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 sub-waveforms 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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2022-03-01


    Size :

    4337136 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English