Influence of Conductive Additives and Binder on the Impedance of Lithium-Ion Battery Electrodes: Effect of Morphology

Hein, Simon; Danner, Timo; Westhoff, Daniel; Prifling, Benedikt; Scurtu, Rares-George; Kremer, Lea; Hilger, André; Osenberg, Markus; Manke, Ingo; Wohlfahrt‐Mehrens, Margret; Schmidt, Volker; Latz, Arnulf

doi:10.1149/1945-7111/ab6b1d

Cited by 131 publications

(163 citation statements)

References 70 publications

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“…However, in their simulations the authors neglected the concentration dependence of transport parameters, which we implemented in our model. Moreover, our studies show the importance of electrode microstructure, as well as carbon black and binder morphology, [10] on the ionic transport, especially in thick electrodes [11,12] …”

Section: Introductionmentioning

confidence: 65%

Influence of the Electrolyte Salt Concentration on the Rate Capability of Ultra‐Thick NCM 622 Electrodes

Kremer

Danner

Hein

et al. 2020

Batteries & Supercaps

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In traditional Li‐ion batteries, the electrolyte consists of a Li‐conducting salt dissolved in organic solvents at a concentration of ∼ 1 mol L−1 (1 M). In this work, we use increased LiPF6 concentrations between 1 and 2.3 M to investigate the influence of the electrolyte salt concentration on the rate capability of ultra‐thick (49.5 mg cm−2) and thin (5.6 mg cm−2) NCM 622 electrodes, respectively. At higher electrolyte salt concentrations than 1 M, thin electrodes suffer from increased polarization, due to a higher viscosity and a reduced ionic conductivity. In contrast, by raising the salt concentration from 1 to 1.9 M the discharge capacity of ultra‐thick electrodes is increased by more than 50 % for current densities above 3 mA cm−2, which significantly improves their rate capability. 3D microstructure resolved simulations revealed that this effect results from the mitigation of Li‐ion depletion in the electrolyte filled pore space of ultra‐thick electrodes.

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Section: Introductionmentioning

confidence: 65%

Influence of the Electrolyte Salt Concentration on the Rate Capability of Ultra‐Thick NCM 622 Electrodes

Kremer

Danner

Hein

et al. 2020

Batteries & Supercaps

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“…A plausible explanation lies in the high uncertainty of the representation of the carbon and binder additives. Since the additive phase is very difficult to image, several groups [33,34] have tried to numerically generate it by various approaches. Their results indicate that, depending on how this phase is distributed within the pore, its impact on the tortuosity factor varies significantly.…”

Section: Discussionmentioning

confidence: 99%

Effect of Anode Porosity and Temperature on the Performance and Lithium Plating During Fast‐Charging of Lithium‐Ion Cells

et al. 2020

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Twenty-four single-layer %32 mAh pouch cells are tested to determine the effect of electrode porosity on lithium plating. Twelve cells contain a graphite electrode that is 26% porous, and 47% for the other twelve. The cells are cycled using a 6-C charge and a C/2 discharge protocol at temperatures in the range of 20-50 C. A macro-homogeneous electrochemical model and microstructure analysis tool set are used to help interpret experimental observations for the effect of anode porosity and ambient temperature on fast-charging performance. Comparison between the two also highlights gaps in current theoretical understanding that need to be addressed. In post-test examination, lithium plating is seen in all cells, regardless of porosity. Elevated temperature is shown to reduce the amount of lithium plating and improve initial fast-charge capacity, but also changes the rate of other, less well-understood degradation mechanisms. Apparent kinetic rate laws, At þ Bt 1/2 , where A and B are constants, can be fit to most of the capacity loss and resistance increase data. The relative magnitudes of A and B change with temperature and porosity. The capacity loss data at 50 C from the high-porosity cells are fit by a logistics rate law.

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“…The ratio of binder and additive can affect the mechanical stability and the conductivities of the cathode material; therefore, this would have an impact in predicting the long‐term discharge capacities. [ 32 ] However, due to the complexity in curating such information while considering that the active LMO component being the main driving force for the electrochemical reactions, such mixing ratio is assumed to be standardized during the data collection and is not involved in the model construction. Second, the microstructure of the cathode material such as particle sizes and their distribution as well as their surface morphologies are known to be affecting the conductivity and the rate of electrochemical reactions of the cathode.…”

Section: Resultsmentioning

confidence: 99%

Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium‐Ion Batteries Applications

et al. 2021

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The electrochemical potentials of spinel lithium manganese oxide (LMO) have long been plagued by the significant Mn3+ dissolution during long cycle discharging, resulting in rapid capacity fading and short cycle life. Although the doping mechanisms are effective in suppressing these reactions, the correlations of their effects on the material properties and the improved discharging performance still remain uncovered. In this study, seven machine learning (ML) methods are applied to a manually curated dataset of 102 doped LMO spinel systems to predict the initial discharge capacities (IC) and 20th cycle end discharge capacities (EC) from fundamental system properties like material molar mass and crystal structure dimension. Gradient boosting models achieved the best prediction powers for IC and EC with their errors estimated to be 11.90 and 11.77 mAhg−1, respectively. Besides, a higher formula molar mass of doped LMO can improve both capacities and additionally, a shorter crystal lattice dimension with a dopant with smaller electronegativity can slightly improve the value of the IC and EC, respectively. This study demonstrates the great potential of using ML models to both predict the discharging performance of doped spinel cathodes and identify the governing material properties for controlling the discharging performance.

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Influence of Conductive Additives and Binder on the Impedance of Lithium-Ion Battery Electrodes: Effect of Morphology

Cited by 131 publications

References 70 publications

Influence of the Electrolyte Salt Concentration on the Rate Capability of Ultra‐Thick NCM 622 Electrodes

Influence of the Electrolyte Salt Concentration on the Rate Capability of Ultra‐Thick NCM 622 Electrodes

Effect of Anode Porosity and Temperature on the Performance and Lithium Plating During Fast‐Charging of Lithium‐Ion Cells

Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium‐Ion Batteries Applications

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