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Control‐relevant parameter estimation application to a model‐based PHEV power management system
Author(s) -
Taghavipour Amir,
Masoudi Ramin,
Azad Nasser L.,
McPhee John
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2320
Subject(s) - powertrain , model predictive control , controller (irrigation) , control engineering , power management , computer science , automotive industry , control (management) , control theory (sociology) , power (physics) , engineering , torque , artificial intelligence , physics , quantum mechanics , aerospace engineering , biology , agronomy , thermodynamics
Summary Explicit model predictive control approach is a promising approach to fulfill automotive real‐time controls requirements. A key factor in the performance and real‐time capabilities of a predictive model‐based controller is the accuracy of the control‐oriented model. The control‐oriented model should capture the essential dynamics of the real plant and be adequately simple to make the controller implementable on a commercial hardware with limited memory and computational capabilities. In this study, control‐relevant parameter estimation is used to find a control‐oriented model for a real‐time predictive power management system for a plug‐in hybrid powertrain. Simulations, which are conducted using an equation‐based model of the powertrain, demonstrate a significant improvement of the power management system performance by improving the control‐oriented model with no effect on real‐time capabilities of the controller.