Integrated Intelligent Energy ›› 2024, Vol. 46 ›› Issue (7): 53-62.doi: 10.3969/j.issn.2097-0706.2024.07.007

• Energy Storage Technology • Previous Articles     Next Articles

Spatiotemporal distributed parameter modeling of solid oxide electrolysis cells

DOU Zhenlan1(), LI Jiawen1(), ZHANG Chunyan1, CAI Zhenqi1, YUAN Benfeng2, JIA Kunqi1, XIAO Guoping2, WANG Jianqiang2   

  1. 1. State Grid Shanghai Power Supply Company, Shanghai 200122, China
    2. Shanghai Institute of Applied Physics, CAS, Shanghai 201800, China
  • Received:2024-04-17 Revised:2024-06-14 Published:2024-07-25
  • Supported by:
    Science and Technology of SGCC(520911220003)

Abstract:

There are complex thermoelectric couplings in solid oxide electrolysis cells (SOEC)operating under high temperature environment. Thus, temperature control and voltage control are crucial to the stable and safe operation of SOEC stacks. In view that SOEC stacks are complex, nonlinear and spatiotemporal distributed systems, a spatiotemporal distributed model for the temperature and voltage of an SOEC is constructed based on the spatiotemporal least square support vector machine(LS-SVM). A kernel function is adopted to represent the spatial correlations at different locations along the flow channel, and dynamic regression equation is used to represent the temporal features of the temperature and voltage of the SOEC stack. A mechanism model of the SOEC stack is established in Simulink to obtain the sample data,which are used for the training and testing of the spatiotemporal distributed model. The simulation results show that the developed model can effectively predict the spatiotemporal distributions of the temperature and voltage in SOEC and has an excellent generalization ability, which can guide the further optimization and control of electrolysis cells.

Key words: solid oxide electrolysis cell, temperature control, spatiotemporal LS-SVM, distributed dynamic prediction model, electrolysis system, water electrolysis for hydrogen production

CLC Number: