Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

Goldammer, Erik; Kowal, Julia

doi:10.3390/batteries7020036

Cited by 26 publications

(11 citation statements)

References 37 publications

(89 reference statements)

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“…The equivalent circuit model of the Gr anode can be simplified as shown in Figure S8 (Supporting Information) in a short‐term relaxing process, presuming complete relaxation of Q SEI . [ 23 ] The potential variations of a R CT Q CT parallel circuit contain a fractional derivative and the Mittag–Leffler function is used to incorporate a fraction relaxation in Equation (). [ 24 ]

\begin{equation} V(t)={\eta}_{\mathrm{CT}}\ensuremath{\times{}}\left\{1-{E}_{n}\left[-{\left(\frac{t}{{\tau}_{\mathrm{CT}}}\right)}^{n}\right]\right\},\, \left(0.5&lt;n&lt;1\right) \end{equation}

where E n ( z ) is the one‐parameter Mittag–Leffler function defined by Equation ().…”

Section: Resultsmentioning

confidence: 99%

“…As the relaxation progresses, only processes with larger time constants are relevant. [ 23 ] Considering that the RT constants of the R SEI Q SEI parallel circuit ( τ SEI ) are below 0.03 ms (Figure S6, Table S2, Supporting Information), rational selection of sampling rate and data range emphasizes the relaxing process of charge transfer. In this case, we performed pulse/relaxing tests on a 3‐electrode Li||Gr cell with a sampling rate of 2000 points s −1 after every 10 min of 0.2 C charging (corresponding to the Li intercalation of Gr anode), as displayed in Figure A.…”

Section: Resultsmentioning

confidence: 99%

“…The equivalent circuit model of the Gr anode can be simplified as shown in Figure S8 (Supporting Information) in a short-term relaxing process, presuming complete relaxation of Q SEI . [23] The potential variations of a R CT Q CT parallel circuit contain a fractional derivative and the Mittag-Leffler function is used to incorporate a fraction relaxation in Equation ( 2). [24] V…”

Section: Fast 𝝉 Ct Obtaining Enabled By Rt Detection Methodsmentioning

confidence: 99%

See 2 more Smart Citations

In Situ Li‐Plating Diagnosis for Fast‐Charging Li‐Ion Batteries Enabled by Relaxation‐Time Detection

Xiao

Yang

et al. 2023

Advanced Materials

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The Li plating behavior of Li‐ion batteries under fast charging conditions is elusive due to a lack of reliable indicators of the Li plating onset. In this work, the relaxation time constant of the charge transfer process (τCT) is proposed to be promising for the determination of Li plating onset. A novel pulse/relaxation test method enables a rapid access to the τCT of the graphite anode during battery operation, applicable to both half and full batteries. The diagnosis of Li plating at varying temperatures and charging rates enriches the cognition of Li plating behaviors. Li plating at low temperatures and high charging rates can be avoided because of the battery voltage limitations. Nevertheless, after the onset, severe Li plating evolves rapidly under harsh charging conditions, while the Li plating process under benign charging conditions is accompanied by a simultaneous Li intercalation process. The quantitative estimates indicate that Li plating at high temperatures/high charging rates leads to more irreversible capacity losses. This facile method with rational scientific principles can provide inspiration for exploring the safe boundaries of Li‐ion batteries.This article is protected by copyright. All rights reserved

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\begin{equation} V(t)={\eta}_{\mathrm{CT}}\ensuremath{\times{}}\left\{1-{E}_{n}\left[-{\left(\frac{t}{{\tau}_{\mathrm{CT}}}\right)}^{n}\right]\right\},\, \left(0.5&lt;n&lt;1\right) \end{equation}

where E n ( z ) is the one‐parameter Mittag–Leffler function defined by Equation ().…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Fast 𝝉 Ct Obtaining Enabled By Rt Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

In Situ Li‐Plating Diagnosis for Fast‐Charging Li‐Ion Batteries Enabled by Relaxation‐Time Detection

Xiao

Yang

et al. 2023

Advanced Materials

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“…They exhibit current-voltage dynamics on multiple time scales, from milliseconds to hours. 1 This can be observed both in the time domain (e.g., during a pulse test 2 ) and in the frequency domain (i.e., in electrochemical impedance spectra). 3 While "fast" dynamics (up to several seconds) are well-understood today, 4 "slow" dynamics on long timescales (typically one minute and beyond) have only recently become subject of interest.…”

Section: List Of Symbolmentioning

confidence: 99%

A Grey-box Model with Neural Ordinary Differential Equations for the Slow Voltage Dynamics of Lithium-ion Batteries: Model Development and Training

Brucker,

Bessler,

Gasper

2023

J. Electrochem. Soc.

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Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.

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“…For instance, in SOCs, the evolution of the intensity and the relaxation time of the DRT peaks is commonly studied under different working temperatures, current load, and gas flow rates and compositions [7]. In the case of Li-ion batteries, the DRT profile is commonly studied at different state-of-charge values [8][9][10], while on solar cells, it is examined under different light irradiation levels [11,12].…”

Section: Introductionmentioning

confidence: 99%

Operando Analysis of Losses in Commercial-Sized Solid Oxide Cells: Methodology Development and Validation

et al. 2022

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The development of decarbonised systems is being fostered by the increasing demand for technological solutions for the energy transition. Solid Oxide Cells are high-efficiency energy conversion systems that are foreseen for commercial development. They exhibit potential power generation and power-to-gas applications, including a reversible operation mode. Long-lasting high performance is essential for guaranteeing the success of the technology; therefore, it is fundamental to provide diagnosis tools at this early stage of development. In this context, operando analysis techniques help detect and identify incipient degradation phenomena to either counteract damage at its origin or correct operando protocols. Frequent switches from the fuel cell to the electrolyser mode add more challenges with respect to durable performance, and deep knowledge of reverse-operation-induced damage is lacking in the scientific and technical literature. Following on from preliminary experience with button cells, in this paper, the authors aim to transfer the methodology to commercial-sized Solid Oxide Cells. On the basis of the experimental evidence collected on planar square cells under dry and wet reactant feed gases, the main contributions to impedance are identified as being charge transfer (f = 103–104 Hz), oxygen surface exchanged and diffusion in bulk LSCF (f = 102–103 Hz), and gas diffusion in the fuel electrode (two peaks, f = 1–100 Hz). The results are validated using the ECM methodology, implementing an LRel(RctQ)GWFLW circuit.

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Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

Cited by 26 publications

References 37 publications

In Situ Li‐Plating Diagnosis for Fast‐Charging Li‐Ion Batteries Enabled by Relaxation‐Time Detection

In Situ Li‐Plating Diagnosis for Fast‐Charging Li‐Ion Batteries Enabled by Relaxation‐Time Detection

A Grey-box Model with Neural Ordinary Differential Equations for the Slow Voltage Dynamics of Lithium-ion Batteries: Model Development and Training

Operando Analysis of Losses in Commercial-Sized Solid Oxide Cells: Methodology Development and Validation

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