Electrical and Structural Characterization of Large‐Format Lithium Iron Phosphate Cells Used in Home‐Storage Systems

Yagci, Mehmet C.; Behmann, René; Daubert, Viktor; Braun, Jonas A.; Velten, Dirk; Bessler, Wolfgang G.

doi:10.1002/ente.202000911

Cited by 12 publications

(22 citation statements)

References 33 publications

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“…The estimated coefficients k P and k N are reported in Table 3. Their values are in good agreement with physical electrode thickness and active material fractions measured by other authors [30,[44][45][46][47] in LFP/graphite cells. The result of the fitted model and the experimental measurements are presented in Figure 11.…”

Section: Comparison Of Model With Experimental Measurementssupporting

confidence: 91%

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Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Clerici¹,

Mocera²,

Somà³

2021

Energies

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The characterization of thickness change during operation of LFP/Graphite prismatic batteries is presented in this work. In this regard, current rate dependence, hysteresis behaviour between charge and discharge and correlation with phase changes are deepened. Experimental tests are carried out with a battery testing equipment correlated with optical laser sensors to evaluate swelling. Furthermore, thickness change is computed analytically with a mathematical model based on lattice parameters of the crystal structures of active materials. The results of the model are validated with experimental data. Thickness change is able to capture variations of the internal structure of the battery, referred to as phase change, characteristic of a certain state of charge. Furthermore, phase change shift is a characteristic of battery ageing. Being able to capture these properties with sensors mounted on the external surface the cell is a key feature for improving state of charge and state of health estimation in battery management system.

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Section: Comparison Of Model With Experimental Measurementssupporting

confidence: 91%

“…Results of least mean square estimation and electrode properties from in situ measurements[30,[44][45][46][47].…”

mentioning

confidence: 99%

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Clerici¹,

Mocera²,

Somà³

2021

Energies

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“…The cell was investigated experimentally under a controlled laboratory environment (climate chamber CTS 40/200 Li) using a battery cycler with fourwire measurement (Biologic VMP3). Details on the cell and characterisation methods can be found in our previous publication [18]. Here we carried out additional measurements for GB model parameterisation and testing.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we introduced a second learnable parameter to represent the capacitance C 1 . As we wanted to take into account that the charge-transfer resistance may have different values and characteristics during charging and discharging (as observed experimentally [18]), R 1 is described by two learnable functions. Depending on the sign of the battery current, one of these functions is chosen; at zero current (i bat = 0 A) the mean is taken.…”

Section: Grey-box Modelmentioning

confidence: 99%

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Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model

et al. 2022

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Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

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Closed-loop recycling of lithium iron phosphate cathodic powders via citric acid leaching

Bruno,

Francia,

Fiore

2024

Environ Sci Pollut Res

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Lithium recovery from Lithium-ion batteries requires hydrometallurgy but up-to-date technologies aren’t economically viable for Lithium-Iron-Phosphate (LFP) batteries. Selective leaching (specifically targeting Lithium and based on mild organic acids and low temperatures) is attracting attention because of decreased environmental impacts compared to conventional hydrometallurgy. This study analysed the technical and economic performances of selective leaching with 6%vv. H2O2 and citric acid (0.25-1 M, 25 °C, 1 h, 70 g/l) compared with conventional leaching with an inorganic acid (H2SO4 1 M, 40 °C, 2 h, 50 g/l) and an organic acid (citric acid 1 M, 25 °C, 1 h, 70 g/l) to recycle end of life LFP cathodes. After conventional leaching, chemical precipitation allowed to recover in multiple steps Li, Fe and P salts, while selective leaching allowed to recover Fe and P, in the leaching residues and required chemical precipitation only for lithium recovery. Conventional leaching with 1 M acids achieved leaching efficiencies equal to 95 ± 2% for Li, 98 ± 8% for Fe, 96 ± 3% for P with sulfuric acid and 83 ± 0.8% for Li, 8 ± 1% for Fe, 12 ± 5% for P with citric acid. Decreasing citric acid’s concentration from 1 to 0.25 M didn’t substantially change leaching efficiency. Selective leaching with citric acid has higher recovery efficiency (82 ± 6% for Fe, 74 ± 8% for P, 29 ± 5% for Li) than conventional leaching with sulfuric acid (69 ± 15% for Fe, 70 ± 18% for P, and 21 ± 2% for Li). Also, impurities’ amounts were lower with citric acid (335 ± 19 335 ± 19 of S mg/kg of S) than with sulfuric acid (8104 ± 2403 mg/kg of S). In overall, the operative costs associated to 0.25 M citric acid route (3.17€/kg) were lower compared to 1 M sulfuric acid (3.52€/kg). In conclusion, citric acid could be a viable option to lower LFP batteries’ recycling costs, and it should be further explored prioritizing Lithium recovery and purity of recovered materials.

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Electrical and Structural Characterization of Large‐Format Lithium Iron Phosphate Cells Used in Home‐Storage Systems

Cited by 12 publications

References 33 publications

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model

Closed-loop recycling of lithium iron phosphate cathodic powders via citric acid leaching

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