Degradation prediction model of PEMFC based on multi-reservoir echo state network with mini reservoir

The durability of the proton exchange membrane fuel cell (PEMFC) has always been a major obstacle in its commercialization process and effective degradation prediction can improve this problem to a certain extent. Data-driven degradation prediction model is one of the most effective prediction methods available, which is able to ignore the structure of the PEMFC itself and rely solely on the data to make predictions, greatly simplifying the pre-diction process. Echo state network (ESN), as one of the data-driven methods, has received much attention for its low computational complexity and fast convergence in the degra-dation prediction of PEMFC. In this paper, the multi-reservoir echo state network with mini reservoir (MRM) degradation prediction model of PEMFC is proposed. The structure of MRM is that the main reservoirs are stacked in a layer and the mini reservoir is in the next level to collect and organize the main reservoir states. In addition, in order to improve the prediction accuracy, this paper firstly uses Savitzky-Golay (SG) filter to process the original data, and then investigates the influence of two important parameters, the number of main reservoirs and the number of main reservoir neurons, on the prediction accuracy and finds the optimal number of main reservoirs and main reservoir neurons for this model using particle swarm optimization (PSO) algorithm. Finally, the effectiveness of the model is verified on different lengths of training sets under both static and dynamic conditions. The results show that the model has higher accuracy and better robustness in the PEMFC degradation prediction compared with other models. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a degradation prediction model for proton exchange membrane fuel cells (PEMFC) based on a multi-reservoir echo state network with a mini reservoir. The model achieves high accuracy and robustness in the degradation prediction of PEMFC by optimizing the model parameters using the particle swarm optimization algorithm.