Pseudo 3D Modeling and Analysis of the SEI Growth Distribution in Large Format Li-Ion Polymer Pouch Cells

107

An advanced model is proposed, describing the capacity losses of C 6 /LiFePO 4 batteries under storage and cycling conditions. These capacity losses are attributed to the growth of a Solid Electrolyte Interface (SEI) at the surface of graphite particles in the negative electrode. The model assumes the existence of an inner and outer SEI layer. The rate determining step is considered to be electron tunneling through the inner SEI layer. The inner SEI layer grows much slower than the outer SEI layer. Another contribution to the degradation process is the exfoliation of SEI near the edges of graphite particles during discharging and the formation of new SEI induced by the volumetric changes during the subsequent charging. The model has been validated by storage and cycling experiments. The simulation results show that the capacity losses are dependent on the State-of-Charge (SoC), the storage time, cycle number and graphite particle size. The model can be used to predict both the calendar and cycling life of the Li-ion batteries. Li-ion batteries, based on the C 6 /LiFePO 4 chemistry, attracts nowadays much attention for application in, for example, electric vehicles due to the excellent cycling stability of the LiFePO 4 electrode. The main electrochemical storage reactions of this battery type can be represented byDuring charging electrons and Li + ions are extracted from the LiFePO 4 (0 ≤ x ≤ 1) electrode and flow into the graphite electrode. The reverse reactions take place during discharging. The structure of LiFePO 4 (LFP) is highly robust due to the strong bonding between the P and O atoms. LFP batteries therefore combine a good cycling stability with excellent thermal stability and long lifespan.1-5 The volume decreases only 6.8% 6 when all Li ( x = 1) is extracted and these small volumetric changes do not influence the structure of LiFePO 4 and FePO 4 . Favorably, it almost fully counterbalances the volumetric changes of the graphite electrode during Li (de)insertion.Due to these favorable properties, the capacity losses caused by the LFP cathode are generally assumed to be negligible. Furthermore, Broussely et al. showed that the capacity of the graphite electrode can be fully recovered even after more than 1 year storage under 'floating' conditions at 60• C, demonstrating that the stability of the carbon electrode is also excellent.7 However, capacity losses are still found in LFP batteries. It is generally accepted, that the formation of a Solid Electrolyte Interface (SEI) is one of the main reasons for the capacity losses of graphite-based Li-ion batteries, especially under moderate conditions during the initial stages of aging.The SEI plays a dual-role in the performance of Li-ion batteries. On the one hand, it protects the negative electrode from solvent cointercalation, preventing exfoliation of the graphene layers. On the other hand, it consumes cyclable lithium inside the battery, which is therefore no longer available for the energy storage process and hence * Electrochemical Society Active Member...

mentioning

confidence: 99%

Modeling the SEI-Formation on Graphite Electrodes in LiFePO₄Batteries

Danilov

Zhang

et al. 2015

107

“…The expression of open-circuitpotential (OCP) for NMC has been adopted from Awarke et al Ref. 69 Because damage evolution inside anode is being analyzed here, two different OCP curves have been taken into consideration, which correspond to two different anode materials: (i) Hard carbon, and (ii) Graphite. The OCP of hard-carbon has been adopted from Gu and Wang (see Ref.…”

Section: Resultsmentioning

confidence: 99%

Reduced Order Modeling of Mechanical Degradation Induced Performance Decay in Lithium-Ion Battery Porous Electrodes

Barai

Smith

Chen

et al. 2015

A one-dimensional computational framework is developed that can solve for the evolution of voltage and current in a lithium-ion battery electrode under different operating conditions. A reduced order model is specifically constructed to predict the growth of mechanical degradation within the active particles of the carbon anode as a function of particle size and C-rate. Using an effective diffusivity relation, the impact of microcracks on the diffusivity of the active particles has been captured. Reduction in capacity due to formation of microcracks within the negative electrode under different operating conditions (constant current discharge and constant current constant voltage charge) has been investigated. At the beginning of constant current discharge, mechanical damage to electrode particles predominantly occurs near the separator. As the reaction front shifts, mechanical damage spreads across the thickness of the negative electrode and becomes relatively uniform under multiple discharge/charge cycles. Mechanical degradation under different drive cycle conditions has been explored. It is observed that electrodes with larger particle sizes are prone to capacity fade due to microcrack formation. Under drive cycle conditions, small particles close to the separator and large particles close to the current collector can help in reducing the capacity fade due to mechanical degradation. Due to their high energy and power density, lithium-ion batteries (LIBs) are being used extensively in the electrification of the automotive industry through the development of electric and hybrid electric vehicles (EVs and HEVs).1-3 Several mechanisms exist that can cause a reduction in the capacity of LIBs and subsequent loss of life. [4][5][6][7] Growth of a solid electrolyte interface (SEI) layer on the carbon active particles of the anode is the major reason behind the loss of cyclable lithium ions. [8][9][10] Lithium plating at low temperatures also results in loss of lithium and subsequent capacity fade.11 Delamination of the current collector from the electrode due to gas evolution in the electrolyte can significantly increase the internal resistance of the lithium-ion cell. 12 Crack propagation, rupture, and isolation of portions of active particles can also cause loss of active sites where lithium atoms can intercalate, resulting in effective capacity fade. 13 In the past two to three decades, capacity fade due to the formation of SEI 8,10,[14][15][16][17] and lithium plating 18,19 have been investigated thoroughly. On the other hand, resistance growth and capacity fade due to delamination and site loss have not been explored extensively. In the recent past, some research initiatives have focused on characterizing the generation of diffusion-induced stress within the active particles. 20 A computational methodology was developed to capture the formation of cracks based on the material heterogeneity of the anode active particles. 21 In the present article, the authors have developed a comprehensive reduced order model (RO...

“…1c and 1d) predicts the distribution of the electric potential across the current collectors and the temperature distribution across the cell surface. Due to the small thickness of the cell, the variations of temperature in the x direction are neglected, 1,2,5,17,18 and therefore, the distributed temperature is only defined in the y − z surface plane.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…where 1 represents the boundary at the top of each tab, 2 denotes all the other boundaries of the current collector except 1 , n denotes the unit normal vector pointing out of the boundary, N layer is the number of the jelly roll layers in the battery, and A cs represents the cross sectional area at the top surface of the tab.…”

Section: Parametermentioning

confidence: 99%

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

Fan

Pan

Storti

et al. 2016

In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. 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. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated. Temperature and temperature gradients are some of the key factors that influence the performance and degradation of Li-ion batteries. Despite in Hybrid Electric Vehicles (HEVs) or Electric Vehicles (EVs) the average pack temperature is regulated by the thermal management systems, thermal gradients may still occur within the pack or across individual cells.1-3 For instance, when cooling plates are in contact with only one side of the cells, the heat generated inside each cell will be dissipated to the cooling plates along a preferential direction. Therefore, thermal gradients in the order of ±5• C are common, and particularly in high-power packs for HEVs or high-performance EVs. 4,5 Thermal gradients on the cell surface are particularly critical in large-format batteries because hot spots may induce localized changes in the physical and chemical properties of the electrodes and electrolyte solution, which in turn can increase the local transverse current density and ultimately cause non-uniform degradation of the electrodes.6,7 Multiscale characterization methods have in fact evidenced that the non-uniform utilization of electrode surface leads to highly uneven degradation. 8,9 Furthermore, cell-to-cell variations in large battery packs (due to packaging constraints) may lead to non-uniform heat transfer in packs and induce further thermal gradients among the various cells. 10,11 This motivates the development of procedures and tools that allow for characterization and prediction of thermal gradients in large-format Lithium ion batteries, and understanding their impact on the performance and durability.Multi-Scale, Multi-Dimensional (MSMD) modeling approaches have been proposed to simulate the distribution of surface temperature and Lithium concentration in large format, prismatic cells. As a first approximation, the rate of heat generation within the cell can be approximated as uniform across the surface.12-16 More recently, this simplification has been abandoned in favor of more rigorous approaches that accurately compute the local heat generation rate ...