Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/42668
Title: Fuzzy optimal energy management for fuel cell and supercapacitor systems using neural network based driving pattern recognition
Authors: Zhang, Ridong
Tao, Jili
Zhou, Huiyu
First Published: 13-Jul-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Fuzzy Systems, 2018
Abstract: A novel adaptive energy management strategy is proposed for real time power split between fuel cells and supercapacitors in a hybrid electric vehicle in view of the fact that driving patterns greatly affect fuel economy. The driving pattern recognition (DPR) is achieved based on the features extracted from the historical velocity window with a multi-layer perceptron neural network. After the DPR has been obtained, an adaptive fuzzy energy management controller is utilized for power split according to the required power for vehicle running. In order to prolong the fuel cell lifetime whilst decreasing the hydrogen consumption, a genetic algorithm is applied to optimize critical factors such as adaptive gains and fuzzy membership function parameters for several standard driving cycles. In the proposed method, the future driving cycles are not required and the current driving pattern can be successfully recognized, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions. Compared with conventional energy management systems, the proposed framework can ensure the state of charge of supercapacitors within the desired limit.
DOI Link: 10.1109/TFUZZ.2018.2856086
ISSN: 1941-0034
Links: https://ieeexplore.ieee.org/abstract/document/8410796/
http://hdl.handle.net/2381/42668
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Institute of Electrical and Electronics Engineers (IEEE). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
ALL_17-Tfs-0598.pdfPost-review (final submitted author manuscript)811.99 kBAdobe PDFView/Open


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