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Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles
Author(s) -
Fu Jiangtao,
Song Shuzhong,
Fu Zhumu,
Ma Jianwei
Publication year - 2018
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1759
Subject(s) - powertrain , controller (irrigation) , model predictive control , control theory (sociology) , state of charge , fuel efficiency , optimal control , nonlinear system , engineering , control engineering , prime mover , computer science , trajectory , battery (electricity) , control (management) , automotive engineering , power (physics) , torque , artificial intelligence , mathematical optimization , physics , mathematics , quantum mechanics , astronomy , agronomy , biology , thermodynamics
One purpose of the control strategy for a parallel hybrid electric vehicle (PHEV) is to control the state of charge (SOC) of the battery to achieve a maximum powertrain efficiency. Because of the nonlinearity of the powertrain, the control strategy should implement nonlinear optimization in real time. This paper presents a new hierarchical optimal control design to execute real‐time optimization on the basis of a model predictive control concept. The proposed control architecture suggests a two‐layer control subjected to the related constraints according to the changing rate of different parameters. The prime controller optimizes the best trajectory of the state of charge according to the load detected by a load prediction module, while the second controller will apply the output signal from the prime controller along with the signal from the prediction module and the driver inputs to control the powertrain. Additionally, a new methodology to predict the future load imposed on the wheels is introduced. Through simulation and experiment results, it is shown that the proposed prediction control can effectively reduce fuel consumption and exhaust emission.