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Determination of Principal Variables for Prediction of Fuel Economy using Principal Component Analysis
Technical Paper
2019-26-0359
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The complexity of Urban driving conditions and the human behavior introduces undesired variabilities while establishing Fuel economy for a vehicle. These variabilities pose a great challenge while trying to determine that single figure for assessment of vehicle’s fuel efficiency on an urban driving cycle. This becomes even more challenging when two or more vehicles are simultaneously evaluated with respect to a reference vehicle. The attempt to fit a generalized linear model, between Fuel Economy as predicted variable and components of a driving cycle as predictor variables produced oxymoronic and counter-institutive results. This is primarily due to existence of multi-collinearity among the predictor variables. The context of the study is to consider the event of driving on a cycle as a random sampling experiment. The outcome of a driving cycle is summarized into a list of predictor variables or components. The aim of this study is to reduce the variables which are strongly co-related using various statistical techniques, the primary and the most effective technique being Principal Component Analysis. The selected variables or principal components are then used to predict F.E using a machine learning algorithm.
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Authors
Citation
Gadde, R., Sriganesh, R., C, K., Lalasure, S. et al., "Determination of Principal Variables for Prediction of Fuel Economy using Principal Component Analysis," SAE Technical Paper 2019-26-0359, 2019, https://doi.org/10.4271/2019-26-0359.Data Sets - Support Documents
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References
- Sovran , G. and Blaser , D. A Contribution to Understanding Automotive Fuel Economy and its Limits SAE Technical Paper 2003-01-2070 2003 10.4271/2003-01-2070
- Wang , L. , Duran , A. , Gonder , J. , and Kelly , K. Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties SAE Technical Paper 2015-01-2812 2015 10.4271/2015-01-2812
- Lebeau , P. , de Cauwer , C. , van Mierlo , J. , Macharis , C. et al. Conventional, Hybrid, or Electric Vehicles: Which Technology for an Urban Distribution Centre? Sci. World J. 302867:1 302867:11 2015
- Ma , H. , Xie , H. , Chen , S. , Yan , Y. et al. Effects of Driver Acceleration Behavior on Fuel Consumption of City Buses SAE Technical Paper 2014-01-0389 2014 10.4271/2014-01-0389
- De Cauwer , C. , Van Mierlo , J. , and Coosemans , T. Energy Consumption Prediction for Electric Vehicles Based on Real-World Data Energies 8 8 8573 8593 2015 10.3390/en8088573
- Evan , L. Driver Behavior Effects on Fuel Consumption in Urban Driving Human Factors 21 4 389 398 1979 10.1177/001872087902100401