Dynamic Electrochemical Impedance Spectroscopy of a Three-Electrode Lithium-Ion Battery during Pulse Charge and Discharge

Huang, Jun; Ge, Hao; Li, Zhe; Zhang, Jianbo

doi:10.1016/j.electacta.2015.07.017

Cited by 67 publications

(45 citation statements)

References 22 publications

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“…This ability of EIS is the responsible that nowadays EIS has become a key experimental method in a large spectrum of fields [4]. This wide range of fields includes typically electrochemistry related fields as fuel cells [5][6][7][8][9][10][11][12], batteries [13][14][15][16][17], coatings [18][19][20], electrochemical sensors [21][22][23][24][25][26] and supercapacitors [27][28][29][30][31]. But it also includes fields not traditionally linked to electrochemistry as enzymatic kinetics [32], biochemistry [33][34][35], food quality control [36], cancer detection [37][38] and immunology [39][40], amongst others.…”

Section: Introductionmentioning

confidence: 99%

Application of a Montecarlo based quantitative Kramers-Kronig test for linearity assessment of EIS measurements

Giner-Sanz

Ortega

Pérez-Herranz

2016

Electrochimica Acta

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Electrochemical Impedance Spectroscopy (EIS) is a very powerful tool to study the behaviour of electrochemical systems. Three conditions must be fulfilled during EIS measurements: causality, linearity, and stability. If any of these conditions is not achieved, then the conclusions obtained from the analysis of the measured spectra may be biased, or even misguided. For this reason, the verification of the compliance of these conditions is critical before accepting any analysis performed on an experimental spectrum. In a previous work, an experimental spectrum quantitative validation technique based on Kramers-Kronig relations was presented. The validation method consists in a Kramers-Kronig (KK) validation test, by equivalent electrical circuit fitting, coupled with a Montecarlo error propagation method. The validation technique builds a consistency region for a given confidence level that allows to discriminate between the individual points of the EIS spectrum that are consistent with the four fundamental hypotheses, and the inconsistent ones. The aim of this work is to validate experimentally if the quantitative validation technique is able to detect nonlinearities. In order to achieve this goal, the EIS spectrum of a markedly nonlinear system was measured experimentally using different perturbation amplitudes. The validation technique was applied to each one of the measured spectra. The method successfully managed to identify large nonlinearities; but did not detect slight ones.

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Section: Introductionmentioning

confidence: 99%

Application of a Montecarlo based quantitative Kramers-Kronig test for linearity assessment of EIS measurements

Giner-Sanz

Ortega

Pérez-Herranz

2016

Electrochimica Acta

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“…26 In the charging-discharging process, only Joule heat Q J can be used to heat the battery. 27 Joule heat consists of ohmic heat and polarized heat are as shown in the Equation (2)…”

Section: Battery Heat Generation Powermentioning

confidence: 99%

Preheatingstrategy ofvariable‐frequencypulse for lithium battery in cold weather

2020

Int J Energy Res

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Aiming to the issue of charging difficulty and capacity fading for lithium-ion battery at low temperature, this study proposes a preheating strategy using variablefrequency pulse. The innovation of this paper is to propose the thermo-electric coupling model based on the electrochemical impedance spectroscopy of battery at different temperatures, integrated with variable frequency changing for pulse method to develop an effective inner pre-heating strategy. Meanwhile, the evaluating method of impact of this strategy on capacity fading of battery has also been proposed to examine its effectiveness, to find the optimal strategy. First, temperature rise model and the thermo-electric coupling model at different temperatures according to the equivalent circuit model of battery are presented. Further, optimal heating frequency of current pulse at different temperatures is calculated according to the changing of internal impedance. The results show that the optimal variable-frequency pulse pre-heating strategy can heat the lithium-ion battery from −20 C to 5 C in 1000 seconds. Meanwhile, it brings less damage to the battery health and improves the performance of battery in cold weather based on the views of power consumption, capacity attenuation, and internal impedance changes.

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“…However, the principle of the EIS method is to apply to a cell a sinusoidal signal and measure the characteristic response from the cell, it is not suitable for the real application. The aim of the EIS is to improve the speed of the calculation [8] and make use of the free signals of current and voltage during the operation of the battery instead of the additional burden of the input signal, so as to carry out SOH estimation in real time [9,10]. Moreover, Equivalent Circuit Models (ECMs) can simulate the static and dynamic behavior of a battery.…”

Section: Introductionmentioning

confidence: 99%

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

et al. 2018

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The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.

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Dynamic Electrochemical Impedance Spectroscopy of a Three-Electrode Lithium-Ion Battery during Pulse Charge and Discharge

Cited by 67 publications

References 22 publications

Application of a Montecarlo based quantitative Kramers-Kronig test for linearity assessment of EIS measurements

Application of a Montecarlo based quantitative Kramers-Kronig test for linearity assessment of EIS measurements

Preheatingstrategy ofvariable‐frequencypulse for lithium battery in cold weather

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

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