Sensitivity-Based State and Parameter Estimation for Fuel Cell Systems

The thermal behavior of high-temperature fuel cell systems is characterized by a large variety of parameters which are not directly accessible for measurements. To derive mathematical models for the dynamics of such systems, it is essential to identify the parameter values on the basis of knowledge about the current operating conditions. After an offline parameter identification with suitable experimental data, the values are further adjusted by online state and parameter estimation. In previous work, various approaches have been employed for the offline parameter identification of fuel cell systems. These approaches include the application of commercial local optimization procedures and novel interval arithmetic routines which aim at a global optimization within a bounded parameter range. Since it could be shown that interval procedures provide system parameterizations with an improved approximation quality, further alternative algorithms are investigated in this paper for the parameter identification of fuel cell systems. These algorithms make use of a sensitivity analysis of suitable performance criteria. These performance criteria can be employed both offline for parameter identification and online for state and disturbance estimation. Numerical results show the advantages of the sensitivity-based procedure in comparison with the above-mentioned estimation approaches.