Title :
Nonlinear Model Predictive Control for power-split Hybrid Electric Vehicles
Author :
Borhan, H.A. ; Zhang, Chen ; Vahidi, Ardalan ; Phillips, Anthony M. ; Kuang, Ming L. ; Di Cairano, S.
Author_Institution :
Dept. of Mech. Eng., Univ. of Clemson, Clemson, SC, USA
Abstract :
In this paper, a causal optimal controller based on Nonlinear Model Predictive Control (NMPC) is developed for a power-split Hybrid Electric Vehicle (HEV). The global fuel minimization problem is converted to a finite horizon optimal control problem with an approximated cost-to-go, using the relationship between the Hamilton-Jacobi-Bellman (HJB) equation and the Pontryagin´s minimum principle. A nonlinear MPC framework is employed to solve the problem online. Different methods for tuning the approximated minimum cost-to-go as a design parameter of the MPC are discussed. Simulation results on a validated high-fidelity closed-loop model of a power-split HEV over multiple driving cycles show that with the proposed strategy, the fuel economies are improved noticeably with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and a linear time-varying MPC controller previously developed by the authors.
Keywords :
closed loop systems; hybrid electric vehicles; nonlinear control systems; predictive control; Hamilton-Jacobi-Bellman equation; Pontryagin´s minimum principle; Powertrain System Analysis Toolkit; approximated minimum cost-to-go; causal optimal controller; finite horizon optimal control; fuel economies; global fuel minimization problem; high-fidelity closed-loop model; nonlinear model predictive control; power-split hybrid electric vehicles; Biological system modeling; Engines; Fuels; Hybrid electric vehicles; Mathematical model; System-on-a-chip;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5718075