LAPSE:2023.29808
Published Article
LAPSE:2023.29808
Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control
Xiao Tang, Chunsheng Wang, Yukun Hu, Zijian Liu, Feiliang Li
April 13, 2023
Abstract
An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.
Keywords
adaptive fuzzy PID (AFPID), granular function fuzzy PID (GFPID), oxygen excess ratio, oxygen starvation, proton exchange membrane fuel cell (PEMFC)
Suggested Citation
Tang X, Wang C, Hu Y, Liu Z, Li F. Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control. (2023). LAPSE:2023.29808
Author Affiliations
Tang X: School of Automation, Central South University, Changsha 410083, China; Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China [ORCID]
Wang C: School of Automation, Central South University, Changsha 410083, China; Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China
Hu Y: Department of Civil, Environment & Geomatic Engineering, University College London, London WC1E 6BT, UK
Liu Z: School of Automation, Central South University, Changsha 410083, China; Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China
Li F: School of Automation, Central South University, Changsha 410083, China; Hunan Xiangjiang Artificial Intelligence College, Changsha 410083, China
Journal Name
Energies
Volume
14
Issue
4
First Page
1140
Year
2021
Publication Date
2021-02-21
ISSN
1996-1073
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Original Submission
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PII: en14041140, Publication Type: Journal Article
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LAPSE:2023.29808
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https://doi.org/10.3390/en14041140
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