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Physically-based Li-ion electrochemical cell models have been shown capable of predicting cell performance and degradation, but are computationally expensive for optimization-oriented design applications. Faster empirical models have been developed from experimental data, but are not generalizable to operating conditions outside of the range established by the calibration data. In this paper, a reduced-order capacity-loss model for graphite anodes is derived based upon the salient physical loss mechanisms to improve computational efficiency without sacrificing model fidelity. This model captures the two primary degradation mechanisms that occur in the graphite anode of a typical lithium ion cell: a) capacity loss due to Solid Electrolyte Interface (SEI) layer growth, and b) capacity loss due to isolation of active material. The model is calibrated and validated for a commercial 2.3-Ah cell with a Lithium Iron Phosphate (LFP) cathode and graphite anode. One data set is used for calibration, another four data sets are used for validation. The model matches experimental capacity degradation results within a 10% error. Moreover, the reported model is 2400×faster than currently existing more complex physically-based electrochemical models that are only slightly more accurate (less than 9% error).

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A control-oriented dynamic model of a catalytic partial oxidation-based fuel processor is developed using physicsbased principles. The Fuel Processor System (FPS) converts a hydrocarbon fuel to a hydrogen (H 2 ) rich mixture that is directly feed to the Proton Exchange Membrane Fuel Cell Stack (PEM-FCS). Cost and performance requirements of the total powerplant typically lead to highly integrated designs and stringent control objectives. Physics based component models are extremely useful in understanding the system level interactions, implications on system performance and in model-based controller design. The model can be used in a multivariable analysis to determine characteristics of the system that might limit performance of a controller or a control design.In this paper, control theoretic tools such as the relative gain array (RGA) and the observability gramian are employed to guide the control design for a FPS combined with a PEM-FC. For example this simple multivariable analysis suggests that a decrease in HDS volume is critical for the hydrogen starvation control. Moreover, RGA analysis shows different level of coupling between the system dynamics at different power levels. Finally, the observability analysis can help in assessing the relative cost-benefit ratio in adding extra sensors in the system.

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