Remaining useful life prediction of PEMFC systems under dynamic operating conditions

Abstract

The Prognostic and Health Management (PHM) has been developed for more than two decades. It is capable to anticipate the impending failures and make decisions in advance to extend the lifespan of the target systems, such as Proton Exchange Membrane Fuel Cell (PEMFC) systems. Prognostic is a critical stage of PHM. Among various prognostic methods, the data-driven ones could predict the system lifespan based on the device’s knowledge and historical data. In the Remaining Useful Life (RUL) prediction, the Health Indicators (HIs) should be able to reflect the health states of the PEMFC stack. Moreover, an effective HI could help to define an explicit degradation state and improve the prediction accuracy. The HIs of voltage and power are usually used under static conditions due to their monotonic decreasing characteristics. Besides, the measurements of voltage and current are implemented easily in practice. Nevertheless, the static HIs are unable to be directly used under the dynamic operating conditions because they are sensitive to the mission profiles. To overcome the weakness of static HIs, a convenient and practical HI named Relative Power-loss Rate (RPLR) is proposed herein. According to the polarization curve at the beginning of life, the initial power under different mission profiles can be identified. Then the actual power is obtained by monitoring the current and voltage continuously. Finally, the RPLR is calculated based on the initial power and actual power. Afterward, the RUL of PEMFC is predicted by some Artificial Intelligence (AI) prognostic algorithms. Among the various data-driven prognostic approaches, Echo State Network (ESN) has provided an efficient and promising solution for the RUL prediction of PEMFC systems. Compared with classical Recurrent Neural Network (RNN), it could accelerate the convergence rate and reduce the computational complexity. Nevertheless, the traditionally used single-input ESN structure is feeble to handle the varying mission profiles. As a scheduling variable, the current is an interesting parameter since it represents the working properties to some extent. Considering the system’s dynamic characteristics, the stack current is regarded as another input of ESN, and the output matrix’s dimension is increased at the same time. Therefore, a double-input ESN structure is proposed to enhance the prediction performance. Based on the dynamic HI of RPLR, three dynamic micro-cogeneration (μ-CHP) durability tests of PEMFC systems are used to verify the improved ESN prediction structure.