State of health estimation of cycle aged large format lithium-ion cells based on partial charging

Jenu, Samppa; Hentunen, Ari; Haavisto, Jari; Pihlatie, Mikko

doi:10.1016/j.est.2021.103855

Cited by 45 publications

(17 citation statements)

References 44 publications

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“…Since both active materials operate within the stability window of the electrolyte, the formation of an effective and protecting SEI on Li 4 Ti 5 O 12 does not occur Figure A shows the voltage profile of an LTO–LFP battery cell in the absence and in the presence of a redox mediator galvanostatically cycled at 0.3 C. The profiles are comparable to those reported in the literature for this battery chemistries. − The capacity in the first discharge step in the absence of a redox mediator was 8.5 mAh, which is close to the theoretical value of 10 mAh (1 mAh cm –2 ). The only significant difference in the voltage profile related to the presence of a redox mediator is the longer charge process and the short discharge step.…”

Section: Resultssupporting

confidence: 80%

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

García-Quismondo

Alvarez-Conde

García

et al. 2022

ACS Appl. Mater. Interfaces

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The formation of a protecting nanolayer, so-called solid electrolyte interphase (SEI), on the negative electrode of Li-ion batteries (LIBs) from product precipitation of the cathodic decomposition of the electrolyte is a blessing since the electrically insulating nature of this nanolayer protects the electrode surface, preventing continuous electrolyte decomposition and enabling the large nominal cell voltage of LIBs, e.g., 3.3–3.8 V. Thus, the protection performance of the nanolayer SEI is essential for LIBs to achieve a long cycle life. Unfortunately, the evaluation of this critical property of the SEI is not trivial. Herein, a new, cheap, and easily implementable methodology, the redox-mediated enhanced coulometry, is presented to estimate the protecting quality of the SEI. The key element of the methodology is the addition of a redox mediator in the electrolyte during the degassing step (after the SEI formation cycle). The redox mediator leads to an internal self-discharge process that is inversely proportional to the protecting character of the SEI. Also, the self-discharge process results in an easily measurable decrease in Coulombic efficiency. The influence of vinylene carbonate as an electrolyte additive in the resulting SEI is used as a case study to showcase the potential of the proposed methodology.

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Section: Resultssupporting

confidence: 80%

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

García-Quismondo

Alvarez-Conde

García

et al. 2022

ACS Appl. Mater. Interfaces

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“…To avoid the single working condition of the sample, there are 20 batteries in the experiment, which come from buses in three different cities in China, including Xiamen (7), Beijing (7), and Chongqing (6). After long-term operation of the bus, the battery capacity has different degrees of attenuation.…”

Section: Battery Sample and Capacity Calibrationmentioning

confidence: 99%

“…Lithium iron phosphate battery (LFP) is widely used in electric vehicles and energy storage due to its advantages of good safety performance, long cycle life, low price, and low environmental pollution [3][4][5] . However, the voltage platform of the LFP battery is very flat, and its SOC (state of charge) estimation and residual capacity estimation are the focus and difficulty in the industry [6][7] .…”

Section: Introductionmentioning

confidence: 99%

Estimation of the residue capacity of lithium iron phosphate battery based on the internal resistance obtained from charging voltage drop

Yin,

Yu,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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This study takes the 80 Ah lithium iron phosphate (LFP) prismatic battery that is from the vehicle and is in the middle or end of life as the research target, and the voltage-drop resistance (VDR), which is calculated through the voltage drop at the charging end, is used to for residue capacity estimation. The relationship between the VDR and the residue capacity of the battery is systematically studied, including factors such as charging cut-off voltage, charging current, charging end time, and charging current change mechanism. The results show that the residue capacity is a quadratic function relationship with the charging cut-off voltage and VDR. The VDR is affected by the charging cut-off voltage, charging cut-off current, and charging end time. There is no significant difference in the influence of VDR obtained by the charging modes of step charging and constant current charging. By testing the VDR under different charging conditions and different resting times, and standardizing the VDR, the residue capacity estimation error of 97% of the vehicle’s batteries is within ±5%, which meets the application requirements for residue capacity estimation.

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“…The technique of differential analysis has been widely used to study various Lithium-Ion Battery (LIB) chemistries, such as LFP, NMC, and LTO. [11] Typically, these studies focus on investigating aging data obtained from low-rate constantcurrent experiments. Jiang et al [12] conducted research on large-format LFP cells and analyzed their incremental capacity (IC) curves.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Capacity Analysis‐based State‐of‐health Estimation Model for Lithium‐ion Batteries in High‐power Applications

Hamed

Yusuf

Suliga

et al. 2023

Batteries & Supercaps

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The Incremental Capacity (IC) is a rich source of data for the state‐of‐health estimation of lithium‐ion batteries. This data is typically collected during a low C‐rate (dis)charge of the battery which is not representative of many real‐world applications outside the research laboratories. Here, this limitation is showcased to be mitigated by employing a new feature‐extraction technique applied to a large dataset including 105 batteries with cycle lives ranging from 158 to 1637 cycles. The state‐of‐health of these batteries is successfully predicted with a mean‐absolute‐percentage error below 0.7 % by using three regression models of support vector regressor, multi‐layer perceptron, and random forest. The methodologies proposed in this work facilitate the development of accurate IC‐based state‐of‐health predictors for lithium‐ion batteries in on‐board applications.

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State of health estimation of cycle aged large format lithium-ion cells based on partial charging

Cited by 45 publications

References 44 publications

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

Estimation of the residue capacity of lithium iron phosphate battery based on the internal resistance obtained from charging voltage drop

An Incremental Capacity Analysis‐based State‐of‐health Estimation Model for Lithium‐ion Batteries in High‐power Applications

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