{"title":"Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application","authors":"Helge Sören Stein*, ","doi":"10.1021/acsengineeringau.3c00047","DOIUrl":null,"url":null,"abstract":"<p >Most secondary batteries in academia are (dis)charged by applying a constant current (CC) followed by a constant voltage (CV), i.e., a CCCV procedure. The usual concept is then to condense data for interpretation into representations such as differential capacity, or d<i>Q</i>/d<i>V</i>, graphs. This is done to extract information related to phenomena such as the growth of the solid electrolyte interphase or, more broadly, degradation. Typically, these measurements take several months because measurements for differential capacity analysis need to be performed at relatively low C-rates. An alternate charging schedule to CCCV is pulsed charging, where CC sections are interrupted by an open-circuit measurement on a second time scale. These and similar partially constant current strategies primarily target diffusive effects during charging and broadly fall into a linear charging category, where the time derivative for the actuated property is mostly zero. Herein, the author explores nonlinear charging, i.e., the process of actively applying a potential with a nontrivial time derivate and a resulting nontrivial current time derivative, to engineer (dis)charge cycles with enhanced information density. This method of nonlinear charging is then used to charge a cell such that some potential ranges in the differential capacity diagram are omitted. This study is purely a simulative endeavor and not backed by experimentation owing mainly to the lack of facile implementation of arbitrary function inputs for battery cyclers and might point to limitations of the underlying theory. If found to be confirmed through an experiment, then this technique would, however, motivate a new roadmap to better understand secondary battery degradation inspired by electrocatalyst degradation.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"4 3","pages":"345–350"},"PeriodicalIF":4.3000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.3c00047","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.3c00047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Most secondary batteries in academia are (dis)charged by applying a constant current (CC) followed by a constant voltage (CV), i.e., a CCCV procedure. The usual concept is then to condense data for interpretation into representations such as differential capacity, or dQ/dV, graphs. This is done to extract information related to phenomena such as the growth of the solid electrolyte interphase or, more broadly, degradation. Typically, these measurements take several months because measurements for differential capacity analysis need to be performed at relatively low C-rates. An alternate charging schedule to CCCV is pulsed charging, where CC sections are interrupted by an open-circuit measurement on a second time scale. These and similar partially constant current strategies primarily target diffusive effects during charging and broadly fall into a linear charging category, where the time derivative for the actuated property is mostly zero. Herein, the author explores nonlinear charging, i.e., the process of actively applying a potential with a nontrivial time derivate and a resulting nontrivial current time derivative, to engineer (dis)charge cycles with enhanced information density. This method of nonlinear charging is then used to charge a cell such that some potential ranges in the differential capacity diagram are omitted. This study is purely a simulative endeavor and not backed by experimentation owing mainly to the lack of facile implementation of arbitrary function inputs for battery cyclers and might point to limitations of the underlying theory. If found to be confirmed through an experiment, then this technique would, however, motivate a new roadmap to better understand secondary battery degradation inspired by electrocatalyst degradation.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)