The effects of cycling on ionic and electronic conductivities of Li –ion battery electrodes

Pouraghajan, Fezzeh; Thompson, Andrea I.; Hunter, Emilee E; Mazzeo, Brian A.; Christensen, Jake; Subbaraman, Ram; Wray, Michael; Wheeler, Dean R.

doi:10.1016/j.jpowsour.2021.229636

Cited by 22 publications

(17 citation statements)

References 52 publications

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“…[ 40 ] Besides that, during cycling the graphite electrode undergoes volume changes due to (de‐)intercalation that leads to deformations of the graphite structure which, in turn, may lead to better ionic conductivity. [ 41 ] However, from the literature it is known that cycling also leads to SEI growth which is associated with an increased NE resistance. [ 9 ] In this study, the cells have a lower NE resistance after 244 cycles, which means that the NE resistance increase due to SEI growth was compensated by the other effects.…”

Section: Resultsmentioning

confidence: 99%

Fast Charging Formation of Lithium‐Ion Batteries Based on Real‐Time Negative Electrode Voltage Control

2022

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In lithium-ion battery production, the formation of the solid electrolyte interphase (SEI) is one of the longest process steps. [1] The formation process needs to be better understood and significantly shortened to produce cheaper batteries. [2] The electrolyte reduction during the first charging forms the SEI at the negative electrodes. [3,4] Besides that, a SEI is also formed at the positive electrode (PE-SEI) during the first cycles. [5,6] Especially, the SEI has a substantial impact on the battery's performance and aging by limiting further reductive decomposition of the electrolyte. [7] The SEI material compositions and electrochemical properties have been reviewed extensively. [7][8][9][10] State-of-the-art formation procedures in the industry last up to multiple days [1] and consist of several charge and discharge cycles. [11] Long periods at high state of charges (SOCs) increase the reduction rate at the negative electrode (NE) and the oxidation rate at the positive electrode (PE) that lead to the formation of the SEI and PE-SEI, respectively. [12] Higher SOCs lead to lower NE potentials which exponentially increase the SEI growth rate whereas increased C-rates have a linear relation. [13] Similar results for accelerated SEI growth at low NE potentials were simulated by a model based on differential voltage analysis. [14] Formation strategies with multiple subcycles at high SOCs are recommended in the literature as it was found to guarantee stable surface layer properties that resulted in good cell performance while ensuring shorter formation times, e.g., 21, [15] 14, [1] and 13 h. [16] Increased ambient temperatures and external pressure enabled a formation time about 3 h. [17] Variable fast charging current rates based on an electrode equivalent circuit model even resulted in a formation time below 1 h without negative impacts on cell performance compared to longer reference formations. [18] However, comparing formation times between different cell configurations is complicated, as different materials (e.g., electrolyte or active material) or properties (e.g., coating thickness or porosity) influence the maximum applicable current. [19] Charging currents that lead to negative NE potentials may form lithium-plating on the NE's surface [20][21][22] as lithium ions react to metallic lithium depositions instead of intercalating into the NE. [23,24] In general, lithium-plating is an undesired sidereaction which comes along with capacity loss and may result in an internal short circuit due to dendrite formation. [25][26][27] Just a part of the plated lithium is reversible and reacts back to lithium ions during discharging, which is called lithiumstripping. [28] Irreversible lithium-plating can be proved by cell disassembly and optical investigation of the NE. Therefore, it is recommended to discharge the cell to 0% SOC followed by a

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Section: Resultsmentioning

confidence: 99%

Fast Charging Formation of Lithium‐Ion Batteries Based on Real‐Time Negative Electrode Voltage Control

2022

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“…In later studies, changes to electronic conductivity aer cycling were measured for four types of electrode; LCO, LFP (LiFePO 4 ) and NMC (LiNi 1−x−y -Mn x Co y O 2 ) cathodes, and graphite anodes. 31 Cycling caused a signicant decrease in conductivity for the LCO and graphite electrodes, a smaller decrease for the NMC cathode, and a marginal increase for the LFP cathode. The data analysis also extracted values for the contact resistance between the coating and the current collector.…”

Section: Rsc Advancesmentioning

confidence: 96%

Measurement of anisotropic volumetric resistivity in lithium ion electrodes

Lain,

Apachitei,

Dogaru

et al. 2023

RSC Adv.

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“…Pouraghajan et al reported substantial changes in electrode tortuosity and electronic resistivity caused by battery aging in graphite anodes and different cathode chemistries (including LCO, NMC, and LFP). [169] Hence, regular re-parameterization based on recorded voltage and current data is necessary throughout the lifetime of the battery for accurate prediction of LIB deterioration.…”

Section: Parameter Identification Of Electrochemical-aging Coupled Modelmentioning

confidence: 99%

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Ding,

Li,

Dai

et al. 2023

Advanced Energy Materials

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Precise prediction of lithium‐ion cell level aging under various operating conditions is an imperative but challenging part of ensuring the quality performance of emerging applications such as electric vehicles and stationary energy storage systems. Accurate and real‐time battery‐aging prediction models, which require an exact understanding of the degradation mechanisms of battery components and materials, could in turn provide new insights for materials and battery basic research. Furthermore, the primary barrier to meaningful artificial intelligence/machine learning for accelerating the prediction period is the exploitation of accurate aging mechanistic descriptors. This review comprehensively summarizes the evolution of deterioration mechanisms at the material and cell level in different environments and usage scenarios, including the intricate relationships between aging mechanisms, degradation modes, and external influences, which are the cornerstones of modeling simulation and machine learning techniques. Recent advances in electrochemical models coupled with internal battery degradation mechanisms as well as identification and tracking of aging parameters are shown, with particular emphasis on electrode balance and the anticipated trend of machine learning‐assisted reliable remaining useful life prediction. Precise simulation prediction of cell level aging will continue to play an essential role in advanced smart battery research and management, enhancing its performance while shortening experimental sequences.

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The effects of cycling on ionic and electronic conductivities of Li –ion battery electrodes

Cited by 22 publications

References 52 publications

Fast Charging Formation of Lithium‐Ion Batteries Based on Real‐Time Negative Electrode Voltage Control

Fast Charging Formation of Lithium‐Ion Batteries Based on Real‐Time Negative Electrode Voltage Control

Measurement of anisotropic volumetric resistivity in lithium ion electrodes

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

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