Real-time predictive control strategy for a plug-in hybrid electric powertrain
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Date
2015-08-01
Authors
Taghavipour, Amir
Azad, Nasser L.
McPhee, John
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.
Description
The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.mechatronics.2015.04.020 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Automotive powertrain, Explicit model predictive control, Plug-in hybrid electric vehicle, Power management