PREDICTION OF MULTI-STEP ADVANCE AGING OF PEMFC BASED ON DWT-LSTM HYBIRD DRIVING METHOD

Yang Yinlong; Luo Maji

doi:10.19912/j.0254-0096.tynxb.2024-2139

PDF(2194 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 575-582. DOI: 10.19912/j.0254-0096.tynxb.2024-2139

PREDICTION OF MULTI-STEP ADVANCE AGING OF PEMFC BASED ON DWT-LSTM HYBIRD DRIVING METHOD

Yang Yinlong, Luo Maji

Author information +

History +

Abstract

Aiming at the problem of low accuracy of multi-step advance prediction of voltage of proton exchange membrane fuel cell （PEMFC） under dynamic conditions, a fuel cell multi-step advance prediction method based on DWT-LSTM hybrid drive is proposed. Firstly, in order to eliminate the influence of operating parameter fluctuations on voltage prediction under dynamic conditions, the correlation between operating parameters and voltage is studied, and the prediction models are constructed for the operating parameters with strong correlation and discrete wavelet transform （DWT） decomposition voltage respectively using long short-term memory neural network （LSTM）. The model hyperparameters and fusion weights are given by particle swarm optimization algorithm （PSO）. At the same time, in order to prevent the multi-step advance prediction error of the model from accumulating too quickly, a physical aging model is built to correct the multi-step advance prediction results. Finally, tests were carried out based on the durability data of the aged battery stack under dynamic conditions, in which the root mean square error （RMSE） and determination coefficient （R²） of the short-term prediction results reached 0.00254 and 0.9926; the multi-step advance prediction results show that this method can effectively predict the aging trend and local fluctuation of voltage. When the window value is selected as 4 hours, the root mean square error and determination coefficient reach 0.0224 and 0.8941. The program ablation experiment also shows that multi-parameter input and the introduction of physical models are conducive to improving the accuracy of multi-step advance prediction of voltage.

Key words

proton exchange membrane fuel cell / discrete wavelet transform / long short-term memory / aging prediction

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Yang Yinlong, Luo Maji. PREDICTION OF MULTI-STEP ADVANCE AGING OF PEMFC BASED ON DWT-LSTM HYBIRD DRIVING METHOD[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 575-582 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2139

References

[1] HUA Z G, ZHENG Z X, PAHON E, et al.A review on lifetime prediction of proton exchange membrane fuel cells system[J]. Journal of power sources, 2022, 529: 231256.
[2] TIAN Q C, CHEN H T, DING S, et al.Remaining useful life prediction method of PEM fuel cells based on a hybrid model[J]. Electronics, 2023, 12(18): 3883.
[3] WANG Y P, WU K C, ZHAO H H, et al.Degradation prediction of proton exchange membrane fuel cell stack using semi-empirical and data-driven methods[J]. Energy and AI, 2023, 11: 100205.
[4] OU M Y, ZHANG R F, SHAO Z F, et al.A novel approach based on semi-empirical model for degradation prediction of fuel cells[J]. Journal of power sources, 2021, 488: 229435.
[5] CHU T K, ZHANG R F, WANG Y B, et al.Performance degradation and process engineering of the 10 kW proton exchange membrane fuel cell stack[J]. Energy, 2021, 219: 119623.
[6] AO Y J, LAGHROUCHE S, DEPERNET D, et al.Proton exchange membrane fuel cell prognosis based on frequency-domain Kalman filter[J]. IEEE transactions on transportation electrification, 2021, 7(4): 2332-2343.
[7] CHEN H, ZHAN Z G, JIANG P X, et al.Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA[J]. Applied energy, 2022, 310: 118556.
[8] ZHANG R F, CHEN T, XIAO F, et al.Bi-directional gated recurrent unit recurrent neural networks for failure prognosis of proton exchange membrane fuel cells[J]. International journal of hydrogen energy, 2022, 47(77): 33027-33038.
[9] LI H L, CHEN Q H, ZHANG L Y, et al.Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs bi-directional long short-term memory[J]. Applied energy, 2023, 344: 121294.
[10] ZHANG F, WANG B W, GONG Z C, et al.Short-term performance degradation prediction of proton exchange membrane fuel cell based on discrete wavelet transform and Gaussian process regression[J]. Next energy, 2023, 1(3): 100052.
[11] 陈景文, 杨淇, 兰天一, 等. 基于集成经验模态分解和极限学习机的质子交换膜燃料电池寿命预测[J]. 太阳能学报, 2025, 46(2): 135-141.
CHEN J W, YANG Q, LAN T Y, et al.Lifetime prediction of PEMFC based on ensemble empirical mode decomposition and extreme learning machine[J]. Acta energiae solaris sinica, 2025, 46(2): 135-141.
[12] HUA Z G, ZHENG Z X, PAHON E, et al.Multi-timescale lifespan prediction for PEMFC systems under dynamic operating conditions[J]. IEEE transactions on transportation electrification, 2022, 8(1): 345-355.
[13] 王艳琴, 谢卓峰, 韩国鹏, 等. 基于极端梯度提升的PEMFC长短期老化趋势预测[J]. 太阳能学报, 2024, 45(7): 232-239.
WANG Y Q, XIE Z F, HAN G P, et al.Short-and long-term degradation prediction for proton exchange membrane fuel cell based on extreme gradient boosting[J]. Acta energiae solaris sinica, 2024, 45(7): 232-239.
[14] MA R, XIE R Y, XU L C, et al.A hybrid prognostic method for PEMFC with aging parameter prediction[J]. IEEE transactions on transportation electrification, 2021, 7(4): 2318-2331.
[15] HONG J C, YANG H X, LIANG F W, et al.State of health prediction for proton exchange membrane fuel cells combining semi-empirical model and machine learning[J]. Energy, 2024, 291: 130364.
[16] YANG X T, ZOU A K, CAO J D, et al.Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks[J]. Electronic research archive, 2023, 31(5): 2667-2688.
[17] ZHAO Y, LUO M J, YANG J W, et al.Numerical analysis of PEMFC stack performance degradation using an empirical approach[J]. International journal of hydrogen energy, 2024, 56: 147-163.
[18] FCLAB Research Federation.2014 IEEE PHM data challenge[EB/OL].[2018-01-02]. http://eng.fclab.fr/ieee-phm-2014-%20data-challenge/.
[19] ZHANG Z D, WANG Y X, HE H W, et al.A short-and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell[J]. Applied energy, 2021, 304: 117841.
[20] BENAGGOUNE K, YUE M L, JEMEI S, et al.A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell[J]. Applied energy, 2022, 313: 118835.