Investigation of the temperature and DOD effect on the performance-degradation behavior of lithium–sulfur pouch cells during calendar aging

Capková, Dominika; Knap, Václav; Fedorková, Andrea Straková; Stroe, Daniel‐Ioan

doi:10.1016/j.apenergy.2022.120543

Cited by 14 publications

(3 citation statements)

References 94 publications

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“…Individual elements of the equivalent circuit of the prepared cathode have the following meanings. R e represents the electrolyte resistance, R1 is the resistance of the surface layers of the sulfur and lithium electrode, CPE1 is the capacitance of the surface layers, R2 is the charge transfer resistance, CPE2 is the double layer capacitance, and W is the Warburg impedance 65 , 66 . Before cycling, the prepared S/STAM-1/Super P/PVDF cathode shows higher charge-transfer resistance ( R ct = 26.27 Ω) than after 50 cycles at 0.5 C ( R ct = 11.03 Ω).…”

Section: Resultsmentioning

confidence: 99%

Novel Cu(II)-based metal–organic framework STAM-1 as a sulfur host for Li–S batteries

Niščáková,

Almáši,

Capková

et al. 2024

Sci Rep

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Due to the increasing demand for energy storage devices, the development of high-energy density batteries is very necessary. Lithium–sulfur (Li–S) batteries have gained wide interest due to their particularly high-energy density. However, even this type of battery still needs to be improved. Novel Cu(II)-based metal–organic framework STAM-1 was synthesized and applied as a composite cathode material as a sulfur host in the lithium–sulfur battery with the aim of regulating the redox kinetics of sulfur cathodes. Prepared STAM-1 was characterized by infrared spectroscopy at ambient temperature and after in-situ heating, elemental analysis, X-ray photoelectron spectroscopy and textural properties by nitrogen and carbon dioxide adsorption at − 196 and 0 °C, respectively. Results of the SEM showed that crystals of STAM-1 created a flake-like structure, the surface was uniform and porous enough for electrolyte and sulfur infiltration. Subsequently, STAM-1 was used as a sulfur carrier in the cathode construction of a Li–S battery. The charge/discharge measurements of the novel S/STAM-1/Super P/PVDF cathode demonstrated the initial discharge capacity of 452 mAh g−1 at 0.5 C and after 100 cycles of 430 mAh g−1, with Coulombic efficiency of 97% during the whole cycling procedure at 0.5 C. It was confirmed that novel Cu-based STAM-1 flakes could accelerate the conversion of sulfur species in the cathode material.

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Section: Resultsmentioning

confidence: 99%

Novel Cu(II)-based metal–organic framework STAM-1 as a sulfur host for Li–S batteries

Niščáková,

Almáši,

Capková

et al. 2024

Sci Rep

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“…First, sulfur electrodes suffer high volume expansion of up to 80% during cycling, resulting in significant structural damage . Second, sulfur has low electronic conductivity, resulting in low utilization of active materials and sluggish kinetics . Third, the formation of polysulfides (Li 2 S x , x = 4–8) results in fast capacity decay, largely irreversible sulfur loss, and low coulombic efficiency during the charge/discharge process. , Hence, to overcome these issues, various strategies have been developed to restrict sulfur within porous carbon hosts, such as etched carbon nanotubes, N-doped carbon materials, metal sulfides, metal oxides, interlayer, and novel binders .…”

Section: Introductionmentioning

confidence: 99%

Low-Cost and Sustainable Cross-Linked Polyvinyl Alcohol–Tartaric Acid Composite Binder for High-Performance Lithium–Sulfur Batteries

Reddy

Ahn

et al. 2023

ACS Appl. Energy Mater.

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A high specific energy density in lithium–sulfur (Li-S) batteries can be achieved by constructing high-sulfur-loading electrodes. However, this electrode type leads to fast capacity decay owing to the formation of large cracks on its surface. Consequently, the optimization of polymeric binders is considered an effective strategy to decrease the volume expansion of sulfur electrodes during cycling. In this study, a three-dimensional (3D) cross-linked polymeric binder was prepared via the polymerization of polyvinyl alcohol (PVA) and tartaric acid (TA). Acetylene carbon black (C) and elemental sulfur (S) were mixed and melt-diffused to prepare a CS composite. The developed 3D cross-linked PVA-TA composite binder films exhibited improved breaking strengths and swelling degrees. Moreover, the inter/intramolecular reactions between the hydroxyl groups of the PVA and TA molecules effectively reduced expansion and suppressed lithium polysulfides (LiPSs) during cycling. The synthesized composite binder also improved the adhesion of the active material and mechanical properties, which decreased the self-discharge and overpotential polarization and increased the lithium-ion diffusion coefficient. The CS electrode (78% sulfur, 3.3–3.5 mg/cm2 sulfur loading) with PVA-TA composite binder exhibited a high initial discharge specific capacity of 537.7 mAh/g and maintained 463.99 mAh/g after 400 cycles at 1.0 C-rate with the decay rate of 0.14%. The outstanding electrochemical performance is due to the 3D cross-linked polymeric network and the excellent adsorption ability. To the best of our knowledge, a sulfur electrode with low-cost and sustainable 3D cross-linked PVA-TA composite binder with excellent electrochemical properties was used for the first time in Li-S batteries.

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“…Lithium-ion batteries (LIBs), responsible for energy storage and supply, are core components of electric vehicles, and their performance and health state have significant impacts on the reliability and safety of electric vehicles [5][6][7]. Although LIBs have long lifespans, they experience varying degrees of aging and degradation with increasing usage time and cycles [8][9][10]. Therefore, accurately assessing and predicting the state-of-health (SOH) of batteries has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

Liu,

Wang

et al. 2023

Sustainability

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The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.

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Investigation of the temperature and DOD effect on the performance-degradation behavior of lithium–sulfur pouch cells during calendar aging

Cited by 14 publications

References 94 publications

Novel Cu(II)-based metal–organic framework STAM-1 as a sulfur host for Li–S batteries

Novel Cu(II)-based metal–organic framework STAM-1 as a sulfur host for Li–S batteries

Low-Cost and Sustainable Cross-Linked Polyvinyl Alcohol–Tartaric Acid Composite Binder for High-Performance Lithium–Sulfur Batteries

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

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