A Data-Driven Short-Term Voltage Prediction Model for Fuel Cells under Multiple Conditions 2022-01-7050

This paper presents a data-driven short-term voltage prediction model for high-power fuel cells under three operating conditions: idle, rated, and variable load. Long short-term memory (LSTM) recurrent neural network has good performance in voltage prediction, but the accuracy of prediction for volatile voltage data is significantly reduced. In this paper, the obvious fluctuations caused by voltage recovery due to resting are discussed, and the proposed model is optimized for this phenomenon from two perspectives: the method I is the wavelet algorithm is used to extract features from the raw voltage data, and then the decomposed waveform is predicted using LSTM, lastly the predicted results are synthesized into the final voltage trend using inverse wavelet; the method II is to divide the voltage loss into reversible loss and irreversible loss parts, the reversible loss part is predicted by exponential model, while the irreversible loss part by LSTM, then the final results are obtained by superimposing the two parts. The results show that method I has better optimization effect under variable load and rated conditions, and method II has higher accuracy for idle condition. With the optimized model to predict the voltage more accurately, it can help to adjust the unsuitable operation condition timely, making the voltage decline in slower speed, that improves the durability of the fuel cells.