Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt

This paper presents an adaptive supervisory controller, based on Pontryagin’s Minimum Principle (PMP), for on-line energy management optimization of a plug-in hybrid electric vehicle. Using minimum driving information, such as the total trip length and the average cycle speed, the proposed algorithm relies on adaptation of the control parameter from state of charge feedback. The proposed strategy is referred in the paper to as Adaptive-PMP (A-PMP). The new controller is applied to a detailed forward vehicle simulator of the plug-in hybrid Chevrolet Volt manufactured by General Motors, where an experimentally validated LG Chem battery model is used. The strategy we propose aims at achieving a blended trajectory of the state of charge to minimize the consumed fuel, resulting in an overall better performance than the actual Charge Depleting/Charge Sustaining (CD/CS) strategy currently used on-board of the vehicle. A comparative analysis of three strategies, i.e., the optimal one (PMP), the proposed one (A-PMP) and the in-vehicle one (CD/CS), is conducted in simulation which shows that improvement above 20% in fuel consumption may be achieved when the proposed algorithm is used instead of the current on-board strategy.

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

[2]  Uwe Dieter Grebe,et al.  Voltec – The Propulsion System for Chevrolet Volt and Opel Ampera , 2011 .

[3]  Chen Zhang,et al.  Route Preview in Energy Management of Plug-in Hybrid Vehicles , 2012, IEEE Transactions on Control Systems Technology.

[4]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

[5]  Binggang Cao,et al.  Component sizing optimization of plug-in hybrid electric vehicles , 2011 .

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

[7]  Tomaž Katrašnik,et al.  Analytical method to evaluate fuel consumption of hybrid electric vehicles at balanced energy content of the electric storage devices , 2010 .

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

[9]  Huei Peng,et al.  Energy management strategy for a parallel hybrid electric truck , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[10]  Robert Willis,et al.  Principles of Mechanism , 2007 .

[11]  Simona Onori,et al.  Capacity and power fade cycle-life model for plug-in hybrid electric vehicle lithium-ion battery cells containing blended spinel and layered-oxide positive electrodes , 2015 .

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

[13]  Alan G. Holmes,et al.  The GM “Voltec” 4ET50 Multi-Mode Electric Transaxle , 2011 .

[14]  Yi-Hsuan Hung,et al.  An integrated optimization approach for a hybrid energy system in electric vehicles , 2012 .

[15]  Pierluigi Pisu,et al.  A control benchmark on the energy management of a plug-in hybrid electric vehicle , 2014 .

[16]  Lino Guzzella,et al.  Predictive Reference Signal Generator for Hybrid Electric Vehicles , 2009, IEEE Transactions on Vehicular Technology.

[17]  Namwook Kim,et al.  Sufficient conditions of optimal control based on Pontryagin’s minimum principle for use in hybrid electric vehicles , 2012 .

[18]  H. Saunders,et al.  Book Reviews : Fracture and Fatigue Control in Structures - Application of Fracture Mechanics: S.T. Rolfe and J.M. Barsom Prentice-Hall, Inc., Englewood Cliffs, NJ, 1977 , 1979 .

[19]  Fabrizio Martini,et al.  Methodology Procedure for Hybrid Electric Vehicles Design , 2011 .

[20]  Viktor Larsson,et al.  Benefit of route recognition in energy management of plug-in hybrid electric vehicles , 2012, 2012 American Control Conference (ACC).

[21]  Dimitri Jeltsema,et al.  Proceedings Of The 2000 American Control Conference , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[22]  Huei Peng,et al.  Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle , 2011, IEEE Transactions on Control Systems Technology.

[23]  Hamid Khayyam,et al.  Adaptive intelligent energy management system of plug-in hybrid electric vehicle , 2014 .

[24]  Hai Yu,et al.  Trip-Oriented Energy Management Control Strategy for Plug-In Hybrid Electric Vehicles , 2014, IEEE Transactions on Control Systems Technology.

[25]  Simona Onori,et al.  Adaptive energy management strategy calibration in PHEVs based on a sensitivity study , 2013 .

[26]  Tony Markel,et al.  PHEV Energy Storage Performance/Life/Cost Trade-Off Analysis (Presentation) , 2008 .

[27]  Simona Onori,et al.  ECMS as a realization of Pontryagin's minimum principle for HEV control , 2009, 2009 American Control Conference.

[28]  Giorgio Rizzoni,et al.  Energy management for plug-in hybrid electric vehicles using equivalent consumption minimisation strategy , 2010 .

[29]  Simona Onori,et al.  Analysis of energy management strategies in plug-in hybrid electric vehicles: Application to the GM Chevrolet Volt , 2013, 2013 American Control Conference.

[30]  Hans P. Geering,et al.  Optimal control with engineering applications , 2007 .

[31]  L. Guzzella,et al.  Control of hybrid electric vehicles , 2007, IEEE Control Systems.

[32]  Simona Onori,et al.  A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles , 2011 .

[33]  Simona Onori,et al.  A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications , 2012 .

[34]  Xiaosong Hu,et al.  Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes , 2013 .