Multi-Timescale Lifespan Prediction for PEMFC Systems Under Dynamic Operating Conditions

Data-driven prognostic methods aim at estimating the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) system and contribute to its lifespan extension and reliability improvement. Lack of dynamic health indicator (HI) and low prediction accuracy is the weaknesses for current RUL prediction methods, especially when the PEMFC system works at the variable load profiles. To overcome the drawbacks of static His (e.g., voltage and power), a novel dynamic HI of relative power-loss rate (RPLR) is put forward to represent the degradation state under the variable load profiles. Besides, the discrete wavelet transform and ensemble echo state network (DWT-EESN) approach is proposed to deal with the multi-timescale features of RPLR and enhance the prediction performance. First, the decomposition method of discrete wavelet transform (DWT) is applied to decompose the RPLR into several sub-waveforms. Second, several independent echo state networks (ESNs) are utilized to predict the suhwaveforms separately. Finally, the prediction results in different timescales are integrated to achieve the overall RUL prediction. Three micro-combined heat and power (mu-CHP) experiments with different dynamic profiles are carried out, and results show that the DWT-EESN has a higher long-term prediction accuracy than the traditional ESN structure.

Automated summary

Data-driven prognostic methods are important for estimating the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) system. Current RUL prediction methods have weaknesses in terms of lack of dynamic health indicator (HI) and low prediction accuracy, especially under variable load profiles. To overcome these weaknesses, a novel dynamic HI of relative power-loss rate (RPLR) and the DWT-EESN approach are proposed to enhance the prediction performance.