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Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space

Abstract : Prognostic plays an important role in improving the reliability and durability performance of fuel cells (FCs); although it is hard to realize an adaptive prognostic because of complex degradation mechanisms and the influence of operating conditions. In this paper, an adaptive data-driven prognostic strategy is proposed for FCs operated in different conditions. To extract a feasible health indicator (HI), a series of linear parameter-varying models are identified in sliding data segments. Then, virtual steady-state stack voltage is formulated in the identified model space and considered as the HI. To enhance the adaptability of prognostic, an ensemble echo state network is then implemented , given the extracted HI data. Long-term tests on a type of low-powerscale proton-exchange membrane FC stack in different operating modes are carried out. The performance of the proposed strategy is evaluated using the experimental data. Index Terms-Adaptability, data-driven prognostic, echo state network (ESN) ensemble, health indicator (HI), model space, PEMFC.
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Submitted on : Friday, February 14, 2020 - 8:50:39 AM
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Zhongliang Li, Zhixue Zheng, Rachid Outbib. Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space. IEEE Transactions on Industrial Electronics, Institute of Electrical and Electronics Engineers, 2019, 67, ⟨10.1109/TIE.2019.2893827⟩. ⟨hal-02475763⟩

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