Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2024-02-12 DOI:10.1021/acsengineeringau.3c00047
Helge Sören Stein*, 
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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.

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用于娱乐、教育和应用的非线性电位动力电池充电协议
学术界的大多数二次电池都是通过恒定电流(CC)和恒定电压(CV)(即 CCCV 程序)进行(放电)的。通常的概念是将数据浓缩为差分容量图或 dQ/dV 图,以便进行解释。这样做是为了提取与固态电解质间相的增长或更广泛的降解等现象有关的信息。通常情况下,这些测量需要几个月的时间,因为差分容量分析测量需要在相对较低的 C 速率下进行。脉冲充电是 CCCV 的另一种充电方式,在脉冲充电中,CC 部分会被第二时间尺度的开路测量所中断。这些策略和类似的部分恒流策略主要针对充电过程中的扩散效应,大致属于线性充电类别,其中致动特性的时间导数大多为零。在本文中,作者探讨了非线性充电,即主动施加具有非三维时间导数和由此产生的非三维电流时间导数的电势,从而设计出信息密度更高的(去)充电循环。然后利用这种非线性充电方法为电池充电,从而省略差分容量图中的某些电位范围。这项研究纯粹是模拟性的,没有得到实验的支持,主要原因是电池循环器缺乏对任意函数输入的简单实现,而且可能会指出基础理论的局限性。不过,如果通过实验得到证实,那么这项技术将为更好地理解由电催化剂降解引发的二次电池降解提供新的思路。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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)
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