| Autor: |
Jungme Park, Zhihang Chen, Kiliaris, Leonidas, Kuang, Ming L., Masrur, M. Abul, Phillips, Anthony M., Murphey, Yi Lu |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Vehicular Technology; Nov2009, Vol. 58 Issue 9, p4741-4756, 16p, 5 Black and White Photographs, 5 Diagrams, 5 Charts, 5 Graphs |
| Abstrakt: |
Previous research has shown that current driving conditions and driving style have a strong influence over a vehicle's fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT&TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT&TC level. An online University of Michigan-Dearborn intelligent power controller (UMD_IPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMD_IPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMD_IPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMD_IPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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