Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118576
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Energy management of fuel cell hybrid vehicle based on partially observable Markov decision process
Author: Shen, D.
Lim, C.
Shi, P.
Bujlo, P.
Citation: IEEE Transactions on Control Systems Technology, 2020; 28(2):318-330
Publisher: IEEE
Issue Date: 2020
ISSN: 1063-6536
1558-0865
Statement of
Responsibility: 
Di Shen, Cheng-Chew Lim, Peng Shi and Piotr Bujlo
Abstract: This paper presents a nonmyopic approach for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programming under a partially observable Markov decision process based framework. We propose an average-reward approximator to minimise a long- run average cost instead of utilizing a model to predict the specific future power request. Thus, the dependency between the closed- loop performance properties of the system and the accuracy of the model to predict the future power request is decoupled in the new energy management strategy for fuel cell hybrid vehicles. The designed energy management strategy for fuel cell hybrid vehicles includes a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling method, and an action selector system through rollout algorithm with a convex programming based policy. The performance achieved by the developed approach is shown in simulation via real-world driving experiments and compared to the ones obtained by other three benchmark schemes.
Keywords: Convex programing; energy management; fuel cell hybrid vehicle; Markov chain Monte Carlo (MCMC); model predictive control (MPC); partially observable Markov decision process (POMDP)
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030103081
DOI: 10.1109/TCST.2018.2878173
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.