NDLI: A stochastic control strategy for hybrid electric vehicles

Content Provider	IEEE Xplore Digital Library
Author	Chan-Chiao Lin Huei Peng Grizzle, J.W.
Copyright Year	2004
Description	Author affiliation: Dept. of Mech. Eng., Michigan Univ., MI, USA (Chan-Chiao Lin; Huei Peng; Grizzle, J.W.)
Abstract	The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.
Sponsorship	American Automatic Control Council IFAC
Starting Page	4710
Ending Page	4715
File Size	426747
Page Count	6
File Format	PDF
ISBN	0780383354
ISSN	07431619
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Stochastic processes Hybrid electric vehicles Supervisory control Energy management Optimal control Fuel economy Vehicle dynamics Power demand Markov processes Dynamic programming
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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