A novel generalized prognostic method of proton exchange membrane fuel cell using multi-point estimation under various operating conditions

The proton exchange membrane fuel cell has been introduced into the fields of transportation, power production, and mobile devices. Especially, priority is given to promoting the application in commercial vehicles. However, more severe fluctuations in power demands give new challenges to the lifespan of fuel cells. The accurate state estimation and reasonable degradation prediction can assist in improving the lifetime of fuel cell devices. To realize the on-road prediction, herein, a novel generalized prognostic method called the Multi-point Square-root central difference Kalman filter (MP-SRCDKF) is proposed. First of all, an extended mathematical model is introduced. Later, the sensitivity analysis and parameter recognition are conducted based on the Sobol’ method and the jellyfish searching algorithm. The performance of the SRCDKF method is compared under static and quasi-dynamic conditions, which is further extended into the road condition and verified under the dynamic cycle condition temporarily. In the end, the remaining useful life prediction under various operating conditions is discussed. Furthermore, the generalized method can provide an approach for prognostic decision-making and the updating of the dynamic polarization curve to expand the lifetime and optimize the control system.