Rate dependency of incremental capacity analysis (dQ/dV) as a diagnostic tool for lithium-ion batteries

Fly, Ashley; Chen, Rui

doi:10.1016/j.est.2020.101329

Cited by 113 publications

(53 citation statements)

References 41 publications

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“…The terminal voltage of a Li-ion battery cell is a sum of the OCV, hysteresis voltage, and overpotentials due to polarization and the internal (ohmic) resistance, all affected by cell temperature. Usually, when the current rates applied to the battery are very low, both the hysteresis voltage and the polarization overpotentials are negligible [29], [30], and the effect of temperature change due to self-heating can also be ignored [31]. Hence, the voltage data measured under very low current rates in the laboratory dataset usually contain the most pertinent knowledge of the OCV, and we denote them as the pseudo-open-circuit voltage (pseudo-OCV) data, e.g., the C/25 constant current in the characterization processes in the Oxford Battery Degradation Dataset.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

She

Zou

et al. 2022

IEEE Trans. Transp. Electrific.

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Section: B Data Preprocessingmentioning

confidence: 99%

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

She

Zou

et al. 2022

IEEE Trans. Transp. Electrific.

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“…As cycle increases, the two oxidations (O1 and O2) and two reductions (R1 and R2) peaks are still evident in both cells, but the intensity gradually decreases. However, the peak broadening of LMO/Li cell suggests that the uncoated composite is subjected to severe fatigue at high voltage during prolonged cycling 71 . The highly fatigued LMO/Li cell demonstrates that without coating, the Mn dissolution is rather high under high operating voltage 72 .…”

Section: Resultsmentioning

confidence: 99%

“…However, the peak broadening of LMO/Li cell suggests that the uncoated composite is subjected to severe fatigue at high voltage during prolonged cycling. 71 The highly fatigued LMO/Li cell demonstrates that without coating, the Mn dissolution is rather high under high operating voltage. 72 Unlike LMO/Li cell, the dQ/dV curves of LMO-VO/Li cell is more overlapped with sharper oxidation and reduction peaks.…”

Section: Electrochemical Propertiesmentioning

confidence: 98%

Improved cycling stability of V₂O₅ modified spinel LiMn₂O₄ cathode at high cut‐off voltage for lithium‐ion batteries

Radzi

Kufian

Vengadaesvaran

et al. 2022

Int J Applied Ceramic Tech

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Spinel lithium manganese oxide, LiMn2O4 coated with V2O5 layer (labeled as LMO‐VO) has been developed and its electrochemical performances as cathode material for lithium‐ion batteries has been evaluated at high cut‐off voltage (>4.5 V vs. Li/Li+) and compared with pristine LiMn2O4 (labeled as LMO). The crystal structure investigations show that LMO‐VO has longer Li–O bond length for fast Li‐ion diffusion kinetic process. The scanning electron microscopy results indicate that LMO‐VO has finer particles and the V2O5 layer has been successfully coated on the LMO surface uniformly. The highly conductive V2O5 coating layer enhances the ionic conductivity of the LMO cathode, as evidenced by the significant drop of Rct value from the Nyquist plot. Under high operating voltage, the cell employed with coated LMO shows exceptional cycling performance in capacity retention and potential difference. After 300 cycles, the capacity retention per cycle has been boosted from 99.90% to 99.94% by adopting the V2O5 coating layer. In addition, surface coating with V2O5 stabilizes the potential difference at very minimal change for a longer period. This convincingly proves that the V2O5 coating layer not only protects against hydrofluoric acid (HF) attack and greatly restrains the increase of cell polarization at high voltage.

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“…Differential methodologies such as incremental capacity analysis (ICA) and differential voltage analysis (DVD) are used to study the degradation of the Li-ion battery [39]- [41]. These peaks shift in terms of amplitude and position with time in ICA peaks indicates degradation of the battery.…”

Section: A Literature Reviewmentioning

confidence: 99%

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

et al. 2022

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Lithium-ion (Li-ion) batteries have increasingly been used in diverse applications. Accurate estimation of the state of health (SOH) of the Li-ion batteries is vital for all stakeholders and critical in various applications such as electric vehicles (EVs). The electrical equivalent circuit (EEC) 2-RC model is often used to model the battery operation but has not been used to capture the degradation of battery cells over time. This paper uses the 2-RC model to capture the degradation of the Li-ion battery. The proposed model is not only time-dependent but also captures the effect of temperature on battery degradation. The proposed approach estimates the SOH accurately and is also considerably flexible for diverse cells of different chemistry. We further generalize an N-RC model approach to evaluate the SOH of the battery. We compare the proposed model (2-RC) with the 1-RC model, and through numerical results, we show that the 2-RC model outperforms 1-RC and reduces the computational cost significantly. Similarly, the 2-RC model outperforms 3-RC and higher-order circuits. We also show that the proposed approach can capture the battery dynamics better for specific smaller orders of the polynomial (associated with Arrhenius equation) when compared with the 1-RC approach with considerably reduced (up to 60%) root mean square error (RMSE). Lastly, the average testing RMSE for 2-RC is 52.4%. INDEX TERMS Lithium-ion battery, State-of-health, Equivalent circuit model, Open circuit voltageThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Rate dependency of incremental capacity analysis (dQ/dV) as a diagnostic tool for lithium-ion batteries

Cited by 113 publications

References 41 publications

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

Improved cycling stability of V₂O₅ modified spinel LiMn₂O₄ cathode at high cut‐off voltage for lithium‐ion batteries

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

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Rate dependency of incremental capacity analysis (dQ/dV) as a diagnostic tool for lithium-ion batteries

Cited by 113 publications

References 41 publications

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

Improved cycling stability of V2O5 modified spinel LiMn2O4 cathode at high cut‐off voltage for lithium‐ion batteries

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

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Product

Resources

About

Improved cycling stability of V₂O₅ modified spinel LiMn₂O₄ cathode at high cut‐off voltage for lithium‐ion batteries