Title: Real-time implementable optimal control strategy for hybrid electric vehicles energy management: application to medium-duty commercial vehicles
Authors: Chinmaya Patil; Payam Naghshtabrizi; Yueyue Deng; Harsh Vinjamoor
Addresses: Corporate Research and Technology, Eaton Corporation, USA ' Corporate Research and Technology, Eaton Corporation, USA ' Corporate Research and Technology, Eaton Corporation, USA ' Corporate Research and Technology, Eaton Corporation, USA
Abstract: This paper presents a model predictive control approach called predictive power management to optimise the hybrid electric vehicle energy management controls over a horizon for medium-duty commercial applications. The two challenges associated with optimisation over a horizon including: a) reliable vehicle speed duty cycle prediction; b) fast computation to make optimal control decisions in real-time, are addressed. A novel dynamic programming-based approach, where the cost function is decoupled into a computationally expensive offline part and a fast computing online part to enable real-time implementation, forms the optimisation algorithm. A vehicle speed prediction algorithm based on duty cycle learning is developed for commercial applications with repetitive duty cycles. The simulation results of implementing the predictive power management demonstrate significant fuel economy improvement over a baseline energy management strategy. The impact of length of vehicle speed prediction horizon on the fuel savings is discussed.
Keywords: hybrid electric vehicles; HEVs; energy management; real-time control; dynamic programming; optimal control; speed prediction; medium-duty commercial vehicles; vehicle speed; hybrid vehicles; model predictive control; MPC; simulation; fuel economy.
International Journal of Powertrains, 2015 Vol.4 No.3, pp.225 - 242
Received: 05 Mar 2014
Accepted: 13 Dec 2014
Published online: 16 Sep 2015 *