Comparison of chronoamperometric response and rate-performance of porous insertion electrodes: Towards an accelerated rate capability test

Heubner, Christian; Lämmel, C.; Nickol, A.; Liebmann, Tobias; Schneider, Michael; Michaelis, Alexander

doi:10.1016/j.jpowsour.2018.06.087

Cited by 25 publications

(37 citation statements)

References 22 publications

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“…Wong et al[30] used equation 5 to fit a large number of Q/M v CR data sets from the literature. They found correlations between  and various electrode parameters such as particle size and the activation energy associated with solid state diffusion (with the latter relationship implying an inverse scaling between  and solid-state diffusion coefficient, consistent with equation 3 and equation 12 below).Equations 6 and 7 were both derived[31] using methods[35] designed to convert chronoamperometry data to capacity-rate data. These equations have the advantage that they have some physical basis, are expressed in a simple form and fit experimental data well.…”

mentioning

confidence: 71%

Developing models to fit capacity–rate data in battery systems

Coleman

Tian

2020

Current Opinion in Electrochemistry

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Analyzing rate-performance data in battery electrodes is greatly facilitated by access to simple analytical models. Here we describe a number of simple equations for fitting capacity-rate data. These equations output fit parameters, such as the characteristic time, which quantify rate-performance. This characteristic time can be linked to mechanistic effects, such as diffusion and electrical limitations, allowing quantitative analysis.

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mentioning

confidence: 71%

Developing models to fit capacity–rate data in battery systems

Coleman

Tian

2020

Current Opinion in Electrochemistry

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“…To understand individual contributions of Si and Gr to the rate capability of Si/Gr blended electrodes, rate performance tests based on chronoamperometry [74,75] were carried out using Si/Gr MLBE. Figure 4a shows the deconvoluted capacity as a function of the C‐rate obtained from a Si/Gr MLBE ( m Si : m Gr 1 : 19) for the case of lithiation.…”

Section: Resultsmentioning

confidence: 99%

Understanding Component‐Specific Contributions and Internal Dynamics in Silicon/Graphite Blended Electrodes for High‐Energy Lithium‐Ion Batteries

Heubner

Liebmann

Lohrberg

et al. 2021

Batteries & Supercaps

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Blended electrode materials containing high‐capacity silicon (Si) and robust graphite (Gr) materials are considered advanced alternatives to pure graphite electrodes used in Li‐ion batteries. Understanding the component‐specific lithiation and delithiation behavior and electrochemical interactions between the blended materials is of crucial importance for targeted optimization of composition and microstructural design, yet hardly addressed to date. Herein, a model‐like Si/Gr blended electrode and special electrochemical cell are introduced to directly capture the component specific behaviors for the first time. This includes studies of the formation cycles, the reaction distribution between Si and Gr, the component‐specific contributions to the capacity at different charge and discharge rates, and the internal dynamics during pulse loads and subsequent relaxation. The deconvolution of the components’ behavior during operation provides fundamental insights that contribute to a profound understanding and targeted optimization of Si/Gr blended electrodes. Furthermore, the application of the presented experimental approach can serve scientists to identify and study other advanced materials combinations as blended electrodes for rechargeable batteries.

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“…8 The cells were first tested via GCD measurements (BioLogic VMP-3. Three cycles were performed between Current transients from CA were converted to specific capacity (Q/M) vs. rate (R) curves using the following equations: 8,22 ( )…”

Section: Electrochemical Measurementsmentioning

confidence: 99%

Quantifying the Dependence of Battery Rate Performance on Electrode Thickness

Horváth

Coelho

Tian

et al. 2020

ACS Appl. Energy Mater.

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Simultaneous optimisation of capacity and rate-performance in battery electrodes would be much simplified by access to a simple equation relating rate-performance to electrode thickness. While a number of equations have been proposed, data on the effect of electrode thickness on rate-performance is not extensive enough to identify the most appropriate model for thickness-dependence. Here, using LiNi0.815Co0.15Al0.035O2 (NCA) as a model system, we use chronoamperometry as a procedure to rapidly generate capacity-rate curves for >50 different electrode thicknesses. Using a semi-empirical fitting equation, we extract the characteristic time () associated with charge/discharge for each thickness (LE). We find the resultant -LE curve to be inconsistent with minimal models based on liquid-or solidphase-diffusion alone, but to be in excellent agreement with a relatively simple rate model which includes liquid-and solid-phase-diffusion effects as well as electrical and electrochemical limitations. Thickness-dependent impedance measurements show that the magnitudes of the electrochemical and solid-state diffusion contributions are perfectly in line with the outputs of the rate model.

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Comparison of chronoamperometric response and rate-performance of porous insertion electrodes: Towards an accelerated rate capability test

Cited by 25 publications

References 22 publications

Developing models to fit capacity–rate data in battery systems

Developing models to fit capacity–rate data in battery systems

Understanding Component‐Specific Contributions and Internal Dynamics in Silicon/Graphite Blended Electrodes for High‐Energy Lithium‐Ion Batteries

Quantifying the Dependence of Battery Rate Performance on Electrode Thickness

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