Intelligent vehicle power management through neural learning

Power management for the Hybrid Electric Vehicle (HEV) is a challenging problem because of the dual-power-source nature of HEV design and implementation. In this paper, we present an Intelligent Power Controller, UMD_IPC, trained with a machine learning approach to provide optimal power flow for in-vehicle operations. The UMD_IPC is implemented in a HEV model provided by PSAT simulation environment, and its performances on three drive cycles are close to the optimal results generated by Dynamic Programming.

[1]  Hao Ying,et al.  Derivation and Experimental Validation of a Power-Split Hybrid Electric Vehicle Model , 2006, IEEE Transactions on Vehicular Technology.

[2]  Huei Peng,et al.  A stochastic control strategy for hybrid electric vehicles , 2004, Proceedings of the 2004 American Control Conference.

[3]  Theo Hofman,et al.  Energy analysis of hybrid vehicle powertrains , 2004 .

[4]  Giorgio Rizzoni,et al.  General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles , 2001 .

[5]  Stephen P. Boyd,et al.  Finding Ultimate Limits of Performance for Hybrid Electric Vehicles , 2000 .

[6]  Yaobin Chen,et al.  Optimisation design of an energy management strategy for hybrid vehicles , 2006 .

[7]  Tony Markel,et al.  Optimizing Energy Management Strategy and Degree of Hybridization for a Hydrogen Fuel Cell SUV , 2001 .

[8]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[9]  Keith Wipke,et al.  HEV Control Strategy for Real-Time Optimization of Fuel Economy and Emissions , 2000 .

[10]  Mutasim A. Salman,et al.  Fuzzy logic control for parallel hybrid vehicles , 2002, IEEE Trans. Control. Syst. Technol..

[11]  Lino Guzzella,et al.  A Real-Time Optimal Control Strategy for Parallel Hybrid Vehicles with On-Board Estimation of the Control Parameters , 2004 .

[12]  Thierry-Marie Guerra,et al.  Control of a parallel hybrid powertrain: optimal control , 2004, IEEE Transactions on Vehicular Technology.

[13]  Yi Lu Murphey,et al.  Intelligent Vehicle Power Management: An Overview , 2008, Computational Intelligence in Automotive Applications.

[14]  Yi Lu Murphey,et al.  Multiclass pattern classification using neural networks , 2004, ICPR 2004.

[15]  Huei Peng,et al.  Power management strategy for a parallel hybrid electric truck , 2003, IEEE Trans. Control. Syst. Technol..

[16]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[17]  U. Epa,et al.  Development of Speed Correction Cycles , 1997 .

[18]  Yaobin Chen,et al.  Analysis and Design of an Optimal Energy Management and Control System for Hybrid Electric Vehicles , 2002 .

[19]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process , 2005, IEEE Transactions on Vehicular Technology.

[20]  Eva Ericsson,et al.  Variability in urban driving patterns , 2000 .

[21]  Arsie Ivan,et al.  Optimization of Supervisory Control Strategy for Parallel Hybrid Vehicle with Provisional Load Estimate , 2004 .

[22]  Yeong-Il Park,et al.  Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition , 2002 .

[23]  Yi Lu Murphey,et al.  Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion , 2009, IEEE Transactions on Vehicular Technology.

[24]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results , 2005, IEEE Transactions on Vehicular Technology.