Reduced complexity models for water management and anode purge scheduling in DEA operation of PEMFCs

In this work, the dynamic behavior of Fuel Cell operation under Dead-Ended Anode conditions is shown. A DEA can be fed with dry hydrogen, since water crossing through the membrane is sufficient to humidify the fuel. The reduced requirements for inlet humidification yield a system with lower cost and weight compared to FCs with flow-through or recirculated anodes. The accumulation of water and nitrogen in the anode channel is first observed near the outlet. A stratified pattern develops in the channel where a hydrogen-rich area sits above a depleted region and is stabilized by the effect of gravity. A model is presented which describes the dynamic evolution of a blanketing N2 front in the anode channel and a hydrogen starved region. Understanding, modeling, and predicting the front evolution can reduce the H2 wasted during purges, avoid over drying the membrane, and mitigate degradation associated with hydrogen starved areas

Experimental validation of equilibria in fuel cells with dead-ended anodes

This paper investigates the nitrogen blanketing front during the dead-ended anode (DEA) operation of a PEM fuel cell. Surprisingly the dynamic evolution of nitrogen and water accumulation in the dead-ended anode (DEA) of a PEM fuel cell arrives to a steady-state suggesting the existence of equilibrium behavior. We use a multi-component model of the two-phase one-dimensional (along-the-channel) system behavior to analyze and exploit this phenomenon. Specifically, the model is first verified with experimental observations, and then utilized for showing the evolution towards equilibrium. The full order model is reduced to a second-order ordinary differential equation (ODE) with one state, which can be used to predict and amalyse the surprising but experimentally observed steady state DEA behavior

Nitrogen front evolution in purged polymer electrolyte membrane fuel cell with dead-ended anode

In this paper, we model and experimentally verify the evolution of liquid water and nitrogen fronts along the length of the anode channel in a proton exchange membrane fuel cell operating with a dead-ended anode that is fed by dry hydrogen. The accumulation of inert nitrogen and liquid water in the anode causes a voltage drop, which is recoverable by purging the anode. Experiments were designed to clarify the effect of N-2 blanketing, water plugging of the channels, and flooding of the gas diffusion layer. The observation of each phenomenon is facilitated by simultaneous gas chromatography measurements on samples extracted from the anode channel to measure the nitrogen content and neutron imaging to measure the liquid water distribution. A model of the accumulation is presented, which describes the dynamic evolution of a N-2 blanketing front in the anode channel leading to the development of a hydrogen starved region. The prediction of the voltage drop between purge cycles during nonwater plugging channel conditions is shown. The model is capable of describing both the two-sloped behavior of the voltage decay and the time at which the steeper slope begins by capturing the effect of H-2 concentration loss and the area of the H-2 starved region along the anode channel

Experiments and Modeling of PEM Fuel Cells for Dead-Ended Anode Operation.

This thesis develops models for the design and control of Dead-Ended Anode (DEA) fuel cell systems. Fuel cell operation with a dead-ended systems anode reduces fuel cell system cost, weight, and volume because the anode external humidification and recirculation hardware can be eliminated. However, DEA operation presents several challenges for water management and anode purge scheduling. Feeding dry hydrogen reduces the membrane water content near the anode inlet. Large spatial distributions of hydrogen, nitrogen, and water develop in the anode, affecting fuel cell durability. The water and nitrogen which cross through the membrane accumulate in the anode during dead-ended operation. Anode channel liquid water plugging and nitrogen blanketing can induce hydrogen starvation and, given the right conditions, trigger cathode carbon oxidation leading to permanent loss of active catalyst area. Additionally, the accumulation of inert gases in the anode leads to a decrease in cell efficiency by blocking the catalyst and reducing the area available to support the reaction. Purging the anode uncovers the catalyst and recovers the available area, but at the expense of wasting hydrogen fuel. To understand, design, and control DEA fuel cells, various models are developed and experimentally verified with plate-to-plate experiments using neutron radiography and gas chromatography. The measurements are used to parameterize dynamic models of the governing two-phase (water liquid and vapor) spatially distributed transport phenomena. A reduced order model is developed that captures the water front evolution inside the gas diffusion layer and channels. A second model captures the nitrogen blanketing front location along the anode channel. The reduced order models are combined to form a complete description of the system. They require less computational effort, allow efficient parameterization, and provide insight for developing control laws or designing and operating DEA fuel cells.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78800/1/siegeljb_1.pd

Measurement of Liquid Water Accumulation in a Proton Exchange Membrane Fuel Cell with Dead-Ended Anode

The operation and accumulation of liquid water within the cell structure of a polymer electrolyte membrane fuel cell (PEMFC) with a dead-ended anode is observed using neutron imaging. The measurements are performed on a single cell with 53 square centimeter active area, Nafion 111-IP membrane and carbon cloth Gas Diffusion Layer (GDL). Even though dry hydrogen is supplied to the anode via pressure regulation, accumulation of liquid water in the anode gas distribution channels was observed for all current densities up to 566 mA cm-2 and 100% cathode humidification. The accumulation of liquid water in the anode channels is followed by a significant voltage drop even if there is no buildup of water in the cathode channels. Anode purges and cathode surges are also used as a diagnostic tool for differentiating between anode and cathode water flooding. The rate of accumulation of anode liquid water, and its impact on the rate of cell voltage drop is shown for a range of temperature, current density, cathode relative humidity and air stoichiometric conditions. Neutron imaging of the water while operating the fuel cell under dead-ended anode conditions offers the opportunity to observe water dynamics and measured cell voltage during large and repeatable transients

A Non-intrusive Approach for Physics-constrained Learning with Application to Fuel Cell Modeling

A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physical models. In contrast to parameter calibration, this work seeks corrections to the structure of the model by a) inferring augmentation fields that are consistent with the underlying model, and b) transforming these fields into corrective model forms. The proposed approach couples the inference and learning steps in a weak sense via an alternating optimization approach. This coupling ensures that the augmentation fields remain learnable and maintain consistent functional relationships with local modeled quantities across the training dataset. An iterative solution procedure is presented in this paper, removing the need to embed the augmentation function during the inference process. This framework is used to infer an augmentation introduced within a Polymer electrolyte membrane fuel cell (PEMFC) model using a small amount of training data (from only 14 training cases.) These training cases belong to a dataset consisting of high-fidelity simulation data obtained from a high-fidelity model of a first generation Toyota Mirai. All cases in this dataset are characterized by different inflow and outflow conditions on the same geometry. When tested on 1224 different configurations, the inferred augmentation significantly improves the predictive accuracy for a wide range of physical conditions. Predictions and available data for the current density distribution are also compared to demonstrate the predictive capability of the model for quantities of interest which were not involved in the inference process. The results demonstrate that the weakly-coupled IIML framework offers sophisticated and robust model augmentation capabilities without requiring extensive changes to the numerical solver

Phenomenological model of lithium-ion battery formation cycling and aging

This work proposes a semi-empirical model for the SEI growth process during the early stages of lithium-ion battery formation cycling and aging. By combining a full-cell model which tracks half-cell equilibrium potentials, a zero-dimensional model of SEI growth kinetics, and a semi-empirical description of macroscopic cell expansion, the resulting model replicated experimental trends measured on a 2.5 Ah pouch cell, including the first-cycle efficiency, cell thickness changes, and electrolyte reduction peaks during the first charge dQ/dV signal. This work also introduces an SEI growth boosting formalism which enables a unified description of SEI growth during both formation cycling and aging. The model further provides a homogenized representation of multi-component SEI reactions which enables the study of both solvent and additive consumption during formation. This work bridges the gap between electrochemical descriptions of SEI growth and applications towards industrial battery manufacturing technology where battery formation is an essential but time-consuming final step. We envision that the formation model can further be used to predict the impact of formation protocols and electrolyte systems on SEI passivation and resulting battery longevity.Comment: Submitted to the Journal of the Electrochemical Society on May 24, 202

Hybrid nonlinear observer for battery state- of- charge estimation using nonmonotonic force measurements

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/4/adc238.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/3/adc238-sup-0001-supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/2/adc238_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/1/adc238-sup-0002-supinfo.pd