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.
Optimal Rating and Position of Distributed Generators under Variable Loading Conditions using Bacterial Foraging Algorithm
