Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles

Energy management strategy is crucial in improving the fuel economy of hybrid electric vehicles (HEVs). This paper targets at evaluating the role of velocity forecast in the adaptive equivalent consumption minimization strategies (ECMS) for HEVs. A neural network based velocity predictor is constructed to forecast the short-term future driving behaviors by learning from history data. Then the velocity predictor is combined with adaptive-ECMS to provide temporary driving information for real-time equivalence factor (EF) adaptation. Compared with traditional adaptive-ECMS, which uses historical driving profile for EF estimation, the proposed strategy is able to foresee the change of the driving behaviors and adjust the EF more reasonably. Simulation results show that, compared with traditional adaptive-ECMS, the proposed improvement with velocity forecast incorporated is able to achieve better fuel economy and more stable battery state of charge (SOC) trajectory, with a fuel consumption reduction by over 3%.

[1]  Martin T. Hagan,et al.  Neural network design , 1995 .

[2]  G Ripaccioli,et al.  A stochastic model predictive control approach for series hybrid electric vehicle power management , 2010, Proceedings of the 2010 American Control Conference.

[3]  Ali Emadi,et al.  Modern electric, hybrid electric, and fuel cell vehicles : fundamentals, theory, and design , 2009 .

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

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

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

[7]  Daniel Krajzewicz,et al.  Traffic Simulation with SUMO – Simulation of Urban Mobility , 2010 .

[8]  M. Krstić,et al.  Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model , 2014 .

[9]  J.M. Miller,et al.  Hybrid electric vehicle propulsion system architectures of the e-CVT type , 2006, IEEE Transactions on Power Electronics.

[10]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  Chia-Nan Ko,et al.  Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm , 2009, Neurocomputing.

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

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

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

[15]  Guillermo R. Bossio,et al.  Optimization of power management in an hybrid electric vehicle using dynamic programming , 2006, Math. Comput. Simul..

[16]  Hosam K. Fathy,et al.  A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles , 2011, IEEE Transactions on Control Systems Technology.

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

[18]  F. R. Salmasi,et al.  Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends , 2007, IEEE Transactions on Vehicular Technology.

[19]  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.

[20]  Fan Yang,et al.  Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification , 2003, IEEE Trans. Neural Networks.

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

[22]  Pierluigi Pisu,et al.  A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles , 2007, IEEE Transactions on Control Systems Technology.

[23]  Thierry-Marie Guerra,et al.  Equivalent consumption minimization strategy for parallel hybrid powertrains , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

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