Heavy vehicle fuel economy improvement using ultracapacitor power assist and preview-based MPC energy management

A heavy hybrid vehicle is considered in which an electric motor and ultracapacitor energy storage are used in a parallel hybrid configuration as a power assist to improve fuel economy. The ultracapacitor's high power capabilities make it a good choice for this application. The optimal control technique of Dynamic Programming (DP) is applied to obtain the "best possible" fuel economy for the vehicle over the driving cycle under pointwise-in-time hard system constraints. Attainable fuel economy improvements are illustrated using a real-time implementable Model Predictive Control (MPC) method using a simple model for predicting future torque demands. The incorporation of simulated telematic future information is also investigated to further improve the the fuel economy of the MPC method close to the DP-calculated maximum.

[1]  J.M. Miller,et al.  Ultracapacitor Plus Battery Energy Storage System Sizing Methodology for HEV Power Split Electronic CVT's , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[2]  H. A. Borhan,et al.  Model predictive control of a power-split Hybrid Electric Vehicle with combined battery and ultracapacitor energy storage , 2010, Proceedings of the 2010 American Control Conference.

[3]  Stephen P. Boyd,et al.  Receding Horizon Control , 2011, IEEE Control Systems.

[4]  Bo Egardt,et al.  Predictive energy management of a 4QT series-parallel hybrid electric bus , 2009 .

[5]  Kaijiang Yu,et al.  Model predictive control of a power-split hybrid electric vehicle system , 2012, Artificial Life and Robotics.

[6]  Chen Zhang,et al.  Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles , 2010, IEEE Transactions on Vehicular Technology.

[7]  John B. Heywood,et al.  Internal combustion engine fundamentals , 1988 .

[8]  D. Rotenberg,et al.  Ultracapacitor assisted powertrains: Modeling, control, sizing, and the impact on fuel economy , 2008, 2008 American Control Conference.

[9]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[10]  A. Bemporad,et al.  Model Predictive Control Design: New Trends and Tools , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[11]  Lino Guzzella,et al.  Vehicle Propulsion Systems: Introduction to Modeling and Optimization , 2005 .

[12]  Ilya V. Kolmanovsky,et al.  Ultracapacitor Assisted Powertrains: Modeling, Control, Sizing, and the Impact on Fuel Economy , 2011, IEEE Transactions on Control Systems Technology.

[13]  Dirk Abel,et al.  Comparison of Two Real-Time Predictive Strategies for the Optimal Energy Management of a Hybrid Electric Vehicle , 2007 .

[14]  A. Vahidi,et al.  A Decentralized Model Predictive Control Approach to Power Management of a Fuel Cell-Ultracapacitor Hybrid , 2007, 2007 American Control Conference.

[15]  Yaoyu Li,et al.  Optimal power management of plug-in HEV with intelligent transportation system , 2007, 2007 IEEE/ASME international conference on advanced intelligent mechatronics.

[16]  Behrang Asadi,et al.  Predictive Use of Traffic Signal State for Fuel Saving , 2009, CTS 2009.

[17]  W. Kwon,et al.  Receding Horizon Control: Model Predictive Control for State Models , 2005 .

[19]  Wahyudi,et al.  Comprehensive driving behavior model for intelligent transportation systems , 2008, 2008 International Conference on Computer and Communication Engineering.

[20]  Eduardo F. Camacho,et al.  Model Predictive Controllers , 2007 .

[21]  Tiecheng Wang,et al.  Simulation and performance analysis on an energy storage system for hybrid electric vehicle using ultracapacitor , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

[22]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.