Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm

This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.