Modeling Polarization of a DMFC System via Neural Network with Immune-Based Particle Swarm Optimization

Abstract

Multitudinous parameters involved have made the direct methanol fuel cell (DMFC) acomplex "black-box," posing challenges and difficulties in its modeling. This paper presents a neural network (NN) model with immune-based particle swarm optimization (IPSO) approach of the DMFC system, which is different from the conventional complex mathematical models. With the actual operation of DMFC taken into consideration, the polarization curves are run under a stable condition as the reference data for training the model. To reduce time cost for the training procedure and maintaining minimum modeling error, the IPSO algorithm is applied to the learning procedure of NN model. By combining the NN and the IPSO, the weight of the transfer function on the node in the hidden layer can be adjusted to minimize modeling error. The simulation results were in agreement with the experimental results, showing that the hybridization of NN model with IPSO approach can effectively demonstrate the polarization behaviors on a DMFC system. Therefore, this hybrid NN model with IPSO approach can be used as a simulation tool, which can save much money and time for reforming the conventional mathematical models with expensive experiment. Furthermore, the proposed method reveals an adaptive ability to improve the model even if the DMFC system structure is different.