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https://hdl.handle.net/2440/132401
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Type: | Journal article |
Title: | Robust fuzzy model predictive control for energy management systems in fuel cell vehicles |
Author: | Shen, D. Lim, C.C. Shi, P. |
Citation: | Control Engineering Practice, 2020; 98:104364-1-104364-12 |
Publisher: | Elsevier |
Issue Date: | 2020 |
ISSN: | 0967-0661 1873-6939 |
Statement of Responsibility: | Di Shen, Cheng-Chew Lim, Peng Shi |
Abstract: | Fuel cell vehicle combines the benefits of fuel cell stack and energy storage system to achieve fuel economy and zero emission. The energy management system is vital to the hybrid vehicle systems since it regulates power flow from the fuel cell stack and energy storage system. In this paper, we design an energy management scheme in fuel cell vehicle systems. By using optimal control principle, we aim to reduce hydrogen consumption while maintaining battery state of charge under practical operating constraints and uncertain future power demand. Fuzzy modelling approach is employed to describe the nonlinear power plant and a robust model predictive based control is designed to achieve the desired system performance. Moreover, traffic condition is incorporated into the energy management controller design to further improve the system performance. The effectiveness and advantages of the proposed control scheme are illustrated by a simulator developed based on real-world experimental data. |
Keywords: | Energy management; hybrid vehicle; fuzzy control; model predictive control; fuel cell; electric vehicle |
Description: | Available online 10 March 2020 |
Rights: | © 2020 Elsevier Ltd. All rights reserved. |
DOI: | 10.1016/j.conengprac.2020.104364 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1016/j.conengprac.2020.104364 |
Appears in Collections: | Aurora harvest 8 Electrical and Electronic Engineering publications |
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