Modeling Lithium Intercalation of a Single Spinel Particle under Potentiodynamic Control

Zhang, Dong; Popov, Branko N.; White, Ralph E.

doi:10.1149/1.1393279

Cited by 173 publications

(129 citation statements)

References 23 publications

(57 reference statements)

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“…For the latter, both decoupled [35] and coupled [37][38][39] numerical solutions are available. Importantly, the error induced by decoupling approximation is shown to be proportional to the squire of the molar expansion tensor and generally does not exceed 30% [38], well below the uncertainty of tip-surface contact radius in a typical SPM experiment.…”

Section: Mechanical Displacement Caused By Ionic Diffusionmentioning

confidence: 99%

Local probing of ionic diffusion by electrochemical strain microscopy: Spatial resolution and signal formation mechanisms

Morozovska

Eliseev

Balke

et al. 2010

Journal of Applied Physics

146

180

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Section: Mechanical Displacement Caused By Ionic Diffusionmentioning

confidence: 99%

Local probing of ionic diffusion by electrochemical strain microscopy: Spatial resolution and signal formation mechanisms

Morozovska

Eliseev

Balke

et al. 2010

Journal of Applied Physics

146

180

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“…Based on electrochemical kinetics and transport equations they can simulate cell characteristics and intercalation as well as side reactions. 19,20 The best-known electrochemistrybased models are the pseudo two-dimensional (P2D) model developed by Newman and co-workers [28][29][30] and the single particle model (SPM) first introduced by Zhang et al 31 The often proved accuracy and agreement with experimental data of the P2D model originate from its basic implementation of porous electrode theory as well as concentrated solution theory. 28,32 Up to today, the P2D model represents the most precise and -though computationally costly -most popular model in lithium-ion battery research.…”

mentioning

confidence: 99%

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Kindermann

Keil

Frank

et al. 2017

J. Electrochem. Soc.

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In this paper we introduce a pseudo two-dimensional (P2D) model for a common lithium-nickel-cobalt-manganese-oxide versus graphite (NCM/graphite) cell with solid electrolyte interphase (SEI) growth as the dominating capacity fade mechanism on the anode and active material dissolution as the main aging mechanism on the cathode. The SEI implementation considers a growth due to non-ideal insulation properties during calendar as well as cyclic aging and a re-formation after cyclic cracking of the layer during graphite expansion. Additionally, our approach distinguishes between an electronic (σ SEI ) and an ionic (κ SEI ) conductivity of the SEI. This approach introduces the possibility to adapt the model to capacity as well as power fade. Simulation data show good agreement with an experimental aging study for NCM/graphite cells at different temperatures introduced in literature. © The Author Lithium-ion batteries are one of the most promising candidates for energy storage in future stationary storage systems and electric vehicles.1-3 Enormous research efforts have been conducted to get a thorough understanding of the system "lithium-ion cell" and to further develop it for higher energy and power density, higher safety standards as well as longer cycle life. 4 The aging behavior of lithium-ion batteries has been a focus issue of battery research since the introduction of lithium-ion cells by Sony in 1991. 5 Reviews by Agubra et al., 6,7 Arora et al., 8 Aurbach et al., 9,10 Birkl et al., 11 Broussely et al., 12 Verma et al. 13 and Vetter et al. 14 are just a few examples of the extensive literature regarding aging behavior. Commonly accepted and experimentally verified aging phenomena as mentioned in the previously cited literature are electrolyte decomposition leading to solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) growth, solvent co-intercalation, gas evolution with subsequent cracking of particles, a decrease of accessible surface area and porosity due to SEI growth, contact loss of active material particles due to volume changes during cycling, binder decomposition, current collector corrosion, metallic lithium plating and transition-metal dissolution from the cathode. The listed aging mechanisms can be assigned to three different categories that are a loss of lithium-ions (LLI), an impedance increase and a loss of active material (LAM). 12,[15][16][17][18] The LLI is synonymous to a decrease in the amount of cyclable lithium-ions as they are trapped in a passivating film on either of the electrodes or in plated metallic lithium. Due to the growth of the passivating layers and/or the formation of rock-salt in the cathode (residue of the cathode active material after transition-metal dissolution), kinetic transport of lithium-ions through those inactive areas is limited and results in an impedance rise. An LAM can be caused by the dissolution of transition-metal-ions from the cathode bulk material, changes in the electrode composition and/or changes in crystal structure of the a...

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“…Second, variations of the electrolyte concentration and potential are ignored. 30 Equations 1 and 5 of Table II are the governing equations of the SPM. These equations comprise the solid-state concentration and the Butler-Volmer kinetics equations at both negative and positive electrodes.…”

Section: Direct Models Of Li-ion Batteriesmentioning

confidence: 99%

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Jokar

Rajabloo

Désilets

et al. 2016

J. Electrochem. Soc.

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An electrochemical Parameter Estimation (PE) study of lithium-ion batteries for different materials is presented. The PE methodology is developed in Part I of the study and the challenges on the different materials for the positive electrode including LiCoO 2 , LiMn 2 O 4 and LiFePO 4 are examined in Part II. The most influential electrochemical parameters of the Li-ion battery are estimated by means of an inverse method. The inverse method rests on five elements: the input parameters, a direct model, the reference data, an objective function and an optimizer. Eight electrochemical variables are considered as the target of the PE study. A simplified version of Pseudo-two-Dimensional (P2D) model is developed for the direct model. The P2D model predictions coupled to a random noise function are employed to generate the reference data. The data include the cell potential values with respect to the battery capacity at low and high discharge rates. The least-squared function and Genetic Algorithm are employed as the objective function and its optimizer, respectively. The best time domain for the estimation of each parameter is calculated by using a sensitivity analysis performed for different discharge curves. Results show that the methodology remains accurate and stable at both low and high discharge rates. The simultaneous high power and energy density of Lithium-ion (Li-ion) batteries have made it the preferred device for storing electricity. As a result, Li-ion batteries are increasingly used in various applications including electronics and the automotive industry. Regardless of the shape and of the battery pack arrangement, the internal structure of the battery usually comprises four main components: Two electrodes, positive and negative, an electrolyte, and a separator. These components are made of materials that have been gradually modified over the years so as to improve the efficiency, the safety and the performance of the batteries and to reduce their cost. 1-3A Battery Management System (BMS) is crucial for monitoring the operation of the battery pack. The BMS must interact with all the elements of the system in order to control it and to protect the Li-ion cells. The intelligence of the BMS is based on a mathematical model that simulates and predicts the different operating conditions of the Li-ion battery pack. In high tech and automotive industries, the BMS usually relies on empirical-based models. These models are simple and provide fast response. They cannot however predict the performance of the battery as it ages. Moreover, they are only applicable to a specific cell, i.e., they cannot be transposed to other battery packs without recalibration. [4][5][6] Electrochemical-based models of Li-ion batteries, on the other hand, overcome these shortcomings. These models rest on chemical/electrochemical kinetics and transport equations. These Li-ion battery models are more complicated and CPU time-consuming than empirical based models. They are, on the other hand, more versatile and they provide reliable a...

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Modeling Lithium Intercalation of a Single Spinel Particle under Potentiodynamic Control

Cited by 173 publications

References 23 publications

Local probing of ionic diffusion by electrochemical strain microscopy: Spatial resolution and signal formation mechanisms

Local probing of ionic diffusion by electrochemical strain microscopy: Spatial resolution and signal formation mechanisms

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

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