Pith. sign in

REVIEW 3 major objections 6 minor 69 references

Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A method reconstructs quasi-open-circuit voltage curves from home storage field data and uses them to estimate degradation modes, finding loss of lithium inventory dominant in the evaluated systems.

desk verdict Genuinely new field-data qOCV reconstruction and ICA/DVA degradation-mode analysis, but the LLI-dominant headline lacks an independent SOC anchor and should be treated as qualitative until validated. read the letter →

arxiv 2411.08025 v1 pith:4AT33ABD submitted 2024-11-12 eess.SY cond-mat.mtrl-scics.SY

Jan Figgener , Jakob Bors , Matthias Kuipers , Felix Hildenbrand , Mark Junker , Lucas Koltermann , Philipp Woerner , Marc Mennekes
show 3 more authors
David Haberschusz Kai-Philipp Kairies Dirk Uwe Sauer
This is my paper · ORCID
classification eess.SYcond-mat.mtrl-scics.SY
keywords fieldlithiumdatadegradationhomemethodstoragevoltage
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Batteries in home solar storage systems slowly lose capacity as they age. Engineers usually measure a battery's open circuit voltage, the voltage with no current flowing, in a laboratory to see what is wearing out inside. This paper shows how to reconstruct that voltage curve from ordinary operation data collected over years in real homes, using quiet overnight periods when the battery is supplying power at low, steady current. The reconstructed curve is then compared year to year to track changes in the battery's internal chemistry.

The authors applied established diagnostic methods, incremental capacity analysis and differential voltage analysis, to these reconstructed curves. These methods turn small bends in the voltage curve into peaks that can be linked to specific aging processes. They found that the main aging process in the home storage systems they studied is loss of lithium inventory, meaning the battery loses usable lithium ions and therefore holds less charge. Some systems also showed possible loss of active material in one electrode, but the evidence was less clear.

The method is not perfect. It depends on the battery's state of charge estimate from an earlier study by the same group, and the authors had to shift some curve segments horizontally to correct for errors. For lithium iron phosphate batteries, which have a very flat voltage curve, the results were too uncertain to draw strong conclusions. The authors say field capacity tests support their reconstruction, but they also argue their reconstructed curves are more reliable than those tests.

Extended reading notes

Core claim

The abstract states: "We show that low-dynamic operational phases, such as the overnight household supply with electricity, are suitable for recreating quasi OCV curves" and "The dominant degradation mode observed for the home storage systems under evaluation is the loss of lithium inventory." If correct, the paper demonstrates that OCV curves can be reconstructed continuously from multi-year field operation data and that established ICA/DVA feature tracking can identify LLI as the main aging mode in real home storage systems.

Load-bearing premise

The reconstructed qOCV curves inherit SOC estimates from Figgener et al. [1], and when SOC errors appear, the authors shift entire partial curves horizontally to their mean (Section 2.2.3). The method assumes these SOC estimates are accurate enough, and that the alignment correction restores the true SOC axis without distorting the OCV shape. If the SOC error is larger or more systematic than the mean alignment can absorb, the resulting qOCV, IC, and DV curves would be shifted and the FOI-DM correlations would be unreliable.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents a method for reconstructing quasi-open-circuit-voltage (qOCV) curves from multi-year operational field data of 21 home storage systems. Low-dynamic discharge phases (e.g., overnight household supply) are filtered, corrected for ohmic drops using a DCR look-up table, and combined into annual qOCV curves. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are applied, with literature-derived features of interest (FOIs) tracked over time and correlated with state-of-health (SOH) estimates from the authors' prior work. The central claim is that loss of lithium inventory (LLI) is the dominant degradation mode for LMO/NMC and NMC systems, with LFP less conclusive. The method is validated against field capacity tests from the same prior dataset.

Significance. If the central claim holds, the paper would demonstrate a valuable capability: continuous electrochemical diagnostics of home storage batteries from ordinary operational data, without laboratory OCV measurements. The dataset is substantial (106 system-years, 14 billion datapoints) and publicly available, and the method is presented with enough detail to be reproduced. The use of ICA/DVA with literature-based FOI tracking is well motivated, and the multi-chemistry application (LMO/NMC, NMC, LFP) adds breadth. However, the conclusion that LLI dominates is not supported by an independent ground truth: the SOC axis of every reconstructed qOCV curve is inherited from the same prior capacity-estimation pipeline that is later used for validation, and the horizontal alignment step cannot correct a systematic SOC bias common to all phases. The paper's own LFP section concedes that SOC inaccuracies limit the analysis for at least one chemistry. These issues do not invalidate the method but require additional validation or sensitivity analysis before the degradation-mode conclusion can be considered robust.

major comments (3)
  1. [Section 3.3] The reconstructed qOCV curves' SOC axis is taken entirely from the capacity/SOC estimates of Figgener et al. [1], and the alignment step in Section 2.2.3 shifts each partial curve horizontally to the mean of all phases in a period. This mean-shift cannot remove a systematic SOC error that is common to all phases in a period, such as a bias in the estimated capacity fade that grows with age. Because ICA and DVA features are computed along this SOC axis, a proportional error in the estimated capacity would shift IC peak positions and change peak intensities in ways that mimic the classic LLI signatures described in [37]. The paper reports in Section 3.2 and Figure 9 that LFP systems indeed suffer from SOC inaccuracies, confirming that this mechanism is real in the data. The authors should provide a sensitivity analysis—for example, artificially scaling or shifting the SOC axis by plausible error bounds and showing how the FOI trends and the LLI conclusion change—or anchor the SOC axis to an independent measurement on at least a subset of systems.
  2. [Section 3.3] The validation in Section 3.3 uses field capacity tests from [1,2], which are the same underlying measurements and the same prior estimation pipeline that supplies the SOC axis for the reconstructed qOCV curves. The statement that the qOCV curve 'is considered more reliable' than the field capacity tests further emphasizes that the validation is not independent. Since the degradation-mode inference is built on correlations between FOI shifts and the SOH estimates from [1], any systematic error in those SOH estimates propagates directly into both the qOCV shape and the correlation analysis. To break this circularity, the authors should compare at least one reconstructed qOCV curve to a laboratory-measured OCV curve from the same cell type, or generate synthetic field-like data with known degradation modes and verify that the FOI-DM correlations recover the true modes.
  3. [Section 3.1] The attribution of FOI trends to specific degradation modes is qualitative and non-unique. For example, FOI 2's intensity decline is stated to be 'attributable mainly to LLI, but could also include LAMNE and LAMPE,' and FOI 4's decrease is said to indicate LAMNE/LAMPE but is also correlated with SOH. The conclusion that LLI is dominant is supported by multiple FOIs, but the paper never quantifies the relative contributions of LLI, LAMNE, and LAMPE. The correlation coefficients in Table 1 show that FOI shifts correlate with SOH, but correlation does not identify the degradation mode, and the p-values are reported without multiple-comparison correction. The authors should either apply a quantitative degradation-mode decomposition (e.g., fitting half-cell OCP-based models as in [8]) or explicitly state that the LLI-dominant conclusion is a hypothesis consistent with the FOI trends rather than a quantified result.
minor comments (6)
  1. [Section 2.2.2] The dynamics filter threshold is given as '10 % of the C-rate'; please clarify how the C-rate is defined for each system, since the systems have different power and energy ratings and the C-rate may vary over time.
  2. [Section 2.2.3] In the sentence 'Sometimes, SOC errors occur, leading to a horizontal deferral of single identified phases,' the word 'deferral' should likely be 'offset' or 'shift'.
  3. [Figure 5] The normalization of FOI values in Figure 5d (intensity normalized by highest peak, position by voltage range, distance by SOC range) is described only in the caption; please define these normalizations in the main text of Section 3.1 so that the reported slopes in percent per year are unambiguous.
  4. [Table 1] The p-values are reported only as '< 0.05' or '0.85'; please report exact values or at least a meaningful precision, and consider whether a multiple-comparison correction across the ten FOI/SOH correlations is appropriate.
  5. [Abstract] The abstract emphasizes 'eight years' of field data, but the method produces yearly qOCV curves and the analysis in Section 3.1 shows a seven-year span for the exemplary SmallLMO system; please clarify how many systems reach the full eight years and whether 'year one' refers to the first full year of operation.
  6. [Section 3.1] The abbreviations EOC and EOD are used without definition; please define them at first use (end-of-charge and end-of-discharge).
Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several domain assumptions that are not independently verified in this paper: accurate SOC estimates from prior work, adequacy of the DCR correction, transferability of laboratory FOI-DM correlations to field systems, and validity of the field capacity tests used for validation. The method also depends on hand-chosen thresholds and ad hoc SOC alignment.

free parameters (6)
  • Dynamics filter threshold = 10% of C-rate
    Upper limit on short-term current change to select low-dynamic phases; affects which data are used for qOCV reconstruction. Section 2.2.2.
  • Charge throughput filter threshold = 5% of battery capacity
    Minimum SOC range covered by a phase to be included; affects number and length of partial qOCV curves. Section 2.2.2.
  • Gaussian filter smoothing parameter = not specified
    Applied before differentiation; width will affect IC/DV peak positions and intensities. Section 2.3.
  • Horizontal SOC alignment shift = mean of all partial curves
    Ad hoc correction of SOC errors by shifting phases to mean; can mask SOC estimation errors. Section 2.2.3.
  • DCR look-up table = estimated from current pulses
    DCR(SOC,T) used for overvoltage correction; estimated from the same field data and recalculated over time. Section 2.2.1.
  • qOCV period length = one year (could be shorter)
    Data split into yearly periods to create independent qOCV curves; period length affects curve resolution and aging resolution. Section 2.2.3.
assumptions (5)
  • domain assumption The OCV curve is the electrochemical signature of the battery and can be reconstructed from low-dynamic operational phases with sufficient accuracy.
    Foundational premise of the method; stated in Introduction and Section 2.2.
  • domain assumption SOC estimation from Figgener et al. [1] is accurate enough for OCV reconstruction.
    qOCV curves are plotted against SOC; the paper inherits SOC estimates from prior work and only corrects them by horizontal shifting. Section 2.2.3.
  • domain assumption The DCR-based overvoltage correction removes enough kinetic overpotential to make quasi-OCV curves reliable.
    Equation (2-2) assumes a linear resistance model and neglects C-rate dependence; Section 2.2.1.
  • domain assumption FOI-DM correlations from laboratory studies apply to field home storage systems.
    The degradation mode classification relies on literature-derived FOI correlations [37,56], not on a model fitted to the field data. Section 2.3.
  • domain assumption Field capacity tests from Figgener et al. [1] are valid for validation.
    Used to validate qOCV curves, but the same authors derived them from the same dataset. Section 3.3.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data." pith.science (2026). https://pith.science/paper/4AT33ABD

@misc{pith2026241108025,
  author       = {Pith},
  title        = {Pith review of: Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4AT33ABD}},
  note         = {Machine review of arXiv:2411.08025}
}
read the original abstract

A battery's open circuit voltage (OCV) curve can be seen as its electrochemical signature. Its shape and age-related shift provide information on aging processes and material composition on both electrodes. However, most OCV analyses have to be conducted in laboratories or specified field tests to ensure suitable data quality. Here, we present a method that reconstructs the OCV curve continuously over the lifetime of a battery using the operational data of home storage field measurements over eight years. We show that low-dynamic operational phases, such as the overnight household supply with electricity, are suitable for recreating quasi OCV curves. We apply incremental capacity analysis and differential voltage analysis and show that known features of interest from laboratory measurements can be tracked to determine degradation modes in field operation. The dominant degradation mode observed for the home storage systems under evaluation is the loss of lithium inventory, while the loss of active material might be present in some cases. We apply the method to lithium nickel manganese cobalt oxide (NMC), a blend of lithium manganese oxide (LMO) and NMC, and lithium iron phosphate (LFP) batteries. Field capacity tests validate the method.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

69 extracted references · 44 canonical work pages

  1. [1]

    Multi-year field measurements of home storage systems and their use in capacity estimation

    Figgener J, van Ouwerkerk J, Haberschusz D, Bors J, Woerner P, Mennekes M, Hildenbrand F, Hecht C, Kairies K-P, Wessels O, Sauer DU. Multi-year field measurements of home storage systems and their use in capacity estimation. In: Nat Energy 2024. https://doi.org/10.1038/s41560-024-01620-9

    doi:10.1038/s41560-024-01620-9 2024

  2. [37]

    Best practices for incremental capacity analysis

    Dubarry M, Anseán D. Best practices for incremental capacity analysis. In: Front. Energy Res. 2022. 10. https://doi.org/10.3389/fenrg.2022.1023555

    arXiv 2022

  3. [8]

    Degradation diagnostics for lithium ion cells

    Birkl CR, Roberts MR, McTurk E, Bruce PG, Howey DA. Degradation diagnostics for lithium ion cells. In: Journal of Power Sources 2017. 341:373–86. https://doi.org/10.1016/j.jpowsour.2016.12.011

    doi:10.1016/j.jpowsour.2016.12.011 2017

  4. [2]

    Data for: Multi-year field measurements of home storage systems used for capacity estimation

    Figgener J, Haberschusz D, Wessels O, Kai-Philipp Kairies, Bors J, van Ouwerkerk J, Sauer DU. Data for: Multi-year field measurements of home storage systems used for capacity estimation. In: Zenodo 2024. https://doi.org/10.5281/ze- nodo.12091223

    doi:10.5281/ze- 2024

  5. [3]

    A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

    Wang Z, Feng G, Zhen D, Gu F, Ball A. A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles. In: Energy Reports 2021. 7:5141–61. https://doi.org/10.1016/j.egyr.2021.08.113

    work page doi:10.1016/j.egyr.2021.08.113 2021

  6. [4]

    A Comparative Study on Open Circuit Voltage Models for Lithium-ion Batteries

    Yu Q-Q, Xiong R, Wang L-Y, Lin C. A Comparative Study on Open Circuit Voltage Models for Lithium-ion Batteries. In: Chin. J. Mech. Eng. 2018. 31(1). https://doi.org/10.1186/s10033-018-0268-8

    work page doi:10.1186/s10033-018-0268-8 2018

  7. [5]

    A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

    Ren Z, Du C, Wu Z, Shao J, Deng W. A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries. In: International Journal of Energy Research 2021. 45(9):13692–711. https://doi.org/10.1002/er.6700

    work page doi:10.1002/er.6700 2021

  8. [6]

    A Parametric Open Circuit Voltage Model for Lithium Ion Bat- teries

    Birkl CR, McTurk E, Roberts MR, Bruce PG, Howey DA. A Parametric Open Circuit Voltage Model for Lithium Ion Bat- teries. In: J. Electrochem. Soc. 2015. 162(12):A2271-A2280. https://doi.org/10.1149/2.0331512jes

    work page doi:10.1149/2.0331512jes 2015

Show all 69 references
  1. [7]

    A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries

    Farmann A, Sauer DU. A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries. In: Journal of Power Sources 2017. 347:1–13. https://doi.org/10.1016/j.jpowsour.2017.01.098

    2017 doi

  2. [9]

    Critical review of non-invasive diagnosis techniques for quantifi- cation of degradation modes in lithium-ion batteries

    Pastor-Fernández C, Yu TF, Widanage WD, Marco J. Critical review of non-invasive diagnosis techniques for quantifi- cation of degradation modes in lithium-ion batteries. In: Renewable and Sustainable Energy Reviews 2019. 109:138–

    2019

  3. [10]

    Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries

    Petzl M, Danzer MA. Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries. In: IEEE Trans. En- ergy Convers. 2013. 28(3):675–81. https://doi.org/10.1109/TEC.2013.2259490

    2013

  4. [11]

    Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges

    Gantenbein S, Schönleber M, Weiss M, Ivers-Tiffée E. Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges. In: Sustainability 2019. 11(23):6697. https://doi.org/10.3390/su11236697

    2019 doi

  5. [13]

    Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance

    Zhu J, Dewi Darma MS, Knapp M, Sørensen DR, Heere M, Fang Q, Wang X, Dai H, Mereacre L, Senyshyn A, Wei X, Ehrenberg H. Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance. In: Journal of Powe...

    2020

  6. [14]

    Lithium-ion battery aging mechanisms and life model under differ- ent charging stresses

    Gao Y, Jiang J, Zhang C, Zhang W, Ma Z, Jiang Y. Lithium-ion battery aging mechanisms and life model under differ- ent charging stresses. In: Journal of Power Sources 2017. 356:103–14. https://doi.org/10.1016/j.jpowsour.2017.04.084

    2017 doi

  7. [15]

    A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gauss- ian filter

    Li Y, Abdel-Monem M, Gopalakrishnan R, Berecibar M, Nanini-Maury E, Omar N, van den Bossche P, van Mierlo J. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gauss- ian filter. In: Journal of Power Sources 2018....

    2018 doi

  8. [16]

    Overcharge-induced capacity fading analysis for large format lith- ium-ion batteries with LiyNi1/3Co1/3Mn1/3O2+ LiyMn2O4 composite cathode

    Ouyang M, Ren D, Lu L, Li J, Feng X, Han X, Liu G. Overcharge-induced capacity fading analysis for large format lith- ium-ion batteries with LiyNi1/3Co1/3Mn1/3O2+ LiyMn2O4 composite cathode. In: Journal of Power Sources 2015. 279:626–

    2015

  9. [17]

    Incremental Capacity Analysis and Close-to-Equilibrium OCV Measure- ments to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries

    Dubarry M, Svoboda V, Hwu R, Yann Liaw B. Incremental Capacity Analysis and Close-to-Equilibrium OCV Measure- ments to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries. In: Electrochem. Solid-State Lett

  10. [18]

    Identify capacity fading mechanism in a commercial LiFePO4 cell

    Dubarry M, Liaw BY. Identify capacity fading mechanism in a commercial LiFePO4 cell. In: Journal of Power Sources

  11. [19]

    Combining a Fatigue Model and an Incremental Capacity Anal- ysis on a Commercial NMC/Graphite Cell under Constant Current Cycling with and without Calendar Aging

    Plattard T, Barnel N, Assaud L, Franger S, Duffault J-M. Combining a Fatigue Model and an Incremental Capacity Anal- ysis on a Commercial NMC/Graphite Cell under Constant Current Cycling with and without Calendar Aging. In: Batter- ies 2019. 5(1):36. https://doi.org/10.3390/ba...

    2019 doi

  12. [20]

    Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves

    Berecibar M, Dubarry M, Villarreal I, Omar N, van Mierlo J. Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves. In: 2016 IEEE Vehicle Power and Propulsion Conference (VPPC): Pro- ceedings 17-20 October 2016, Hangzhou, China. Piscatawa...

    2016

  13. [21]

    State of Health Estimation for Lithium-Ion Batteries Based on Healthy Fea- tures and Long Short-Term Memory

    Wu Y, Xue Q, Shen J, Lei Z, Chen Z, Liu Y. State of Health Estimation for Lithium-Ion Batteries Based on Healthy Fea- tures and Long Short-Term Memory. In: IEEE Access 2020. 8:28533–47. https://doi.org/10.1109/AC- CESS.2020.2972344

    2020

  14. [22]

    Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications

    Dubarry M, Truchot C, Cugnet M, Liaw BY, Gering K, Sazhin S, Jamison D, Michelbacher C. Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part I: Initial char- acterizations. In: Journal of Power ...

    2011 doi

  15. [24]

    Data-driven prediction of battery cycle life before capacity degradation

    Severson KA, Attia PM, Jin N, Perkins N, Jiang B, Yang Z, Chen MH, Aykol M, Herring PK, Fraggedakis D, Bazant MZ, Harris SJ, Chueh WC, Braatz RD. Data-driven prediction of battery cycle life before capacity degradation. In: Nat En- ergy 2019. 4(5):383–91. https://doi.org/10.10...

    2019 doi

  16. [25]

    Lithium-ion battery SoH estimation based on incremental capacity peak tracking at several current levels for online application

    Maures M, Capitaine A, Delétage J-Y, Vinassa J-M, Briat O. Lithium-ion battery SoH estimation based on incremental capacity peak tracking at several current levels for online application. In: Microelectronics Reliability 2020. 114:113798. https://doi.org/10.1016/j.microrel.2020.113798

    2020

  17. [26]

    Synthesize battery degradation modes via a diagnostic and prognostic model

    Dubarry M, Truchot C, Liaw BY. Synthesize battery degradation modes via a diagnostic and prognostic model. In: Jour- nal of Power Sources 2012. 219:204–16. https://doi.org/10.1016/j.jpowsour.2012.07.016

    2012 doi

  18. [27]

    Modeling and Evaluation of Li-Ion Battery Performance Based on the Electric Vehicle Field Tests

    Samadani E, Farhad S, Panchal S, Fraser R, Fowler M. Modeling and Evaluation of Li-Ion Battery Performance Based on the Electric Vehicle Field Tests. In: SAE International 2014. https://doi.org/10.4271/2014-01-1848

    2014 doi

  19. [28]

    Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data

    Xu Z, Wang J, Lund PD, Zhang Y. Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data. In: Energy 2021. 225:120160. https://doi.org/10.1016/j.en- ergy.2021.120160

    2021

  20. [29]

    Large-scale field data-based battery aging prediction driven by sta- tistical features and machine learning

    Wang Q, Wang Z, Liu P, Zhang L, Sauer DU, Li W. Large-scale field data-based battery aging prediction driven by sta- tistical features and machine learning. In: Cell Reports Physical Science 2023:101720. https://doi.org/10.1016/j.xcrp.2023.101720

    2023

  21. [30]

    Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence

    Li W, Demir I, Cao D, Jöst D, Ringbeck F, Junker M, Sauer DU. Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence. In: Energy Storage Materials 2022. 44:557–70. https://doi.org/10.1016/j.ensm.2021.10.023

    2022 doi

  22. [31]

    On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

    Weng C, Cui Y, Sun J, Peng H. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. 2013

    2013

  23. [33]

    Electrode ageing estimation and open circuit voltage reconstruction for lithium ion bat- teries

    Tian J, Xiong R, Shen W, Sun F. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion bat- teries. In: Energy Storage Materials 2021. 37:283–95. https://doi.org/10.1016/j.ensm.2021.02.018

    2021 doi

  24. [34]

    On-board aging estimation using half-cell voltage curves for LiFePO4 cathode-based lithium-ion batteries for EV applications

    Marongiu A, Sauer DU. On-board aging estimation using half-cell voltage curves for LiFePO4 cathode-based lithium-ion batteries for EV applications. In: Int.J Automot. Technol. 2016. 17(3):465–72. https://doi.org/10.1007/s12239-016-0048- 3

    2016 doi

  25. [35]

    https://doi.org/10.1016/j.jpowsour.2015.01.051

    2015 doi

  26. [36]

    Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries

    Tang X, Wang Y, Liu Q, Gao F. Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries. In: iScience 2021. 24(10):103103. https://doi.org/10.1016/j.isci.2021.103103

    2021

  27. [38]

    Identifying battery aging mechanisms in large format Li ion cells

    Dubarry M, Liaw BY, Chen M-S, Chyan S-S, Han K-C, Sie W-T, Wu S-H. Identifying battery aging mechanisms in large format Li ion cells. In: Journal of Power Sources 2011. 196(7):3420–5. https://doi.org/10.1016/j.jpowsour.2010.07.029

    2011 doi

  28. [39]

    On-board capacity estimation of lithium iron phosphate batteries by means of half-cell curves

    Marongiu A, Nlandi N, Rong Y, Sauer DU. On-board capacity estimation of lithium iron phosphate batteries by means of half-cell curves. In: Journal of Power Sources 2016. 324:158–69. https://doi.org/10.1016/j.jpowsour.2016.05.041

    2016 doi

  29. [40]

    Calendar Aging of Lithium-Ion Batteries

    Keil P, Schuster SF, Wilhelm J, Travi J, Hauser A, Karl RC, Jossen A. Calendar Aging of Lithium-Ion Batteries. In: J. Electrochem. Soc. 2016. 163(9):A1872-A1880. https://doi.org/10.1149/2.0411609jes

    2016 doi

  30. [41]

    Calendar Aging of NCA Lithium-Ion Batteries Investigated by Differential Voltage Analysis and Cou- lomb Tracking

    Keil P, Jossen A. Calendar Aging of NCA Lithium-Ion Batteries Investigated by Differential Voltage Analysis and Cou- lomb Tracking. In: J. Electrochem. Soc. 2017. 164(1):A6066-A6074. https://doi.org/10.1149/2.0091701jes

    2017 doi

  31. [42]

    Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries

    Tian J, Xiong R, Yu Q. Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries. In: IEEE Trans. Ind. Electron. 2019. 66(2):1576–84. https://doi.org/10.1109/TIE.2018.2798606

    2019

  32. [43]

    Pastor-Fernández C, Uddin K, Chouchelamane GH, Widanage WD, Marco J. A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Batte...

    2017 doi

  33. [44]

    State-of-health estimation of LiFePO4/graphite batteries based on a model using differential capacity

    Torai S, Nakagomi M, Yoshitake S, Yamaguchi S, Oyama N. State-of-health estimation of LiFePO4/graphite batteries based on a model using differential capacity. In: Journal of Power Sources 2016. 306:62–9. https://doi.org/10.1016/j.jpowsour.2015.11.070

    2016 doi

  34. [45]

    A fast estimation algorithm for lithium-ion battery state of health

    Tang X, Zou C, Yao K, Chen G, Liu B, He Z, Gao F. A fast estimation algorithm for lithium-ion battery state of health. In: Journal of Power Sources 2018. 396:453–8. https://doi.org/10.1016/j.jpowsour.2018.06.036

    2018 doi

  35. [46]

    Battery incremental capacity curve extraction by a two-dimensional Luen- berger–Gaussian-moving-average filter

    Tang X, Liu K, Lu J, Liu B, Wang X, Gao F. Battery incremental capacity curve extraction by a two-dimensional Luen- berger–Gaussian-moving-average filter. In: Applied Energy 2020. 280:115895. https://doi.org/10.1016/j.apen- ergy.2020.115895

    2020

  36. [47]

    State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model

    He J, Wei Z, Bian X, Yan F. State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model. In: IEEE Trans. Transp. Electrific. 2020. 6(2):417–26. https://doi.org/10.1109/TTE.2020.2994543

    2020

  37. [48]

    Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path De- pendence of Battery Degradation

    Dubarry M, Baure G, Devie A. Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path De- pendence of Battery Degradation. In: J. Electrochem. Soc. 2018. 165(5):A773-A783. https://doi.org/10.1149/2.0421805jes

    2018 doi

  38. [49]

    Performance comparison of four lithium–ion battery technologies under calendar aging

    Eddahech A, Briat O, Vinassa J-M. Performance comparison of four lithium–ion battery technologies under calendar aging. In: Energy 2015. 84:542–50. https://doi.org/10.1016/j.energy.2015.03.019

    2015 doi

  39. [50]

    Capacity and power fading mechanism identification from a commercial cell evaluation

    Dubarry M, Svoboda V, Hwu R, Liaw BY. Capacity and power fading mechanism identification from a commercial cell evaluation. In: Journal of Power Sources 2007. 165(2):566–72. https://doi.org/10.1016/j.jpowsour.2006.10.046

    2007 doi

  40. [51]

    Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications

    Dubarry M, Truchot C, Liaw BY, Gering K, Sazhin S, Jamison D, Michelbacher C. Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part II. Degradation mecha- nism under 2C cycle aging. In: Journal o...

    2011 doi

  41. [52]

    Non-invasive yet separate investigation of anode/cathode degradation of lithium-ion batteries (nickel–cobalt–manganese vs

    Shafiei Sabet P, Warnecke AJ, Meier F, Witzenhausen H, Martinez-Laserna E, Sauer DU. Non-invasive yet separate investigation of anode/cathode degradation of lithium-ion batteries (nickel–cobalt–manganese vs. graphite) due to ac- celerated aging. In: Journal of Power Sources 20...

    2020

  42. [53]

    Investigation of degradation mechanisms in lithium-ion batteries by incremental open-circuit- voltage characterization and impedance spectra

    Goldammer MS, Kowal J. Investigation of degradation mechanisms in lithium-ion batteries by incremental open-circuit- voltage characterization and impedance spectra. In: 2020 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE. 2020, p. 1–8. https://doi.org/10.1109/VPPC49...

    2020

  43. [54]

    Lithium-Ion Battery Cell Cycling and Usage Analysis in a Heavy-Duty Truck Field Study

    Svens P, Behm M, Lindbergh G. Lithium-Ion Battery Cell Cycling and Usage Analysis in a Heavy-Duty Truck Field Study. In: Energies 2015. 8(5):4513–28. https://doi.org/10.3390/en8054513

    2015 doi

  44. [55]

    Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

    Dubarry M, Tun M, Baure G, Matsuura M, Rocheleau RE. Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests. In: Electronics 2021. 10(13):1593. https://doi.org/10.3390/electro- nics10131593

    2021 doi

  45. [56]

    Analysis of Synthetic Voltage vs

    Dubarry M, Beck D. Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis. In: Energies 2021. 14(9):2371. https://doi.org/10.3390/en14092371

    2021 doi

  46. [57]

    Nondestructive detection, characterization, and quantification of lithium plating in commercial lith- ium-ion batteries

    Petzl M, Danzer MA. Nondestructive detection, characterization, and quantification of lithium plating in commercial lith- ium-ion batteries. In: Journal of Power Sources 2014. 254:80–7. https://doi.org/10.1016/j.jpowsour.2013.12.060

    2014 doi

  47. [58]

    Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

    Dubarry M, Costa N, Matthews D. Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics. In: Nat Commun 2023. 14(1):3138. https://doi.org/10.1038/s41467-023-38895-7

    2023 doi

  48. [59]

    https://doi.org/10.1016/j.rser.2019.03.060

    2019 doi

  49. [60]

    Calendar Aging of Lithium-Ion Batteries: I

    Keil P, Schuster SF, Wilhelm J, Travi J, Hauser A, Karl RC, Jossen A. Calendar Aging of Lithium-Ion Batteries: I. Im- pact of the Graphie Anode on Capacity Fade. In: J. Electrochem. Soc. 2016. 163(9):A1872-A1880

    2016

  50. [61]

    Analysis and key findings from real-world electric vehicle field data

    Pozzato G, Allam A, Pulvirenti L, Negoita GA, Paxton WA, Onori S. Analysis and key findings from real-world electric vehicle field data. In: Joule 2023. https://doi.org/10.1016/j.joule.2023.07.018

    2023 doi

  51. [62]

    Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application

    Waag W, Käbitz S, Sauer DU. Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. In: Applied Energy 2013. 102:885–97. https://doi.org/10.1016/j.apenergy.2012.09.030

    2013 doi

  52. [63]

    Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance

    Dubarry M, Yasir F, Costa N, Matthews D. Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance. In: Batteries 2023. 9(8):395. https://doi.org/10.3390/batteries9080395

    2023 doi

  53. [64]

    The development of bat- tery storage systems in Germany: A market review (status 2023)

    Figgener J, Hecht C, Haberschusz D, Bors J, Spreuer KG, Kairies K-P, Stenzel P, Sauer DU. The development of bat- tery storage systems in Germany: A market review (status 2023). 2023. http://arxiv.org/pdf/2203.06762v3

    2023 arXiv

  54. [65]

    Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries

    Ecker M, Nieto N, Käbitz S, Schmalstieg J, Blanke H, Warnecke A, Sauer DU. Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. In: Journal of Power Sources 2014. 248:839–51. https://doi.org/10.1016/j.jpowsour.2013.09.143

    2014 doi

  55. [66]

    Dissection and Quantitative Description of Aging of Lithium-Ion Batteries Using Non-Destructive Methods Validated by Post-Mortem-Analyses [Doctors thesis]

    Meinert Lewerenz. Dissection and Quantitative Description of Aging of Lithium-Ion Batteries Using Non-Destructive Methods Validated by Post-Mortem-Analyses [Doctors thesis]. Aachen. RWTH Aachen. 2018

    2018

  56. [67]

    Expanded In Situ Aging Indicators for Lithium-Ion Batteries with a Blended NMC-LMO Electrode Cycled at Sub-Ambient Temperature

    Smith AJ, Svens P, Varini M, Lindbergh G, Lindström RW. Expanded In Situ Aging Indicators for Lithium-Ion Batteries with a Blended NMC-LMO Electrode Cycled at Sub-Ambient Temperature. In: J. Electrochem. Soc. 2021. 168(11):110530. https://doi.org/10.1149/1945-7111/ac2d17

    2021 doi

  57. [68]

    Calendar Aging and Gas Generation in Commercial Graph- ite/NMC-LMO Lithium-Ion Pouch Cell

    Mao Z, Farkhondeh M, Pritzker M, Fowler M, Chen Z. Calendar Aging and Gas Generation in Commercial Graph- ite/NMC-LMO Lithium-Ion Pouch Cell. In: J. Electrochem. Soc. 2017. 164(14):A3469-A3483. https://doi.org/10.1149/2.0241714jes

    2017 doi

  58. [71]

    Degradation Mechanisms of the Graphite Electrode in C6/LiFePO4 Batter- ies Unraveled by a Non-Destructive Approach

    Li D, Danilov DL, Gao L, Yang Y, Notten PH. Degradation Mechanisms of the Graphite Electrode in C6/LiFePO4 Batter- ies Unraveled by a Non-Destructive Approach. In: J. Electrochem. Soc. 2016. 163(14):A3016-A3021. https://doi.org/10.1149/2.0821614jes

    2016 doi

  59. [72]

    Statistics review 3: hypothesis testing and P values

    Whitley E, Ball J. Statistics review 3: hypothesis testing and P values. In: Crit Care 2002. 6(3):222–5. https://doi.org/10.1186/cc1493

    2002 doi

  60. [2006]

    https://doi.org/10.1149/1.2221767

    9(10):A454. https://doi.org/10.1149/1.2221767

    doi

  61. [2009]

    https://doi.org/10.1016/j.jpowsour.2009.05.036

    194(1):541–9. https://doi.org/10.1016/j.jpowsour.2009.05.036

    2009 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.