A novel approach for predicting PEMFC in varying ambient conditions by using a transient search optimization algorithm based on a semi-empirical model

Proton exchange membrane fuel cell (PEMFC) is an alternate energy source that produces electricity without any adverse effects on the environment. The drawbacks of PEMFC are its short life and its non-linear voltage with loading current. Also, PEMFC is prone to ambient conditions, and its performance varies with different ambient conditions. In this work, the semi-empirical modeling approach has been used to predict the PEMFC voltage accurately. However, when the ambient condition varies, the voltage of PEMFC varies accordingly and consequently the previous parameters of the EMI-empirical model don't produce good results. Previously the voltage variation due to changes in ambient has been predicted with the help of ambient conditions and load resistance, but this model isn't sui for all PEMFCs. In this work, a new method has been proposed where fast and accurate optimization technique such as Transient search optimization (TSO) has been used to optimize parameters when ambient condition varies and predicts the PEMFC voltage accurately and doesn't consume a lot of time. The proposed method will be very helpful in future research for predicting the PEMFC voltage for various PEMFC systems at different ambient conditions. The proposed method has been validated experimentally by performing experiments on n single-cell PEMFC system at normal and high ambient temperature.