Projection algorithm-based nonlinear observation of internal states in fuel delivery systems with gas diffusion

In this paper, a nonlinear observer design to estimate different gas species profiles and partial pressures in a fuel delivery system (FDS) with gas permeation is presented. Precise knowledge of critical internal states is of great importance to ensure reliable and efficient operation for FDS. First, a nonlinear isothermal dynamic model including fuel supply, hydrogen consumption, mixture recirculation and exhaust is formulated. Then a nonlinear observer based on projection algorithm is proposed, giving detailed information on internal conditions of FDS online. Without calculation of inverse matrix, the stack output voltage is treated as the only measurable observer input. The voltage estimation error is obtained from the difference between the prediction and the actual measurement, and nonlinear error injection matrix and projection term are employed as correction inputs. Finally, the proposed dynamic state observer is validated and compared with second order sliding mode (SOSM) observer on RT-LAB platform, the results indicate that the observer shows better real-time estimation performance and robustness. Furthermore, the observer demonstrates good robustness in terms of hydrogen partial pressures observation, but less robustness in nitrogen partial pressures estimation. Moreover, the sequence of key system parameters effecting the estimations are analyzed deeply and some conclusions are extracted.

Automated summary

A nonlinear observer design for estimating gas species profiles and partial pressures in a fuel delivery system is proposed in this paper. The observer, based on a projection algorithm, shows better real-time estimation performance and robustness compared to a second order sliding mode observer. The study also reveals the observer's good robustness in hydrogen partial pressures observation and less robustness in nitrogen partial pressures estimation, with an analysis of key system parameters affecting the estimations.