Low-temperature charging of lithium-ion cells part I: Electrochemical modeling and experimental investigation of degradation behavior

Tippmann, Simon; Walper, Daniel; Balboa, Luis; Spier, Bernd; Bessler, Wolfgang G.

doi:10.1016/j.jpowsour.2013.12.022

Cited by 265 publications

(189 citation statements)

References 31 publications

Supporting

Mentioning

179

Contrasting

Order By: Relevance

“…• C. 17 Furthermore, this result is in agreement with recent results from Dahn's group. 16 The authors found Li deposition occurs at a given temperature, if a certain value of the charging C-rate is exceeded.…”

supporting

confidence: 91%

“…8,[12][13][14][15] Due to the high chemical reactivity of metallic Li, it readily reacts with electrolyte leading to capacity loss of the cell. 8,9,14,16 It is known that Li deposition mainly depends on (i) charging C-rate, 14,16,17 (ii) temperature, 8,9,12,14,[16][17][18] and (iii) end-of-charge voltage/state-of-charge. 14,17 Several authors reported trends for variation of only one of these parameters respectively.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Interplay of Operational Parameters on Lithium Deposition in Lithium-Ion Cells: Systematic Measurements with Reconstructed 3-Electrode Pouch Full Cells

Waldmann

Hogg

Kasper

et al. 2016

J. Electrochem. Soc.

158

111

View full text Add to dashboard Cite

Deposition of metallic Li is a severe aging mechanism in Lithium-ion cells. This study evaluates the influence of the main operating parameters leading to deposition of Li: temperature, charging C-rate, and end-of-charge voltage. Therefore both, graphite anodes and NMC cathodes from commercial 16Ah pouch cells are reconstructed into 3-electrode full cells Due to their comparably high energy and power densities, Lithiumion batteries are currently used in state-of-the-art electric cars.1-3 In automotive applications, battery life-times of 10 years are expected for customer acceptance. However, it is known that life-time of Lithiumion batteries is limited by aging mechanisms.4-11 One of these aging mechanisms is deposition of metallic Li on anodes. 8,[12][13][14][15] Due to the high chemical reactivity of metallic Li, it readily reacts with electrolyte leading to capacity loss of the cell. 8,9,14,16 It is known that Li deposition mainly depends on (i) charging C-rate, 14,16,17 (ii) temperature, 8,9,12,14,[16][17][18] and (iii) end-of-charge voltage/state-of-charge.14,17 Several authors reported trends for variation of only one of these parameters respectively. E.g. low temperatures during charging are reported to lead to Li plating on graphite anodes. 8,9,14,16,19 However, there is a dearth of experimental results on the topic how specific combinations of operating conditions affect Li deposition. Since such experiments are highly interesting for extension of cycle life 14,20 and improvement of cell safety, 21 this is the topic of the present paper.The reason for deposition of Li on anodes are negative anode potentials E anode vs. (Li/Li + ). 8,12,14,20 Unfortunately, in commercial cells only the cell voltageand not the anode potential vs. (Li/Li + ) can be measured. The reason is that commercial cells do not contain a third reference electrode.Measurements of E anode in full cells would allow detection of Li deposition on anodes (condition: E anode vs. (Li/Li + ) < 0 V). Indeed, by introducing an additional reference electrode, such as metallic Li, the electrode potentials are accessible. 8,12,14,20,22,23 We note that such measurements are also not possible in a halfcell configuration with e.g. either only a graphite anode vs. a Li counter electrode or only a NMC cathode vs. a Li counter electrode, since the interaction between anode and cathode is not taken into account. Instead, 3-electrode full cells with the following electrodes are required to detect Li deposition correctly: (i) anode (e.g. graphite), (ii) cathode (e.g. NMC), and (iii) reference electrode (e.g. metallic Li).Furthermore, it is well-known that the reference electrode has to be positioned near the anode in order to minimize the Ohmic drop and to measure its potential accurately. 24 For example, in aqueous z E-mail: thomas.waldmann@zsw-bw.de systems, this is achieved by a Luggin-Haber capillary. 24 Simulations by Dees et al. also showed that the best position for a reference electrode in Lithium-ion cells is between anode and cathode. 25 The same...

show abstract

supporting

confidence: 91%

mentioning

confidence: 99%

Interplay of Operational Parameters on Lithium Deposition in Lithium-Ion Cells: Systematic Measurements with Reconstructed 3-Electrode Pouch Full Cells

Waldmann

Hogg

Kasper

et al. 2016

J. Electrochem. Soc.

158

111

View full text Add to dashboard Cite

show abstract

“…By the aid of numerical modeling it is possible to simulate the corresponding effective properties, like effective conductivity. Although this combination of image analysis and numerical simulation allows a direct investigation of the relationship between well-defined microstructure characteristics and effective transport properties ( [6] to [9]), it is strongly limited by the high costs of tomographic 3D imaging.…”

Section: Introductionmentioning

confidence: 99%

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

Neumann¹,

Schmidt²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

View full text Add to dashboard Cite

Abstract. We are going to introduce the concept of stochastic 3D modeling of geometrically complex (disordered) microstructures as a tool for virtual materials testing, including applications to battery electrodes as well as to electrodes of fuel cells, and solar cells. Using stochastic 3D models, one can generate a large variety of stochastically simulated micrsotructures with little computational effort. These virtual microstructures can be used as data basis to elucidate microstructure-property relationships. In this way, for example, effective conductivity can be expressed by microstructural characteristics such as volume fraction, tortuosity (windedness of transport paths) and costrictivity (bottleneck criterion) of the considered material phase. In another recent simulation study, we analysed more than 8000 virtual microstructures for various microstructural scenarios. Using data mining techniques like artificial neural networks and random forests, we were able to accurately predict effective conductivities given microstructure properties like volume fraction, tortuosity and constrictivity.

show abstract

“…Therefore, it is important to accurately predict the cell electrochemical behavior and terminal voltage at such conditions, as performing fast charging procedures at low temperatures could severely shorten the cycle life due to lithium deposition on the anode. 1,3,4 Electrochemical battery models based on first principles have shown the ability to accurately predict the concentration dynamics and terminal voltage. [5][6][7][8][9][10][11] In particular, the pseudo two-dimensional model (or the P2D model) based on Porous Electrode Theory 5 and Single Particle Model 12 (SPM) have been widely used for modeling the dynamic response of Lithium ion cells to variable input current profiles.…”

mentioning

confidence: 99%

Modeling of Li-Ion Cells for Fast Simulation of High C-Rate and Low Temperature Operations

Fan

Pan

Canova

et al. 2016

J. Electrochem. Soc.

View full text Add to dashboard Cite

Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentrationdependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations. Lithium ion batteries are considered the state of the art for energy storage in electric and hybrid vehicles. When batteries operates at low temperature conditions, for instance during a cold start of an electric vehicle, the highly reduced ionic conductivity and diffusivity lead to the formation of large gradients in the electrolyte concentration within the cell domain.1,2 Large concentration gradients can also be established at high C-rate conditions, which typically occur when the battery is subject to fast charge/discharge current loads in cold weather. Therefore, it is important to accurately predict the cell electrochemical behavior and terminal voltage at such conditions, as performing fast charging procedures at low temperatures could severely shorten the cycle life due to lithium deposition on the anode. 1,3,4 Electrochemical battery models based on first principles have shown the ability to accurately predict the concentration dynamics and terminal voltage. [5][6][7][8][9][10][11] In particular, the pseudo two-dimensional model (or the P2D model) based on Porous Electrode Theory 5 and Single Particle Model 12 (SPM) have been widely used for modeling the dynamic response of Lithium ion cells to variable input current profiles. Recently, Extended Single Particle Models (ESPM) [13][14][15][16][17] incorporating the electrolyte dynamics have been developed to capture the cell behavior under high C-rates conditions, albeit with simpler mathematical structure than P2D models.While electrochemical models generally predict well the cell terminal voltage as function of current and temperature, they are characterized by the presence of coupled PDEs and nonlinear algebraic equations, increasing the mathematical complexity and leading to computational challenges when applied to fast simulation and to estimation or control desig...

show abstract

Low-temperature charging of lithium-ion cells part I: Electrochemical modeling and experimental investigation of degradation behavior

Cited by 265 publications

References 31 publications

Interplay of Operational Parameters on Lithium Deposition in Lithium-Ion Cells: Systematic Measurements with Reconstructed 3-Electrode Pouch Full Cells

Interplay of Operational Parameters on Lithium Deposition in Lithium-Ion Cells: Systematic Measurements with Reconstructed 3-Electrode Pouch Full Cells

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

Modeling of Li-Ion Cells for Fast Simulation of High C-Rate and Low Temperature Operations

Contact Info

Product

Resources

About