The Role of Velocity Forecasting in Adaptive-ECMS for Hybrid Electric Vehicles

Abstract The energy management strategy is crucial in improving the fuel economy of hybrid electric vehicles (HEVs). This paper targets at evaluating the role of velocity forecasting in the adaptive equivalent consumption minimization strategies (ECMS) of HEVs. By predicting the short-term future velocity through a data-driven approach, the energy management controller is able to optimize the equivalence factor online and adapt to current driving situations intelligently. Compared with basic adaptive ECMS approach without velocity forecasting abilities, the proposed strategy is able to foresee the change of the driving behaviors and adjust the equivalence factor more reasonably. Simulation results show that the adaptive ECMS with velocity forecast ability is more sensitive to the driving profiles, and the resultant fuel economy is improved by over 3%.

[1]  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.

[2]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[3]  Stefano Di Cairano,et al.  MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle , 2012, IEEE Transactions on Control Systems Technology.

[4]  Hosam K. Fathy,et al.  A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles , 2008 .

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

[6]  Xiaosong Hu,et al.  Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles , 2015, IEEE Transactions on Control Systems Technology.

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

[8]  Huei Peng,et al.  Modeling and Control of a Power-Split Hybrid Vehicle , 2008, IEEE Transactions on Control Systems Technology.

[9]  Lino Guzzella,et al.  Optimal control of parallel hybrid electric vehicles , 2004, IEEE Transactions on Control Systems Technology.

[10]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

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