Title :
LQG Optimal Control Applied to On-Board Energy Management System of All-Electric Vehicles
Author :
Florescu, Adrian ; Bratcu, Antoneta Iuliana ; Munteanu, Iulian ; Rumeau, Axel ; Bacha, Seddik
Author_Institution :
Grenoble Electr. Eng. Lab., Domaine Univ., St. Martin d´Hères, France
Abstract :
This paper proposes a general frequency-separation-based strategy of coordinating power sources within off-grid applications. The application chosen to illustrate this strategy is an electric vehicle equipped with two power sources-a battery and an ultracapacitor (UC)-for which coordination problem can be formulated and solved as a linear quadratic Gaussian (LQG) optimal control problem. The two power sources are controlled to share the stochastically variable load according to their respective frequency range of specialization: low-frequency variations of the required power are supplied by the main source, the battery, whereas high-frequency variations are provided by the UC. The studied system is a bilinear one; it can be modeled as a linear parameter varying system. An LQG-based optimal control structure is designed and coupled with a gain-scheduling structure to cover the entire operating range. In this way, load regulation performance and the variations of battery current are conveniently traded off to preserve battery reliability and lifetime. Real-time experiments on a dedicated test rig-based on employing a real UC-validate the proposed optimal power flow management approach.
Keywords :
electric vehicles; energy management systems; linear quadratic Gaussian control; load flow; optimal control; LQG optimal control; all-electric vehicles; battery current; frequency-separation-based strategy; linear parameter varying system; linear quadratic Gaussian optimal control problem; on-board energy management system; optimal power flow management; power sources; Batteries; Energy management; Equations; Hafnium; Optimal control; Vehicles; Voltage control; Electric vehicles (EVs); energy management; gain scheduling; linearization techniques; optimal control; real-time simulation; real-time simulation.;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2014.2372472