Postmortem analysis of calendar-aged graphite/LiFePO4 cells

Kassem, Mohammad; Delacourt, Charles

doi:10.1016/j.jpowsour.2013.01.147

Cited by 136 publications

(84 citation statements)

References 26 publications

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“…Stock data was used for the NE (taken from [22], cycled between 0.01 V to 1.2 V). Experimental validation supporting our simulation results based on loss of lithium inventory (LLI) and loss of active material (LAM) degradation modes has been reported by other groups [30][31][32].…”

Section: Half-cell Preparation and Testingsupporting

confidence: 87%

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Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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Abstract:The use of lithium batteries for power and energy-hungry applications has risen drastically in recent years. For such applications, it is necessary to connect the batteries in large assemblies of cells in series and parallel. With a large number of cells operating together, it is necessary to understand their intrinsic variabilities, not only at the initial stage but also upon aging. In this study, we studied a batch of commercial cells to address their initial cell-to-cell variations and also the variations induced by cycling. To do so, we not only tracked several metrics associated with cell performance, the maximum capacity, the resistance, and the rate capability but also the degradation mechanism via a non-invasive quantification of the loss of lithium inventory (LLI), the loss of active material (LAM) and the kinetic degradation on both electrodes. We found that, even with small initial cell-to-cell variations, significant variations will be observed upon aging because the cells degrade at a different pace. We also observed that these variations were not correlated with the initial variations.

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Section: Half-cell Preparation and Testingsupporting

confidence: 87%

“…Energies 2018, 11, x 4 of 14 lithium inventory (LLI) and loss of active material (LAM) degradation modes has been reported by other groups [30][31][32].…”

Section: Initial Characterizationmentioning

confidence: 91%

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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“…5 Some experimental validation of the 'alawa simulations for cell aging has been reported by other groups recently. 19,20 The a anakonu is the Hawaiian word for "equilibrium." b 'alawa is the Hawaiian word for "to diagnose."…”

Section: Principlesmentioning

confidence: 99%

“…5 An example is illustrated below to highlight the significance of this unique aspect. Calendar aging is known to introduce loss of lithium inventory (LLI), 19,21 and the 'alawa toolbox could be used to simulate various degrees of LLI in single cells. 5 Based on the 'alawa simulations, an example of SCi OCV = f ( SCi SOC) variations for a G||NMC cell that has undergone up to 20% LLI is shown in Figure 6.…”

Section: Battery Packs With Identical Cells But Different Initial Socs-mentioning

confidence: 99%

State-of-Charge Determination in Lithium-Ion Battery Packs Based on Two-Point Measurements in Life

Dubarry

Truchot

Devie

et al. 2015

J. Electrochem. Soc.

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The state-of-charge (SOC) estimation is of extreme importance for the reliability and safety of battery operation. How to estimate SOC for an assembly of cells in a battery pack remains a subject of great interest. Here a viable method for SOC determination and tracking for multi-cell assemblies is proposed and validated. Using 3S1P (three in series and one in parallel) strings as an example, an inference of SOC is illustrated in a battery assembly based on a correct open pack voltage (OPV) versus SOC (i.e. OPV = f (SOC)) function. The proposed method only requires the measurements of the rest cell voltages of the single cells at two distinct occasions. Rechargeable lithium-ion batteries (LIB) continue being considered viable choices for mobile power and energy storage applications. Yet, a reliable deployment of LIB in powertrains remains very challenging, primarily due to the requirements for reliable multi-cell assemblies to provide high energy and power. Better capability to characterize battery pack performance, identify aging mechanism, and perform state-of-charge (SOC) estimation is desired to achieve great efficiency.1,2 In our previous work, we devoted substantial effort to understand the behavior of cells in a pack and the impact of cell variability on pack performance. 3,4 We also reported a diagnostic and prognostic approach to identify and quantify cell-aging mechanisms in the course of cycle aging for a number of cell chemistries.5-11 To enable these methodologies, the SOC determination is the most vital component among all for accurate operation of the battery management system (BMS).1 In our latest work, 12 we showed that the most accurate method to obtain the SOC of a battery pack is either by measuring the residual capacity (Q res ) against the maximum pack capacity (Q max ); i.e. pack SOC = Q res /Q max , or by measuring the rest pack voltage (RPV) to infer SOC based on a SOC versus open pack voltage (OPV) function; i.e. pack SOC = f -1 (OPV). Both methods, however, suffer from their inoperability in practical applications, due to (1) in the case of capacity measurements, the uncertainty related to Q res in a duty cycle, (2) the fade in the Q max over lifetime, and (3) in the case of voltage-based measurements, the need for accurate OPV across the full SOC range. Therefore, a simple and practical method for SOC estimation remains very desirable.For a single cell (SC), the open circuit voltage (OCV) versus SOC function is often preferred for SOC determination because in principle, and at the beginning-of-life (BOL) of the cell, the sc SOC = f -1 (OCV) function is universal for cells of the same chemistry, disregarding size or geometry.3 Therefore, this OCV = f(SOC) function only needs to be determined once and for all from a single cell of a specific design. Upon aging, uncertainties to this OCV = f(SOC) function are introduced due to aging-pathway dependence.6 Such variations could be predicted as a function of duty cycle characteristics using the mechanistic model reported in our prior work. 5...

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“…Therefore, most authors discharge the cells to the end-of-discharge voltage prior to disassembly corresponding to SOC = 0%. 12,16,17,28,30,34,46,69,70,72 A defined SOC is also important for comparability of the results of different cells, e.g. aged and fresh cells of the same type.…”

mentioning

confidence: 99%

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Waldmann

Iturrondobeitia

Kasper

et al. 2016

J. Electrochem. Soc.

214

149

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Improvement of life-time is an important issue in the development of Li-ion batteries. Aging mechanisms limiting the life-time can efficiently be characterized by physico-chemical analysis of aged cells with a variety of complementary methods. This study reviews the state-of-the-art literature on Post-Mortem analysis of Li-ion cells, including disassembly methodology as well as physicochemical characterization methods for battery materials. A detailed scheme for Post-Mortem analysis is deduced from literature, including pre-inspection, conditions and safe environment for disassembly of cells, as well as separation and post-processing of components. Special attention is paid to the characterization of aged materials including anodes, cathodes, separators, and electrolyte. More specifically, microscopy, chemical methods sensitive to electrode surfaces or to electrode bulk, and electrolyte analysis are reviewed in detail. The techniques are complemented by electrochemical measurements using reconstruction methods for electrodes built into half and full cells with reference electrode. The changes happening to the materials during aging as well as abilities of the reviewed analysis methods to observe them are critically discussed. Li-ion batteries are currently used in everyday objects such as smart-phones, power tools and tablet computers as well as in the growing fields of light electric vehicles (LEVs), unmanned aerial vehicles (UAVs), battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). [1][2][3][4] Furthermore, the rise of renewable energy sources like wind and solar power, which are only intermittently available, demands reliable and highly flexible stationary energy storage solutions, which provide high capacities and predictable life-times. 2,5Aging of Li-ion batteries is a general problem for manufacturers as they have to guarantee long-term reliability of their products. For state-of-the-art cells, degradation effects on the material level lead to capacity fade and resistance increase on the cell level. The aging state of a battery is often characterized by the state-of-health (SOH) in % according to 3,16,22,[29][30][31] SO H(t) = discharge capacity (t) discharge capacity (t = 0)[1]where t represents the aging time. In general, one has to differentiate between cycling 7,16,18,21,[23][24][25]32 and calendar aging. 7,19,[21][22][23][24]27 Since commercial Li-ion cells can be subject to calendar aging in the time between manufacturing and delivery, it is good practice to measure the discharge capacity at t = 0 for each cell that undergoes an aging test. Since the discharge capacity depends mainly on temperature, depth-of-discharge (DOD), and discharge current, the SOH is usually monitored by regular check-ups with defined parameter sets, 7,16,21,23,24 which can vary depending on the application. Typically, a temperature of 25• C, 16,22,24 DOD of 100%, 16,21 and discharge rates of 1C 7,16,21,22,24 or lower 23 are used in check-ups. The performance dec...

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Postmortem analysis of calendar-aged graphite/LiFePO4 cells

Cited by 136 publications

References 26 publications

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

State-of-Charge Determination in Lithium-Ion Battery Packs Based on Two-Point Measurements in Life

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

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