Pant, Lalit M. (ORCID:0000000204323902)

DOE OSTI2021

Method—Practices and Pitfalls in Voltage Breakdown Analysis of Electrochemical Energy-Conversion Systems

Many electrochemical energy-conversion systems are evaluated by polarization curves, which report the cell voltage across a range of current densities and are a global measure of operation and state of health. Mathematical models can be used to deconstruct the measured overall voltage and identify and quantify the voltage-loss sources, such as kinetic, ohmic, and mass-transport effects. These results elucidate the best pathways for improved performance. In this work, we discuss several voltage-breakdown methods and provide examples across different low-temperature, membrane-based electrochemical systems including electrolyzers, fuel cells, and related electrochemical energy-conversion devices. We present best practices to guide experimentalists and theorists in polarization-curve breakdown analysis.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Critical Parameter Identification of Fuel-Cell Models Using Sensitivity Analysis

Numerical modeling has been a vital tool in proton-exchange-membrane fuel-cell (PEMFC) analysis; however, the predictive capabilities of these models depend on the input physical parameters, several of which are either not experimentally measured or have large scatter in measured values. This article presents an uncertainty propagation-based sensitivity analysis to identify the model parameters that impact the model predictions most. A comprehensive 2-D membrane electrode assembly (MEA) model is used to perform local sensitivity analysis at multiple operating conditions, which encompass the range of environments and operating conditions a cell can encounter. While at lower humidities, cathode kinetics and membrane-ohmic-loss related parameters are crucial, gas transport and porous-media saturation behavior are more important at humidified conditions. Several of these findings are different from previous studies presented in literature. Identifying the crucial parameters helps focus future material and cell optimization studies as well as experimental studies to quantify these parameters with higher accuracy.

25 ENERGY STORAGE↗