Nonlinear Frequency Response Analysis on Lithium-Ion Batteries: A Model-Based Assessment

Wolff, Nicolas; Harting, Nina; Heinrich, Marco; Röder, Fridolin; Krewer, Ulrike

doi:10.1016/j.electacta.2017.12.097

Cited by 37 publications

(45 citation statements)

References 21 publications

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“…Both electrodes have been produced in the Battery LabFactory Braunschweig and been calendered by 10 % resulting in the volume fractions given in Table 3 at a loading of 4:19 mg cm 2 and 9:88 mg cm 2 for anode and cathode respectively. Fixed model parameters: (a) measured, (b) calculated according to [43] (c) literature value from, [7] (d) literature value from [18]. Copper current collector, aluminium current collector and the pouch packaging have been supplied by Somitomo, Hydro and DNP respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The electrolyte is operated outside its electrochemical stability window and it is therefore being reduced at the anode during cycling. This has been implemented by Ong et al [6] and later by Legrand et al [7] To analyse ageing phenomena, such models need to be extended by an ageing model including a SEI that accounts for changes due to ageing. This SEI is believed to be the dominating effect for an increase of cell impedance and a loss of capacity.…”

Section: Introductionmentioning

confidence: 99%

“…In order to simulate the EIS of such a physicochemical model, the charging and discharging of double layers has to be included. This has been implemented by Ong et al [6] and later by Legrand et al [7] To analyse ageing phenomena, such models need to be extended by an ageing model including a SEI that accounts for changes due to ageing. Most of the following works though do not analyse the ageing impact on the impedance spectrum.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Physico‐Chemical Modeling of a Lithium‐Ion Battery: An Ageing Study with Electrochemical Impedance Spectroscopy

Heinrich

Wolff

Harting

et al. 2019

Batteries & Supercaps

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Electrochemical Impedance Spectroscopy measurements and simulations are performed on a nickel manganese cobalt oxide (NMC)/graphite pouch cell. A physico-chemical continuum battery model is extended by a physical ageing model including a Solid Electrolyte Interphase. The model assumes a loss of electrochemically active surface area at anode and cathode as well as a growth of solid electrolyte interphase (SEI) layer thickness. These ageing parameters have been adjusted with an algorithm to achieve agreement between simulated and measured spectra. The results for a 28 mAh pouch cell show that the ageing model is suitable to correlate the change of the impedance spectrum with the degree of degradation of the cell. In detail, SEI thickness is shown to increase by 45 nm, while the anode and cathode loose 20 % and 57 % of their electrochemically active surface area, respectively. In addition, deviating measurement conditions and the end of life of the cell can be indicated by the parameter identification algorithm. Furthermore, it is demonstrated, that the change of the high and low frequency semicircles can be assigned to the anode SEI and cathode respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physico‐Chemical Modeling of a Lithium‐Ion Battery: An Ageing Study with Electrochemical Impedance Spectroscopy

Heinrich

Wolff

Harting

et al. 2019

Batteries & Supercaps

Self Cite

View full text Add to dashboard Cite

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“…Prior to NFR data analysis and the development of the degradation model, appropriate frequency ranges for the relevant processes had to be identified in the NFR spectrum of the ageing training data sets [24,26]. In Figure 6, NFR is shown over the frequency at specific cycle steps from 0 to 400 cycles, at each 50th cycle, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, a novel dynamic analysis method, the Nonlinear Frequency Response Analysis (NFRA), was established and applied to LIBs to investigate cell behaviour when it is excited with a nonlinear signal [24][25][26]. Thus, the effect of ageing mechanisms on the measured cell response is enhanced, and NFRA-based data might be more suitable for reliable SoH identification [24].…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

et al. 2018

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Abstract:In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 • C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general.

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Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

Schlüter

Novák

Schröder

2022

Advanced Energy Materials

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and the effectiveness and durability of the materials applied, researchers always call for reliable and detailed knowledge of the mechanism(s) of both, main and side reactions, and especially for their kinetics. In order to gain the relevant information, a large number of methods have been proposed to characterize battery materials, battery electrodes and their interfaces, and battery cells. But when samples are taken out of the cells and analyzed later, there is a high risk of both, contamination and changes due to possible relaxation processes, and the challenge is to draw sound conclusions in respect to a "living and breathing" battery cell, i.e., to a cell under current flow (cf. Figure 1). Therefore, the so-called in situ and operando (and often online) methods are preferred. Sometimes they are the only option to collect relevant data to understand and improve materials for energy systems. [5] In many cases, results from different techniques need to be combined to obtain sound conclusions. [6,7] The terminology for the aforementioned methods shifted during the last decade and is not always consistent throughout the literature. Nowadays, the general understanding is as follows: An operando experiment means a characterization experiment in an electrochemical cell (e.g., battery, fuel cell, electrolyzer) under current flow, with either the potential or the current controlled. One of the many examples is the operando neutron powder diffraction, a technique analyzing the changes in the structure of battery materials in "real" battery cells under current flow with the help of neutron diffraction. [9] Another example is the operando X-ray photoelectron spectroscopy experiment (normally limited to vacuum-compatible cells with nonvolatile electrolytes) providing information about the changes of the chemical composition of the interfacial species with the potential. [10,11] An in situ experiment is also an experiment in a battery cell, however, the current flow is interrupted when the data are collected. An example here is the in situ X-ray absorption spectroscopy (XAS) experiment in which the cell has to be disconnected for technical reasons during the operation of the detector [7] or the tomography of energy materials by means of neutron or X-ray source. [12,13] Online techniques are understood here as either continuously or periodically analyzed samples that are taken out of the electrochemical cell. A typical example is the online (differential) electrochemical mass spectrometry (often shortened as online electrochemical mass spectrometry (OEMS)/differential electrochemical mass spectrometry (DEMS)) analyzing the volatile products of electrochemical reactions with the help of a mass spectrometer by

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Nonlinear Frequency Response Analysis on Lithium-Ion Batteries: A Model-Based Assessment

Cited by 37 publications

References 21 publications

Physico‐Chemical Modeling of a Lithium‐Ion Battery: An Ageing Study with Electrochemical Impedance Spectroscopy

Physico‐Chemical Modeling of a Lithium‐Ion Battery: An Ageing Study with Electrochemical Impedance Spectroscopy

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

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