Effectiveness of PEMFC historical state and operating mode in PEMFC prognosis

As a high efficiency and environmental friendly energy conversion technique, proton exchange membrane fuel cell (PEMFC) system faces challenges of limited durability and performance decay during long-term operation. Prognosis estimates the remaining useful life (RUL) of the system, from which maintenance policy can be scheduled to extend its useful life. However, parameters related to either PEMFC historical state or operating mode are used in most existing PEMFC prognostic studies, while their effects on PEMFC predictions are not clarified, this brings great challenge in selecting appropriate parameters for reliable PEMFC prognosis in practical applications subjected to complex operating conditions. In this paper, the effectiveness of PEMFC historical behavior and operating mode on PEMFC future performance at both static and non-static conditions are investigated, using back propagation neural network (BPNN) and adapted neural fuzzy inference system (ANFIS), respectively. From the findings, PEMFC historical state and operating mode make varying contributions to PEMFC prognostic results at different operating scenarios. At static operating condition, PEMFC predictions are dominated by its historical state, since constant operating mode is applied in this scenario, thus reliable prediction can be made by using only parameters representing PEMFC historical state. However, at non-static operating condition, the varying operating mode makes more contribution to the PEMFC predictions, and accurate prognosis should be provided by including variables representing varying operating mode in the prognostic analysis. The results can be beneficial in selecting appropriate parameters in prognostic analysis at practical PEMFC applications, where complex operating conditions may be experienced.