An adaptive internal state observer based on the cubature Kalman filter algorithm for a vehicular PEMFC system

Ensuring a timely and appropriate supply of reactants is paramount for optimizing fuel cell performance. However, achieving precise reactant control depends on accurately determining the system's internal state. In this paper, the challenge of directly measuring the internal state of vehicular PEMFCs is addressed by proposing an adaptive internal state observer. Firstly, an air subsystem model with five state variables is established based on experimental data. This model effectively describes the critical dynamic features of air flow in the fuel cell system. Secondly, a cubature Kalman filtering algorithm is used to estimate the internal state of the cathode side of the PEMFC. To improve the algorithm's flexibility and minimize estimation errors caused by variations of model parameters, a forgetting factor is introduced, which dynamically adjusts the algorithm's parameters based on the changing conditions of the model. Finally, the simulation comparison demonstrates that the ACKF effectively mitigates the degradation of estimation accuracy with strong robustness as the variation of key structural parameters increases. The ACKF reduces the IAE to 15.05% and 10.92% of the CKF when the selected structural parameters vary by 5%, and to 9.82% and 4.87% respectively when the variation is 10%.