Determining the maximum charging currents of lithium-ion cells for small charge quantities

Grimsmann, Florian; Gerbert, T.; Brauchle, Felix; Gruhle, A.; Parisi, J.; Knipper, Martin

doi:10.1016/j.jpowsour.2017.08.044

Cited by 25 publications

(16 citation statements)

References 18 publications

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“…Figure 4c shows the EIS plots of the same cell at different SOC levels. As it can be observed [39,40,175,176] , during charge/discharge, impedance in the mid-frequency range varies with SOC. Both ZCTL and ZSEI contribute to this range.…”

Section: Electrochemical Impedance Spectroscopymentioning

confidence: 79%

“…Li-ion battery comprises of a series of multiple processes including Li-ion diffusion in the electrolyte, migration through the SEI layer, charge transfer through the electrode/electrolyte interface, solid-state diffusion in the bulk of active material and electron transfer external to the battery via the current collectors [39,40,174] . These processes introduce impedance to the charge flow and cause a voltage drop between the two electrodes.…”

Section: Electrochemical Impedance Spectroscopymentioning

confidence: 99%

“…Deviation from this curve indicates Li plating. (5) Measurement of cell thickness [36][37][38][39] . Li metal deposition induces larger volume change than Li + intercalation into graphite, estimated to be 0.37 cm 3 /Ah [36] .…”

Section: Introductionmentioning

confidence: 99%

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Lithium Plating Detection Methods in Lithium-Ion Batteries

2020

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Lithium-ion batteries provide a low-cost, long cycle-life and high energy density solution to the expediting energy requirement of the automotive industry. There is a growing need of fast charging batteries for commercial application. However, large charging currents may cause Lithium plating which describes the deposition of metallic Lithium at the anode surface. It takes place at conditions of high currents and/or low temperatures because of kinetic limitations. The main reason behind plating is the slow solid-state diffusion of Lithium ions inside the active material. If the anode surface potential falls below 0 V versus Li/Li+, the formation of metallic Lithium is thermodynamically feasible. To avoid or reduce the amount Lithium plating, it is essential to detect its onset during a charging event. Determination of accurate Lithium plating curve is crucial in estimating the boundary conditions for battery operation without compromising life and safety. There are various data analysis methods involved in deriving the Lithium plating curve: anode potential using a three-electrode cell, variation of relaxation voltage after charging (dV/dt), variation of accumulated charge with voltage (dQ/dV) and coulombic efficiency of charge/discharge with %SOC (state of charge) are more commonly employed techniques. In addition to these methods, estimation of its occurrence is typically underpinned by electrochemical models where the negative (anode) electrode potential is expressed by a set of partial differential equations based on the electrochemical and physical properties of the battery components such as the electrodes and electrolyte. The present paper reviews the common test methods and analysis that are currently utilized in Lithium plating determination. Knowledge gaps are identified, and recommendations are made for the future development in the determination and verification of Lithium plating curve in terms of modelling and analysis.

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Section: Electrochemical Impedance Spectroscopymentioning

confidence: 79%

Section: Electrochemical Impedance Spectroscopymentioning

confidence: 99%

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Lithium Plating Detection Methods in Lithium-Ion Batteries

2020

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“…Rieger et al [264] applied a more accurate laser scanning technique to detect local thickness changes during operation. Grimsmann et al [265] proposed an inductance sensor based on the eddy current principle with the advantage of high resolution and noncontact to monitor the thickness change of the cell. However, this method is only suitable for pouch cells to detect the volume change after lithium plating.…”

Section: Online Monitoringmentioning

confidence: 99%

Building Safe Lithium-Ion Batteries for Electric Vehicles: A Review

Duan

Tang

Dai

et al. 2019

Electrochem. Energ. Rev.

542

260

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Lithium-ion batteries (LIBs), with relatively high energy density and power density, have been considered as a vital energy source in our daily life, especially in electric vehicles. However, energy density and safety related to thermal runaways are the main concerns for their further applications. In order to deeply understand the development of high energy density and safe LIBs, we comprehensively review the safety features of LIBs and the failure mechanisms of cathodes, anodes, separators and electrolyte. The corresponding solutions for designing safer components are systematically proposed. Additionally, the in situ or operando techniques, such as microscopy and spectrum analysis, the fiber Bragg grating sensor and the gas sensor, are summarized to monitor the internal conditions of LIBs in real time. The main purpose of this review is to provide some general guidelines for the design of safe and high energy density batteries from the views of both material and cell levels.

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“…Battery thickness measurement is based on the irreversible increase in battery thickness when lithium plating occurs, and this method can detect lithium plating during charging and in real-time. 26,27 However, it has stringent test requirements and is only suitable for pouch batteries due to their thickness sensitivity, so it has little significance for practical applications. Jin et al 28 developed a detection method by capturing the leaked H 2 gas generated by the reaction of metallic plated lithium and polymer binders during overcharge, but it may not work under fast and low-temperature charging.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

Tian

Lin

et al. 2022

EcoMat

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Lithium plating seriously threatens the life of lithium-ion batteries at low temperatures charging conditions, but the onboard detection and quantification of lithium plating are severely hampered by the limited available signals and volatile operating conditions in real scenarios. Herein, we propose a detection method to predict the occurrence and quantification of lithium plating under uncertain conditions by only using constant-current curves during charging based on deep learning. A deep neural network (DNN) is developed to extract data-driven features induced by lithium plating from the charge curves, avoiding the challenge of manual feature selection. Only using the most common voltage and current signals as inputs, the network exhibits superior adaptability and accuracy. The detection accuracy of the proposed method is 98.64%, while the quantity of the lithium plating can be accurately predicted with a root-mean-square error <4.1712 mg. Moreover, the generalization ability of the proposed method is verified by its reliable detection accuracy under conditions that are not used in the training dataset. The detection accuracy is 92.39% for brand new charging conditions and 95.53% for brand new aging states. This method shortens the detection time that currently takes more than several hours (the widely used differential curve analysis) to milliseconds and eliminates the need for a rigorous testing environment, showing great potential for onboard application in future battery management systems.

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Determining the maximum charging currents of lithium-ion cells for small charge quantities

Cited by 25 publications

References 18 publications

Lithium Plating Detection Methods in Lithium-Ion Batteries

Lithium Plating Detection Methods in Lithium-Ion Batteries

Building Safe Lithium-Ion Batteries for Electric Vehicles: A Review

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

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