TY - JOUR
T1 - Dynamic hierarchical modeling and control strategy of high temperature proton exchange electrolyzer cell system
AU - Zhao, Dongqi
AU - Xia, Zhiping
AU - Guo, Meiting
AU - He, Qijiao
AU - Xu, Qidong
AU - Li, Xi
AU - Ni, Meng
N1 - Funding Information:
M. NI thanks the grants (Project Number: PolyU 152064/18E and N_PolyU552/20 ) from Research Grant Council, University Grants Committee , Hong Kong SAR. Xi Li thanks the National Natural Science Foundation of China (grant numbers: U2066202 , 61873323 ), the Science, Technology and Innovation Commission of Shenzhen Municipality (grant number: JCYJ20210324115606017 ), and the National Science Centre of the Republic of Poland for SONATA project (grant number: 2018/31/D/ST8/00123 ).
Publisher Copyright:
© 2022 Hydrogen Energy Publications LLC
PY - 2022/6/26
Y1 - 2022/6/26
N2 - High temperature proton exchange membrane electrolyzer cells (HT-PEMECs) show faster reaction kinetics than the low temperature PEMECs (LT-PEMECs) and are suitable for utilizing waste heat from the industry. However, dynamic modeling and control of HT-PEMECs are still lacking, which is critical for integrating the HT-PEMECs with fluctuating renewable power. In this study, hierarchical models are developed to investigate the transient behavior of the HT-PEMEC system with hydrogen recirculation. It is observed that the maximum efficiency point of the reference power can be reached by cooperatively adjusting the current density and anode inlet gas flow rate, and the application of artificial neural networks can accurately predict the operating conditions at the points of maximum efficiency. Moreover, the proposed cooperative model predictive control strategy not only improves the efficiency (about 1.2%) during dynamic processes but also avoids the problem of reactant starvation. This study provides useful information to understand the dynamic behaviors of HT-PEMECs driven by excess renewable power.
AB - High temperature proton exchange membrane electrolyzer cells (HT-PEMECs) show faster reaction kinetics than the low temperature PEMECs (LT-PEMECs) and are suitable for utilizing waste heat from the industry. However, dynamic modeling and control of HT-PEMECs are still lacking, which is critical for integrating the HT-PEMECs with fluctuating renewable power. In this study, hierarchical models are developed to investigate the transient behavior of the HT-PEMEC system with hydrogen recirculation. It is observed that the maximum efficiency point of the reference power can be reached by cooperatively adjusting the current density and anode inlet gas flow rate, and the application of artificial neural networks can accurately predict the operating conditions at the points of maximum efficiency. Moreover, the proposed cooperative model predictive control strategy not only improves the efficiency (about 1.2%) during dynamic processes but also avoids the problem of reactant starvation. This study provides useful information to understand the dynamic behaviors of HT-PEMECs driven by excess renewable power.
KW - Cooperative model predictive control
KW - Hierarchical system model
KW - Hydrogen recirculation system
KW - Multiphysics analysis
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=85132378586&partnerID=8YFLogxK
U2 - 10.1016/j.ijhydene.2022.05.067
DO - 10.1016/j.ijhydene.2022.05.067
M3 - Journal article
AN - SCOPUS:85132378586
SN - 0360-3199
VL - 47
SP - 22302
EP - 22315
JO - International Journal of Hydrogen Energy
JF - International Journal of Hydrogen Energy
IS - 53
ER -
