A comparative analysis of route-based power management strategies for real-time application in plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are promising alternatives for sustainable transportation. Because of their embedded battery pack, they can significantly enhance the fuel economy compared to conventional vehicle. Improving the power management strategy can exploit the full potential of a PHEV powertrain and reduce the fuel consumption considerably. In this study, we compare two outstanding optimal route-based control approaches: model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for different levels of trip information. The controllers are fine-tuned for the 2013 Toyota Prius plug-in hybrid and implemented as a high-fidelity model in Autonomie software. To evaluate the designed control systems, the rule-based controller of Autonomie is considered as a benchmark power management system. The result of simulations show that MPC and A-ECMS lead to an approximately equal fuel economy, and they can improve the fuel consumption of this PHEV up to 10% in comparison to the benchmark controller. Both strategies can be implemented in real-time although A-ECMS is 15% faster than MPC.

[1]  Nasser L. Azad,et al.  Traction-Motor Power Ratio and Speed Trajectory Optimization for Power Split PHEVs Using Route Information , 2012 .

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

[3]  Nasser L. Azad,et al.  Predictive Power Management Strategy for a PHEV Based on Different Levels of Trip Information , 2012 .

[4]  Giorgio Rizzoni,et al.  A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[5]  Ilya V. Kolmanovsky,et al.  Optimally controlling Hybrid Electric Vehicles using path forecasting , 2009, 2009 American Control Conference.

[6]  Liuping Wang,et al.  Model Predictive Control System Design and Implementation Using MATLAB , 2009 .

[7]  Luigi del Re,et al.  Chances and Challenges in Automotive Predictive Control , 2010 .

[8]  G. Rizzoni,et al.  Effects of different PHEV control strategies on vehicle performance , 2009, 2009 American Control Conference.

[9]  Yang Bin,et al.  Multi-information integrated trip specific optimal power management for plug-in hybrid electric vehicles , 2009, 2009 American Control Conference.

[10]  James Wollaeger.  ITS in Energy Management Systems of PHEV's , 2012 .

[11]  Mahyar Vajedi,et al.  Intelligent power management of plug–in hybrid electric vehicles, part I: real–time optimum SOC trajectory builder , 2014 .

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

[13]  Mahyar Vajedi,et al.  Intelligent power management of plug-in hybrid electric vehicles, part II: real-time route based power management , 2014 .

[14]  Nasser L. Azad,et al.  On real-time optimal control of a series Hybrid Electric Vehicle with an ultra-capacitor , 2012, 2012 American Control Conference (ACC).

[15]  Nasser L. Azad,et al.  Design and Evaluation of a Predictive Powertrain Control System for a PHEV to Improve Fuel Economy and Emissions , 2014 .

[16]  John McPhee,et al.  Design and Evaluation of a Real-time Fuel-optimal Control System for Series Hybrid Electric Vehicles , 2012 .

[17]  Nasser L. Azad,et al.  An optimal power management strategy for power split plug-in hybrid electric vehicles , 2012 .

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

[19]  Metin Gokasan,et al.  A Novel Model Validation and Estimation Approach for Hybrid Serial Electric Vehicles , 2007, IEEE Transactions on Vehicular Technology.

[20]  Giorgio Rizzoni,et al.  Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles , 2011, IEEE Transactions on Vehicular Technology.