Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance

Zhu, Jiangong; Darma, Mariyam Susana Dewi; Knapp, Michael; Sørensen, Daniel Risskov; Heere, Michael; Fang, Qiaohua; Wang, Xueyuan; Dai, Haifeng; Mereacre, Liuda; Senyshyn, Anatoliy; Wei, Xuezhe; Ehrenberg, Helmut

doi:10.1016/j.jpowsour.2019.227575

Cited by 178 publications

(103 citation statements)

References 59 publications

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“…Battery degradation is directly observed through measuring the capacity over various cycles (long-term cycling). Degradation mechanisms are commonly attributed to the loss of cyclable lithium due to SEI formation, the increase of internal battery resistance, structural changes, and mechanical degradation (the pulverization of active material or formation of electrode cracks) [ 44 ]. The binder is directly responsible for maintaining electrode integrity.…”

Section: Binder Characteristics and Performance Evaluationmentioning

confidence: 99%

Polymer Binders: Characterization and Development toward Aqueous Electrode Fabrication for Sustainability

Cholewinski

Uceda

et al. 2021

Polymers

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Binders play an important role in electrode processing for energy storage systems. While conventional binders often require hazardous and costly organic solvents, there has been increasing development toward greener and less expensive binders, with a focus on those that can be processed in aqueous conditions. Due to their functional groups, many of these aqueous binders offer further beneficial properties, such as higher adhesion to withstand the large volume changes of several high-capacity electrode materials. In this review, we first discuss the roles of binders in the construction of electrodes, particularly for energy storage systems, summarize typical binder characterization techniques, and then highlight the recent advances on aqueous binder systems, aiming to provide a stepping stone for the development of polymer binders with better sustainability and improved functionalities.

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Section: Binder Characteristics and Performance Evaluationmentioning

confidence: 99%

Polymer Binders: Characterization and Development toward Aqueous Electrode Fabrication for Sustainability

Cholewinski

Uceda

et al. 2021

Polymers

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“…This phenomenon has a lot to do with the charge and discharge mode of the cyclic life tests we set, making the batteries always work in a wide SoC range. Frequent deep insertion and extraction of lithium ions will induce greater mechanical stress within the active material particles, which further causing more damage to the electrode material, such as lithiated material loss and particle cracking [19].…”

Section: Quantitative Results Of Degradation Modes Under Different Comentioning

confidence: 99%

“…Schindler [18], Zhu [19], and Pastor-Fernández [10,20], among others, inherited the concept of degradation mode and further studied the methods of identifying and quantifying different degradation modes. Schindler et al [18] built a model-based framework for analysis of degradation pathways and realized the aging contributions separation from LLI and LAM.…”

Section: Lammentioning

confidence: 99%

“…Non-destructive methods include incremental capacity analysis (ICA), differential voltage analysis (DVA) and electrochemical impedance spectroscopy (EIS). Zhu et al [19] combined quantification method using DVA and EIS with post-mortem analysis to enhance the analysis of the aging mechanisms. Pastor-Fernández et al [20] made a comparison of quantitative results between EIS and incremental capacity-differential voltage (IC-DV) and concluded that the EIS method offer a better resolution of the degradation modes.…”

Section: Lammentioning

confidence: 99%

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Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

et al. 2021

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The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.

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“…7 Electrochemical impedance spectroscopy (EIS) is a promising technique that provides insight into the battery condition through exposing the changes in parameters pertaining to the battery internal resistance and electrochemical properties, and has presented considerable improvements in measurement times. These qualities justify its extensive prior applications in investigation of battery condition [8][9][10][11] and SoH. 12,13 It has been demonstrated through equivalent circuit (EC) modelling of the impedance response of cells that the charge transfer resistance extracted from EIS measurements is a useful indicator of SoH 13 ; however, a principal limitation is that individually such parameters do not always vary significantly or linearly with the battery age, 14 and therefore it is suggested that these approaches are inadequate for SoH estimation in this case.…”

Section: Introductionmentioning

confidence: 99%

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Rastegarpanah

Hathaway

Ahmeid

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

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Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance

Cited by 178 publications

References 59 publications

Polymer Binders: Characterization and Development toward Aqueous Electrode Fabrication for Sustainability

Polymer Binders: Characterization and Development toward Aqueous Electrode Fabrication for Sustainability

Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

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