Prediction of fuel cell degradation trends using long short term memory optimization algorithm based on four-module experimental reactor validation

To solve the problem of life prediction of proton exchange membrane fuel cells (PEMFCs), a novel stack with four modules was used to conduct experiments. With the consistent conditions of the experimental stack, the stack voltage data was used as a life indicator to predict the remaining life of PEMFCs and the trend of performance degradation, which was advantageous for early detection of stack operation problems and timely implementation of maintenance measures. Due to the difficulty of obtaining optimal hyperparameter combinations for traditional Long-Short Term Memory (LSTM) neural networks through limited experiments, which affects the prediction accuracy, the Grey Wolf Optimization (GWO) algorithm is introduced. This improved the accuracy of the test set for Module 1 by 10.154 %, and reduced the prediction error for remaining service life by 11.3 %. Modules 2 and 3 were validated using the optimization algorithm, the accuracy of the test set was improved by 12.289 % and 11.044 %, The prediction error for the remaining service life has been reduced by 21.17 and 28.21 h, respectively. The four-module experimental fuel cell stack can provide multiple operating conditions simultaneously to verify the accuracy and effectiveness of the hybrid prediction model proposed in this paper.