| Abstrakt: |
This paper addresses modeling and predicting vehicle fuel economy based on simple vehicle characteristics. The models are identified using a historical vehicle fuel economy data set. First, the use of least squares regression analysis is pursued, and a mathematical model is created that is capable of predicting highway fuel economy based on six vehicle characteristics: engine displacement volume, vehicle maximum power, vehicle maximum torque, vehicle weight, vehicle wheelbase, and vehicle cross sectional area. Then neural network models are developed and shown to achieve higher accuracy as compared to the regression models, with 70 percent of the data in the validation data set predicted within 2 mpg. Furthermore, we demonstrate that by employing a hybrid architecture, where vehicles are first clustered and then separate models are developed for vehicle clusters, the model accuracy can be improved further. [ABSTRACT FROM PUBLISHER] |
