Abstract
The major challenges for the commercialization of proton exchange membrane fuel cells (PEMFCs) are durability and cost. Prognostics and health management technology enable appropriate decisions and maintenance measures by estimating the current state of health and predicting the degradation trend, which can help extend the life and reduce the maintenance costs of PEMFCs. This paper proposes an online model-based prognostics method to estimate the degradation trend and the remaining useful life of PEMFCs. A non-linear empirical degradation model is proposed based on an aging test, then three degradation state variables, including degradation degree, degradation speed and degradation acceleration, can be estimated online by the particle filter algorithm to predict the degradation trend and remaining useful life. Moreover, a new health indicator is proposed to replace the actual variable loading conditions with the simulated constant loading conditions. Test results using actual aging data show that the proposed method is suitable for online remaining useful life estimation under variable loading conditions. In addition, the proposed prognostics method, which considers the activation loss and the ohmic loss to be the main factors leading to the voltage degradation of PEMFCs, can predict the degradation trend and remaining useful life at variable degradation accelerations.
Highlights
As fossil energy consumption continues to increase and the environment continues to deteriorate, there is an urgent need to find clean renewable energy and conversion devices.Proton exchange membrane fuel cells (PEMFCs) can directly convert chemical energy into electrical energy, the unique characteristics, such as high efficiency, high power density, no pollution, and low operating temperature, make proton exchange membrane fuel cells (PEMFCs) be one of the most promising candidates for power generation
600 h, which leads to a faster summary, the proposed prognostics method can predict the degradation trend at variable growth of α, and which means the degradation of PEMFCs is accelerated
When the current time is 450 h, the degradation degree α450h, the degradation speed v450h and the degradation acceleration a450h can be estimated by particle filter online, the future degradation trend can be predicted based on this information
Summary
As fossil energy consumption continues to increase and the environment continues to deteriorate, there is an urgent need to find clean renewable energy and conversion devices. PHM consists of seven layers [7]: data acquisition, data processing, condition assessment, diagnostics, prognostics, decision support and human–machine interface It aims at utilizing the real-time monitoring data of the target system to diagnose and predict its health status. Precise degradation model hard to build, so the data-based method is more algorithms: wavelet-based approach [6], echo state network [17,18], adaptivelearning neuro-fuzzy and more popular It predicts the degradation trend by kinds of machine algoinference systems [19], relevance vector machine [20,21] [17,18], and so adaptive on.
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