Title :
Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion
Author :
Park, Jungme ; Chen, Zhihang ; Kiliaris, Leonidas ; Kuang, Ming L. ; Masrur, M. Abul ; Phillips, Anthony M. ; Murphey, Yi Lu
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
Abstract :
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.
Keywords :
automobiles; dynamic programming; learning (artificial intelligence); neurocontrollers; optimal control; power control; power transmission (mechanical); real-time systems; University of Michigan-Dearborn; dynamic programming optimization; facility-specific drive cycles; fuel consumption; fuel efficiency; intelligent vehicle power control; machine learning; neural learning algorithm; neural network; optimal control; powertrain systems analysis toolkit; real-time vehicle power control; road type prediction; traffic congestion prediction; vehicle power management; Fuel economy; machine learning; road type and traffic congestion (RT&TC) level prediction; vehicle power management;
Journal_Title :
Vehicular Technology, IEEE Transactions on
DOI :
10.1109/TVT.2009.2027710