Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion

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...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2009-11, Vol.58 (9), p.4741-4756
Main Authors: Jungme Park, Zhihang Chen, Kiliaris, L., Kuang, M.L., Masrur, M.A., Phillips, A.M., Murphey, Y.L.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Communication system traffic control
Computer science
control theory
systems
Congestion
Connectionism. Neural networks
Electric, optical and optoelectronic circuits
Electrical engineering. Electrical power engineering
Electrical machines
Electronics
Energy management
Exact sciences and technology
Fuel consumption
Fuel economy
Fuels
Intelligent vehicles
Machine learning
Management training
Mathematical models
Neural networks
Optimal control
Power control
Regulation and control
road type and traffic congestion (RT&TC) level prediction
Road vehicles
Roads
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Teletraffic
Traffic engineering
Traffic flow
vehicle power management
Vehicles
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Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2009.2027710