Marginalized particle filtering for online parameter estimation of PEMFC applied to hydrogen UAVs

Proton exchange membrane fuel cells (PEMFCs) are an emerging candidate for clean power generation, with applications in smart grids, vehicles, and unmanned aerial vehicles. Accurate modeling of the power characteristics of PEMFC is required for system management purposes, where an electro-chemical model with unknown parameters is often adopted. Estimation of the unknown parameters is challenging as operating condition shifts and system degradation will lead to variation in PEMFC characteristics, particularly in the presence of system nonlinearities and noise. In this paper, a novel online parameter estimation approach for PEMFC is proposed based on the marginalized particle filtering approach. By introducing a filter derivation based on Bayesian inference and estimating the linear and nonlinear parameters separately, the marginalized approach has reduced computation cost compared with conventional particle filters. Estimation accuracy of the proposed approach is validated by experimental and simulation results, where superior accuracy compared with extended Kalman filter is obtained. Furthermore, a self-designed hydrogen quadrotor was flight-tested and energy management studies were conducted to assess the performance of the proposed estimator in hydrogen unmanned aerial vehicle applications using real flight data.