles.