Version 1
: Received: 20 June 2023 / Approved: 21 June 2023 / Online: 21 June 2023 (08:55:25 CEST)
How to cite:
Tian, Q.; Chen, H.; Ding, S.; Shu, L.; Wang, L.; Huang, J. Remaining Useful Life Prediction Method of PEM Fuel Cell Based on Hybrid Model. Preprints2023, 2023061480. https://doi.org/10.20944/preprints202306.1480.v1
Tian, Q.; Chen, H.; Ding, S.; Shu, L.; Wang, L.; Huang, J. Remaining Useful Life Prediction Method of PEM Fuel Cell Based on Hybrid Model. Preprints 2023, 2023061480. https://doi.org/10.20944/preprints202306.1480.v1
Tian, Q.; Chen, H.; Ding, S.; Shu, L.; Wang, L.; Huang, J. Remaining Useful Life Prediction Method of PEM Fuel Cell Based on Hybrid Model. Preprints2023, 2023061480. https://doi.org/10.20944/preprints202306.1480.v1
APA Style
Tian, Q., Chen, H., Ding, S., Shu, L., Wang, L., & Huang, J. (2023). Remaining Useful Life Prediction Method of PEM Fuel Cell Based on Hybrid Model. Preprints. https://doi.org/10.20944/preprints202306.1480.v1
Chicago/Turabian Style
Tian, Q., Lei Wang and Jun Huang. 2023 "Remaining Useful Life Prediction Method of PEM Fuel Cell Based on Hybrid Model" Preprints. https://doi.org/10.20944/preprints202306.1480.v1
Abstract
To predict the remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then the MK-RVM was used to train the model. Then, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by FCLAB, and the prediction accuracy of the RUL of PEMFC was obtained by 95.35%, which showed that the method had good gen-eralization ability and verified the accuracy of the prediction for the RUL of PEMFC.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
