Quantitative validation of calendar aging models for lithium-ion batteries

Hahn, Severin; Storch, Mathias; Swaminathan, Ramanathan; Obry, Björn; Bandlow, Jochen; Birke, Kai Peter

doi:10.1016/j.jpowsour.2018.08.019

Cited by 61 publications

(56 citation statements)

References 41 publications

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“…During battery operation, however, the external conditions, for example, charging rate and depth of discharge, strongly influence the SEI growth rate. The resulting capacity fade was analyzed in several papers with empirical formulas . These approaches nicely agree with experimental measurements but do not give further insights into underlying growth mechanisms.…”

Section: Introductionmentioning

confidence: 60%

“…The resulting capacity fade was analyzed in severalp apers with empirical formulas. [49][50][51][52][53][54][55] These approaches nicely agree with experimental measurements but do not give further insights into underlying growth mechanisms. Physics-based modelsf or SEI growth duringb attery operation remains carcea nd rely on solvent diffusion, [56] electron conduction, [44] or electron tunneling [45] as charge-transport mechanism.…”

Section: Introductionmentioning

confidence: 64%

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Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes

2020

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The capacity fade of modern lithium ion batteries is mainly caused by the formation and growth of the solid–electrolyte interphase (SEI). Numerous continuum models support its understanding and mitigation by studying SEI growth during battery storage. However, only a few electrochemical models discuss SEI growth during battery operation. In this article, a continuum model is developed that consistently captures the influence of open‐circuit potential, current direction, current magnitude, and cycle number on the growth of the SEI. The model is based on the formation and diffusion of neutral lithium atoms, which carry electrons through the SEI. Recent short‐ and long‐term experiments provide validation for our model. SEI growth is limited by either reaction, diffusion, or migration. For the first time, the transition between these mechanisms is modelled. Thereby, an explanation is provided for the fading of capacity with time t of the form t β with the scaling coefficent β , 0≤ β ≤1. Based on the model, critical operation conditions accelerating SEI growth are identified.

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 64%

Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes

2020

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“…During battery cycling, the degradation rate is influenced by C-rate, cycle depth effect (DoD), and temperature [59]. In storage condition, the degradation rate is influenced by temperature, idle time and SoC [60]. The purpose of battery degradation models, therefore, is to predict the battery lifetime considering these influencing parameters.…”

Section: Degradation Modelsmentioning

confidence: 99%

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

et al. 2020

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Increased deployment of intermittent renewable energy plants raises concerns about energy security and energy affordability. Capacity markets (CMs) have been implemented to provide investment stability to generators and secure energy generation by reducing the number of shortage hours. The research presented in this paper contributes to answering the question of whether batteries can provide cost effective back up services for one year in this market. The analysis uses an equivalent circuit lithium ion battery model coupled with two degradation models (empirical and semi-empirical) to account for capacity fade during battery lifetime. Depending on the battery’s output power, four de-rating factors of 0.5 h, 1 h, 2 h and 4 h are considered to study which de-rating strategy can result in best economic profit. Two scenarios for the number of shortage hours per year in the CM are predicted based on the energy demand data of Great Britain and recent research. Results show that the estimated battery profit is maximum with 2 h and 1 h de-rating factors and minimum with 4 h and 0.5 h. Depending on the battery degradation model used, battery degradation cost can considerably impact the potential profit if the battery’s temperature is not controlled with adequate thermal management system. The empirical and semi-empirical models predict that the degradation cost is minimum at 5 °C and 25 °C respectively. Moreover, both models predict degradation is minimum at lower battery charge levels. While the battery’s capacity fade can be minimized to make some profits from the CM service, the increased shortage hours can make providing this service not economically viable.

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“…Therefore, the characterization methods incremental capacity (IC), differential voltage (DV), maximum/minimum electrode potentials, electrochemical impedance spectroscopy (EIS), and pulse current measurements (PCM) are used and introduced in Section 2. After the experimental part in Section 3, Section 4 compares the 1C cyclic aging behavior of commercially manufactured 40 Ah Li-ion pouch cells to their extracted coin cells at the three specified temperatures T ∈ [45,35,25] • C. In case of cycling at 1C, an aggravated capacity loss due to an inhomogeneous temperature distribution inside the 40 Ah cells can be neglected [34]. Finally, the aging mechanisms of both cell types are estimated.…”

Section: Introductionmentioning

confidence: 99%

Aging of Extracted and Reassembled Li-ion Electrode Material in Coin Cells—Capabilities and Limitations

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Hahn³

et al. 2020

Batteries

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Cycling Li-ion cells with large capacities requires high currents and hence an expensive measurement setup. Aging the Li-ion cell material in coin cells offers an orders-of-magnitude-lower power requirement to the battery tester. The preparation procedure used in this work allows one to build coin cells in a reproducible manner. The original 40 Ah pouch cells and the corresponding 4.3 mAh coin cells (PAT-Cell) utilizing electrode material from the original cells are cycled with 1C at different temperatures. The results show the same basic aging mechanisms in both cell types: loss of lithium inventory at room temperature but an increasing proportion of loss of active material toward higher temperatures. This is confirmed by similar activation energies in capacity degradation of the 40 Ah cells and the averaged coin cells. However, the capacity of the coin cells decreases faster over time. This is caused by diffusion of moisture into the coin cell housing. Nonetheless, the increasing water contamination over measurement time is not directly linked to the loss of capacity of the coin cells. Thus, the observed aging mechanisms of the 40 Ah cells can be qualitatively transferred to coin cell level.

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Quantitative validation of calendar aging models for lithium-ion batteries

Cited by 61 publications

References 41 publications

Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes

Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

Aging of Extracted and Reassembled Li-ion Electrode Material in Coin Cells—Capabilities and Limitations

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