Rule-corrected energy management strategy for hybrid electric vehicles based on operation-mode prediction

Abstract The energy crisis and exhaust emissions are serious problems that are largely related to road traffic. One solution to these threats is to switch from traditional gasoline-based vehicles to hybrid electric vehicles (HEVs) or electric vehicles (EVs), which also has the benefit of promoting a more sustainable economy. Energy management strategies (EMS) for HEVs or EVs play an important role in improving fuel economy. As a stochastic prediction method, a Markov chain, has been widely used in the prediction of driving conditions, but the application of a Markov chain in the prediction of HEV-operating modes in a rule-based EMS has rarely been presented in the literature. In addition, the threshold selection of rule-based EMS is usually based on experience and it is difficult to achieve optimal performance. In this paper, the impact of operation-mode prediction on rule-based EMS fuel economy has been explored to achieve real-time on-line corrections to motor and engine torque and to enhance their capacity for on-line optimization. Thus, a new EMS for HEV has been proposed based on operation-mode prediction using a Markov chain, which determines the on-line correction of torque distribution between the engine and the electric motor. The Markov decision processes and transition matrix are introduced first, and then, the transition probability matrix and torque correction model are established using the MATLAB/Simulink platform. The results of the simulation show that the proposed approach provides a 13.1% and 9.6% improvement in real fuel consumption under the New European Driving Cycle and the Urban Dynamometer Driving Schedule respectively, in comparison with the conventional rule-based control strategies without operation-mode prediction. The results also show that the proposed control strategy can significantly enhance the real-time optimization control performance of the EMS while maintaining the state of charge within a reasonable range.

[1]  Hamid Khayyam,et al.  Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification , 2012 .

[2]  Jun Li,et al.  When to switch to a hybrid electric vehicle: A replacement optimisation decision , 2017 .

[3]  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.

[4]  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.

[5]  Kodjo Agbossou,et al.  Optimization-based energy management strategy for a fuel cell/battery hybrid power system , 2016 .

[6]  Angelika Heinzel,et al.  Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System , 2015 .

[7]  Huei Peng,et al.  Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle , 2013 .

[8]  Hongwen He,et al.  Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming , 2017 .

[9]  Chun Wang,et al.  An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle , 2017 .

[10]  Jiayi Cao,et al.  Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle , 2018 .

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

[12]  Norbert Ligterink,et al.  Stochastic Real-World Drive Cycle Generation Based on a Two Stage Markov Chain Approach , 2015 .

[13]  H. A. Bekhet,et al.  Modelling electric vehicle usage intentions: an empirical study in Malaysia , 2015 .

[14]  M Maarten Steinbuch,et al.  Rule-based energy management strategies for hybrid vehicles , 2007 .

[15]  Giorgio Rizzoni,et al.  Optimal energy management in series hybrid electric vehicles , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[16]  Hao Mu,et al.  A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries , 2017 .

[17]  Giorgio Rizzoni,et al.  An Iterative Markov Chain Approach for Generating Vehicle Driving Cycles , 2011 .

[18]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results , 2005, IEEE Transactions on Vehicular Technology.

[19]  Datong Qin,et al.  A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition , 2017 .

[20]  Hongwen He,et al.  Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus , 2017 .

[21]  John A. Gubner.  Probability and Random Processes for Electrical and Computer Engineers , 2006 .

[22]  Zeyu Chen,et al.  Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application , 2018 .

[23]  W. P. M. H. Heemels,et al.  Energy management strategies for vehicular electric power systems , 2005, IEEE Transactions on Vehicular Technology.

[24]  Ahmed Cheriti,et al.  An Optimal Control Solved by Pontryagin's Minimum Principle Approach for a Fuel Cell/Supercapacitor Vehicle , 2014, 2014 IEEE Electrical Power and Energy Conference.

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

[26]  Hongwen He,et al.  A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[27]  Hongwen He,et al.  Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles , 2018, IEEE Access.

[28]  Le Yi Wang,et al.  A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .

[29]  Zeyu Chen,et al.  Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions , 2016 .

[30]  Mcf Tijs Donkers,et al.  An equivalent consumption minimization strategy based on 1-step look-ahead stochastic dynamic programming , 2015 .