Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration. Due to the functional significance and high manufacturing costs of the catalyst layer, monitoring internal fuel cell states is crucial. For this purpose, a diagnostic-oriented multi-scale PEMFC catalytic degradation model is developed which incorporates the failure effects of catalytic degradation on cell dynamics and global stack performance. Embedded to the multi-scale model is a square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme. In this approach, multiple models are used to estimate specific internal PEMFC system parameters, such as the mass transfer coefficient of the gas diffusion layer or the exchange current density, which are treated as additional system states. Online state estimates are provided by the SRUKF, which additionally propagates model-conditioned statistical information to update a Bayesian framework for model selection. The Bayesian model selection method carries fault indication signals that are interpreted by a derived decision logic to obtain reliable information on the current-operating system regime. The proposed diagnosis scheme is evaluated through simulations using the LA 92 and NEDC driving cycles.

Continuous preferential crystallization is an innovative approach to the separation of chiral substances. The process considered in this work takes place in a gently agitated fluidized bed located in a tubular crystallizer. The feasibility of the process has been shown in previous work, but it also turned out that choosing suitable operation conditions is quite delicate. Hence, a model based process design is desirable. Existing models of the process are rather complicated and require long computational times. In this work, a simple linear dynamic model is suggested, which captures the main properties of the process. The model is distributed in space and in a property coordinate. Using the method of characteristics, a semi-analytical solution of the linear model is derived. As a challenge to the solution, there is a recycle loop in the process that causes a feedback and couples the boundary conditions at different boundaries of the computational domain. In order to deal with this, a numerical scheme is suggested. The semi-analytical solution provides a deeper insight into the process dynamics. A comparison with a more detailed mathematical model of the process and with experiments shows strengths and limitations of the linear model.