Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

• Main Authors: Chen, C. (Author), Chen, Z. (Author), Dong, J. (Author), Li, M. (Author), Li, X. (Author), Rao, M. (Author), Wang, L. (Author), Xiong, K. (Author), Xu, J. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Data driven; deep learning; Deep learning; Forecasting; Fuel cell system; Long short-term memory; Mean square error; multi-step prediction; Multi-step prediction; Network layers; Prediction modelling; Prediction problem; Recursive prediction; solid oxide fuel cell; Solid oxide fuel cells (SOFC); Solid-oxide fuel cell; state prediction; State prediction; System state
• Online Access: View Fulltext in Publisher

 A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.