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Performance and degradation characterization of electrochemical power sources using thermodynamics
This study demonstrates a thermodynamics-based instantaneous characterization of electrochemical power sources (EPSs) of various chemistries, sizes, scales and configurations, cycled at different rates. Recently proposed Degradation-Entropy Generation (DEG) methodology is reviewed and characteristic...
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Published in: | Electrochimica acta 2021-01, Vol.365, p.137337, Article 137337 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This study demonstrates a thermodynamics-based instantaneous characterization of electrochemical power sources (EPSs) of various chemistries, sizes, scales and configurations, cycled at different rates. Recently proposed Degradation-Entropy Generation (DEG) methodology is reviewed and characteristic DEG elements are generalized to all electrochemical power sources, experimentally verified via uncontrolled cycling of nickel-metal hydride batteries, lead-acid batteries, lithium-ion batteries, supercapacitors and fuel cells. Dissipation factor and entropic efficiency are introduced as new performance/degradation characterization factors in addition to existing DEG geometric and parametric elements. While data arise from several different power source types and process rates, results are similar and characteristic of system type and performance, consistently verifying and further elucidating anticipated system behaviors. Previously observed near-100% fit of experimental data to theoretical formulations persists in this study. A normalized DEG domain is introduced and compared to Voltage–Charge curves and Ragone plots in simultaneous comparative analysis of the various systems under different operational conditions. DEG methods can be used to adequately quantify the dissipative and degradation tendencies of electrochemical power sources for performance analysis and optimization. |
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ISSN: | 0013-4686 1873-3859 |
DOI: | 10.1016/j.electacta.2020.137337 |