Pith. sign in

REVIEW 6 major objections 6 minor 60 references

A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction

T0 review · 6 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read The paper proposes a transferable physics-informed framework that detects battery degradation phases from current-voltage histograms and predicts capacity knees, reporting a strong linear correlation (rho = 0.962) between detected…

desk verdict A credible battery-degradation framework that overclaims knee prediction: the rho=0.962 correlation is not a prediction experiment. read the letter →

arxiv 2501.14573 v2 pith:Z5K3YSAC submitted 2025-01-24 eess.SY cs.SY

Huang Zhang , Xixi Liu , Faisal Altaf , Torsten Wik This is my paper · ORCID
classification eess.SYcs.SY
keywords batterydegradationdiagnosisknee-onsetdetectionkneepredictionphysics-informedneuralnetworksdeephiddenphysicsmodelXGBoosttransferlearninghistogramfeatures
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

This paper is trying to establish that a battery management system can diagnose degradation, detect the onset of a capacity knee, and predict when the knee arrives, all from ordinary voltage and current measurements, without full physics models or lab-only tests. The authors build a hybrid model: a deep hidden physics model estimates three degradation modes--loss of lithium inventory and loss of active material at each electrode--and an XGBoost classifier turns those estimates plus calendar time into a degradation phase (before knee-onset, between knee-onset and knee, after knee). The model is pre-trained on protocol cycling data and then fine-tuned on a small amount of labeled data from a different dynamic-cycling scenario, freezing the network that encodes degradation dynamics. The central result is that with only one labeled cell that has a knee, the fine-tuned model detects phases well enough that knee-onset is found online, and a strong linear correlation ($\rho = 0.962$) between detected knee-onset and knee points turns that onset detection into a knee prediction. If this holds beyond the dataset, it would make online aging-aware classification, second-life repurposing, and cloud battery digital twins practical.

What carries the argument

The load-bearing object is the deep hidden physics model (DeepHPM), a pair of neural networks that together represent each degradation mode $u_i = f_i(t,x)$: a surrogate network $F$ approximates the mode itself, and a dynamic network $G$ approximates the right-hand side of the hidden PDE $u_t = g(t,x,u,u_x)$, with the PDE residual and its time gradient included in the loss. A separate XGBoost classifier with softmax outputs maps the three estimated modes plus calendar time to one of three degradation phases. The feature pipeline is a set of histograms counting time spent in voltage and/or current ranges, with the 2D current-voltage 17-feature set chosen as the generalizing input. Transfer learning is implemented by freezing $G$ and fine-tuning only $F$ on a small target-scenario sample, on the assumption that degradation dynamics are shared across usage scenarios while the mapping from features to modes is scenario-specific.

What would settle it

Take cells aged under a different knee pathway (for example particle cracking instead of low-temperature lithium plating), fine-tune the surrogate network on one labeled cell, and compare phase-detection accuracy and the knee-onset-to-knee correlation against the source-scenario results; if accuracy or correlation degrades substantially while a model with both networks unfrozen does not, the frozen-dynamics assumption is falsified.

Watch

Extended reading notes

Core claim

The central claim is that battery degradation can be diagnosed and knee behavior predicted online by a transferable hybrid model using histogram features from routinely measured current and voltage. The model splits the degradation process into three phases separated by knee-onset and knee. In the source scenario the 2D histogram-based 17-feature set is the best overall feature set for estimating the three degradation modes, and the XGBoost classifier reaches 96% phase-detection accuracy. In the dynamic cycling target scenario, fine-tuning only the surrogate network with labeled data from one cell that has a knee improves phase-detection accuracy from 67.24% to 88.19%, and makes Phase 3 detectable, which the pre-trained model misses. The authors also report that knee-onset and knee points identified by their curvature-based method are strongly linearly correlated ($\rho=0.962$), so knee prediction can be made from knee-onset detection online.

Load-bearing premise

The transfer works only if the battery's internal degradation dynamics stay the same between the lab source scenario and the field target scenario, because the network encoding those dynamics is frozen and only the output mapping is fine-tuned.

Editorial extensions

If this is right

  • Fine-tuning a pre-trained hybrid model on a single cell with knee occurrence restores Phase 3 detection in the target scenario, and the authors propose this as a lower data bar for field deployment.
  • With degradation phases detected online, knee-onset can be read off as the Phase 1-to-Phase 2 transition, giving an early warning before the knee is reached.
  • The linear relation between knee-onset and knee identified in the data ($\rho=0.962$) means that once knee-onset is detected, the capacity knee point can be predicted rather than only detected.
  • The 2D histogram feature set retains the joint distribution of current and voltage and is the best transferable feature set in both source and target scenarios, suggesting it can be used without per-vehicle feature re-engineering.
  • These components together enable cloud-based battery management functions: degradation diagnosis, aging-aware classification into phases, and second-life repurposing decisions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An untested implication is that the same framework would transfer to other cell chemistries, because histogram features are chemistry-agnostic; that is exactly what the frozen-dynamics assumption would predict, and it is not verified here.
  • If the knee-onset-to-knee correlation holds across diverse usage data, knee prediction becomes a univariate calibration problem once onset is known; the paper only verifies the correlation in the cycling dataset studied, so this is speculative beyond that.
  • The robustness analysis suggests noise hurts more than missing extreme-range histogram features; a practical extension would be to filter or denoise onboard measurements before aggregation rather than to expand the feature set.
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

6 major / 6 minor

Summary. This paper proposes a transferable physics-informed framework for battery degradation diagnosis, knee-onset detection, and knee prediction. The framework combines histogram-based feature engineering (five feature sets, including a 2D current-voltage 17-feature set), a hybrid model consisting of a DeepHPM (surrogate NN plus dynamic NN) for estimating three degradation modes (LLI, LAM_NE, LAM_PE) and an XGBoost classifier for degradation phase detection (Phase 1/2/3), and a fine-tuning strategy that freezes the dynamic NN and adapts the surrogate NN using small amounts of labeled data from a target scenario. The method is evaluated on the ICL dataset: six cells from experiment 1 and six cells from experiment 5 form the source scenarios, and six cells from experiment 4 (WLTP discharge) form the target scenario. The paper reports that fine-tuning improves degradation mode estimation and phase detection, with accuracy increasing from 67.24% (pre-trained) to 88.29% (fine-tuned with two cells). It also reports a strong linear correlation (rho = 0.962) between knee-onset and knee points and claims this enables online knee prediction.

Significance. If the central claims were fully validated, the framework would be a useful contribution to battery BMS digital-twin research: it combines physically motivated degradation-mode estimation with a lightweight transfer strategy and uses only histogram features that are easy to aggregate on-board. The manuscript is transparent about computational costs and reports architecture and hyperparameter details. However, the current version does not validate the knee-prediction claim, and some conclusions are not supported by the paper's own tables. The transfer assumption behind the fine-tuning strategy is acknowledged to be questionable, and the comparison against a Gaussian process baseline is not an apples-to-apples transfer comparison. With additional validation and a careful revision of the feature-set and prediction claims, the work could be a significant contribution; in its present form the significance is conditional.

major comments (6)
  1. [Section 3.2 and Abstract] The knee-prediction claim is asserted but never evaluated. The only support is the correlation rho = 0.962 between knee-onset and knee points identified by the authors' own curvature-based method. No regression equation, scatter plot, out-of-sample prediction, or prediction error metric (e.g., RMSE, calibration, prediction intervals) is provided. With at most six knee cells, a high correlation on the identification data does not demonstrate predictive skill. Please add a direct evaluation of knee prediction from detected knee-onset points, ideally with held-out cells and a clearly defined prediction protocol.
  2. [Tables 4 and 6, and Conclusions] The claim that the 2D histogram-based 17-feature set is "the best choice" in both source and target scenarios is not supported by the reported RMSE values. In Table 4, the 17-feature set achieves the minimum RMSE only for LAM_PE; for LLI the voltage-based 5-feature set is best, and for LAM_NE the current-based 5-feature set is best. In Table 6 (fine-tuned with two cells), the 17-feature set is best only for LAM_NE, while the voltage-based 5-feature set is best for LLI and LAM_PE. If the 17-feature set is selected based on an aggregate criterion, state it explicitly and provide the corresponding comparison; otherwise, the abstract and conclusions must be revised to reflect the mode-dependent ranking.
  3. [Section 2.5] The transfer claim relies on the assumption that parameters in Eqn. (2) remain unchanged across usage scenarios while only the surrogate in Eqn. (1) is fine-tuned. The authors themselves note that "Some physical parameters in Eqn. (2) may indeed change significantly with battery aging, which is contradictory to this assumption." Because the dynamic NN is frozen, the model cannot adapt degradation dynamics in the target scenario. Please provide empirical evidence that this assumption is acceptable for the considered transfer (e.g., compare fine-tuning both NNs with freezing G, and show that the latter does not lose accuracy), or clearly state the limitation and its impact on the generalizability of the transfer results.
  4. [Section 2.6.3 and Table 7] Phase-detection metrics are reported as point estimates averaged over five train-test splits, but no variance or confidence intervals are given. The variability appears substantial: Phase 3 precision/recall/F1 go from 0.00 (fine-tuned with one cell without knee) to 1.00/0.40/0.57 (fine-tuned with one cell with knee). The claim that "the fine-tuning strategy is proven to be effective" requires reporting mean +/- standard deviation (or per-split values) for all classification metrics, as is done for RMSE in Table B.10.
  5. [Tables B.12 and B.13] The GP baseline is only pre-trained and is not fine-tuned in the target scenario, whereas the DeepHPM is fine-tuned. The conclusion that the "fine-tuned DeepHPM model" outperforms the "GP regression surrogate model pretrained in the source scenario" is therefore a comparison of a fine-tuned model against a non-fine-tuned baseline. To support the claim that the physics-informed structure (rather than fine-tuning alone) enables adaptation, please include a fine-tuned GP in the target scenario as well, or otherwise adjust the interpretation.
  6. [Section 3.2] The knee-onset and knee labels are produced by the authors' own curvature-based method (Ref. [38]), and the rho = 0.962 correlation is computed on those same labels. If the curvature method is biased (e.g., systematically early or late knee-onset), the correlation and the phase-detection evaluation inherit that bias. The manuscript should either validate the curvature labels against an independent annotation method, or explicitly discuss the circularity and its effect on the reported correlation and classification accuracy.
minor comments (6)
  1. [Section 4] The abbreviation for performance digital twin is given as "PDT" in Section 3.4 but as "DPT" in Section 4; use one abbreviation consistently.
  2. [Figure 5] The normalized capacity curves are not labeled with cell IDs, making it hard to map them to Table 3; consider adding a legend or noting which color corresponds to which experiment.
  3. [Table 2] The column header "Input feature" is singular but lists multiple features; use "Input features".
  4. [Definition 2.3] Reference [37] (IEEE Std 485-2020) is a standard for lead-acid batteries; citing it for the general knee definition is unconventional. Consider citing a lithium-ion-specific source (e.g., Ref. [21]) in Definition 2.3.
  5. [Section 2.6.2] The Bayesian hyperparameter optimization is described but the exact search space and number of trials are not reported; adding this detail would aid reproducibility.
  6. [Section 3.2] The sentence "It can be concluded from Table B.11 that adding Gaussian noise makes model performance worse than missing four histogram features in extreme ranges" is ambiguous because the comparison is not uniform across all fine-tuning configurations; specify the conditions under which this conclusion holds.

Circularity Check

0 steps flagged · score 2.0 of 10

The main derivation chain is self-contained; the knee-prediction statement in Section 3.2 is an unvalidated correlation-based assertion rather than a circular reduction.

full rationale

The core pipeline (histogram features -> DeepHPM degradation-mode estimation -> XGBoost phase detection) is not circular. DeepHPM targets are the pseudo-OCV-quantified LLI/LAM_NE/LAM_PE modes, an external measurement chain (Birkl et al. [30]), and the loss in Eqns. (4)-(7) fits F and G to those targets; no output is defined in terms of the histogram inputs by construction. Phase detection is a supervised classifier trained on phase labels that are transparently derived from the curvature-based knee/knee-onset identification of Ref. [38]; the inputs are voltage/current histogram features, not the capacity curves used to make the labels, so the classifier is not predicting its own training labels by definition. Knee-onset detection as the Phase 1-to-2 transition is an operational restatement of Definition 2.5, not a hidden fitted result. The one genuine weakness is in Section 3.2: 'We again find a strong linear correlation between knee-onset and knee (rho = 0.962)... With this strong linear correlation, online battery capacity knee prediction can be made from detected knee-onset points.' No out-of-sample prediction, regression equation, or prediction-error metric is reported, so the title's knee-prediction claim is not directly demonstrated. That is an evidentiary gap rather than circularity: the correlation is an empirical statistic, not a parameter fitted and then renamed as a prediction, and no equation in the paper makes knee prediction equal to the fitted correlation by construction. The self-citation to Ref. [38] supplies the ground-truth labels and the correlation, but the central degradation-mode results are independently grounded, so the self-citation is not load-bearing enough to raise the score above 2.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest on data-derived histogram bounds, hyperparameter choices, a fitted knee-onset-to-knee correlation, and assumptions that pseudo-OCV degradation modes are ground truth and that degradation dynamics transfer across scenarios. No new physical entities are introduced.

free parameters (4)
  • Histogram percentile bounds = 1st, 33rd, 67th, 99th percentiles per cell/experiment
    The histogram bins are computed from measured voltage and current data for each cell, so the features are data-dependent and not derived from physics.
  • DeepHPM architecture sizes = For each mode and feature set, e.g., [2,64], [6,32], [4,64] hidden layers and neurons
    Selected by Bayesian hyperparameter optimization in the source scenario; the architecture directly affects degradation-mode estimation accuracy.
  • XGBoost hyperparameters = 100 trees, learning rate 0.0207, max depth 8, minimum sum instance weight 7
    Selected by hyperparameter search; these values control phase-detection classification performance.
  • Knee-onset to knee linear correlation = rho = 0.962; slope and intercept not reported
    The linear relation is fit to knee-onset and knee points identified by the curvature method and is used to justify online knee prediction.
assumptions (5)
  • domain assumption Degradation modes quantified by pseudo-OCV measurements and the Birkl model are treated as true ground truth.
    Section 2.6.1 states, 'we therefore took the quantified degradation modes as the true degradation modes'; no independent physical validation is provided.
  • domain assumption The parameters of the degradation dynamics g in Eqn. (2) are unchanged across source and target scenarios.
    Section 2.5 assumes Eqn. (2) is scenario-invariant and freezes the dynamic NN during fine-tuning, while acknowledging this may contradict aging-dependent physical parameters.
  • domain assumption Histogram features extracted from voltage and current time series are sufficient to infer degradation modes.
    Section 2.3 assumes only voltage and current are available and that time spent in percentile-defined ranges captures aging-relevant information.
  • domain assumption The curvature-based knee-onset and knee identification correctly defines the three degradation phases.
    Section 2.2 relies on the authors' prior method to define phase boundaries; the resulting labels are therefore not independently verified.
  • standard math Neural networks can represent the unknown degradation functions f and g in Eqns. (1) and (2).
    The DeepHPM construction in Section 2.4 relies on the approximation capacity of NNs and automatic differentiation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction." pith.science (2026). https://pith.science/paper/Z5K3YSAC

@misc{pith2026250114573,
  author       = {Pith},
  title        = {Pith review of: A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z5K3YSAC}},
  note         = {Machine review of arXiv:2501.14573}
}
read the original abstract

The techno-economic and safety concerns of battery capacity knee occurrence call for developing online knee detection and prediction methods as an advanced battery management system (BMS) function. To address this, a transferable physics-informed framework that consists of a histogram-based feature engineering method, a hybrid physics-informed model, and a fine-tuning strategy, is proposed for online battery degradation diagnosis and knee-onset detection. The hybrid model is first developed and evaluated using a scenario-aware pipeline in protocol cycling scenarios and then fine-tuned to create local models deployed in a dynamic cycling scenario. A 2D histogram-based 17-feature set is found to be the best choice in both source and target scenarios. The fine-tuning strategy is proven to be effective in improving battery degradation mode estimation and degradation phase detection performance in the target scenario. Again, a strong linear correlation was found between the identified knee-onset and knee points. As a result, advanced BMS functions, such as online degradation diagnosis and prognosis, online knee-onset detection and knee prediction, aging-aware battery classification, and second-life repurposing, can be enabled through a battery performance digital twin in the cloud.

Figures

Figures reproduced from arXiv: 2501.14573 by the authors.

Figure 1
Figure 1. The identified degradation phases and estimated degradation modes of cell E4B [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. The histogram (left) and cumulative histogram (right) generated using the [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. The histogram (left) and cumulative histogram (right) generated using the [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The C/10 discharge-charge curve of a sample cell. [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Normalized capacity fade curves of 18 cells in the ICL dataset. Note that [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]
Figure 6
Figure 6. Figure 6: Predicted and observed degradation modes (left) and detected and observed [PITH_FULL_IMAGE:figures/full_fig_p026_6.png]
Figure 7
Figure 7. Figure 7: Predicted and observed degradation modes (left) and detected and observed [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 55 canonical work pages

  1. [38]

    Zhang, F

    H. Zhang, F. Altaf, T. Wik, Battery capacity knee-onset identifica- tion and early prediction using degradation curvature, Journal of Power Sources 608 (2024) 234619

    work page 2024

  2. [1]

    Parlikar, M

    A. Parlikar, M. Schott, K. Godse, D. Kucevic, A. Jossen, H. Hesse, High- power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation, Applied Energy 333 (2023) 120541

    work page 2023

  3. [2]

    Woody, G

    M. Woody, G. A. Keoleian, P. Vaishnav, Decarbonization potential of electrifying 50% of US light-duty vehicle sales by 2030, Nature Commu- nications 14 (1) (2023) 7077

    work page 2023

  4. [3]

    X. Hu, L. Xu, X. Lin, M. Pecht, Battery lifetime prognostics, Joule 4 (2) (2020) 310–346

    work page 2020

  5. [4]

    K. L. Gering, S. V. Sazhin, D. K. Jamison, C. J. Michelbacher, B. Y. Liaw, M. Dubarry, M. Cugnet, Investigation of path dependence in com- mercial lithium-ion cells chosen for plug-in hybrid vehicle duty cycle protocols, Journal of Power Sources 196 (7) (2011) 3395–3403

    work page 2011

  6. [5]

    Baure, M

    G. Baure, M. Dubarry, Durability and reliability of ev batteries under electric utility grid operations: Impact of frequency regulation usage on cell degradation, Energies 13 (10) (2020) 2494

    work page 2020

  7. [6]

    T. Raj, A. A. Wang, C. W. Monroe, D. A. Howey, Investigation of path- 34 dependent degradation in lithium-ion batteries, Batteries & Supercaps 3 (12) (2020) 1377–1385

    work page 2020

  8. [7]

    J. M. Reniers, G. Mulder, D. A. Howey, Review and performance com- parison of mechanical-chemical degradation models for lithium-ion bat- teries, Journal of The Electrochemical Society 166 (14) (2019) A3189– A3200

    work page 2019

Show all 60 references
  1. [8]

    X.-G. Yang, Y. Leng, G. Zhang, S. Ge, C.-Y. Wang, Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging, Journal of Power Sources 360 (2017) 28–40

    2017

  2. [9]

    J. Keil, N. Paul, V. Baran, P. Keil, R. Gilles, A. Jossen, Linear and non- linear aging of lithium-ion cells investigated by electrochemical analysis and in-situ neutron diffraction, Journal of The Electrochemical Society 166 (16) (2019) A3908

    2019

  3. [10]

    J. Keil, A. Jossen, Electrochemical modeling of linear and nonlinear ag- ing of lithium-ion cells, Journal of The Electrochemical Society 167 (11) (2020) 110535

    2020

  4. [11]

    R. Fang, P. Dong, H. Ge, J. Fu, Z. Li, J. Zhang, Capacity plunge of lithium-ion batteries induced by electrolyte drying-out: Experimental and modeling study, Journal of Energy Storage 42 (2021) 103013

    2021

  5. [12]

    X. Gu, H. Bai, X. Cui, J. Zhu, W. Zhuang, Z. Li, X. Hu, Z. Song, Challenges and opportunities for second-life batteries: Key technologies and economy, Renewable and Sustainable Energy Reviews 192 (2024) 114191

    2024

  6. [13]

    W. Diao, S. Saxena, B. Han, M. Pecht, Algorithm to determine the knee point on capacity fade curves of lithium-ion cells, Energies 12 (15) (2019) 2910

    2019

  7. [14]

    Ferm ´ ın-Cueto, E

    P. Ferm ´ ın-Cueto, E. McTurk, M. Allerhand, E. Medina-Lopez, M. F. Anjos, J. Sylvester, G. Dos Reis, Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells, Energy and AI 1 (2020) 100006. 35

    2020

  8. [15]

    Greenbank, D

    S. Greenbank, D. Howey, Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life, IEEE Transactions on Industrial Informatics 18 (5) (2021) 2965–2973

    2021

  9. [16]

    Zhang, Y

    C. Zhang, Y. Wang, Y. Gao, F. Wang, B. Mu, W. Zhang, Accel- erated fading recognition for lithium-ion batteries with nickel-cobalt- manganese cathode using quantile regression method, Applied Energy 256 (2019) 113841

    2019

  10. [17]

    Sohn, H.-E

    S. Sohn, H.-E. Byun, J. H. Lee, Two-stage deep learning for online prediction of knee-point in li-ion battery capacity degradation, Applied Energy 328 (2022) 120204

    2022

  11. [18]

    Haris, M

    M. Haris, M. N. Hasan, S. Qin, Degradation curve prediction of lithium- ion batteries based on knee point detection algorithm and convolutional neural network, IEEE Transactions on Instrumentation and Measure- ment 71 (2022) 1–10

    2022

  12. [19]

    H. You, J. Zhu, X. Wang, B. Jiang, X. Wei, H. Dai, Nonlinear aging knee-point prediction for lithium-ion batteries faced with different ap- plication scenarios, Etransportation 18 (2023) 100270

    2023

  13. [20]

    Costa, D

    N. Costa, D. Anse´ an, M. Dubarry, L. S´ anchez, Icformer: A deep learn- ing model for informed lithium-ion battery diagnosis and early knee detection, Journal of Power Sources 592 (2024) 233910

    2024

  14. [21]

    P. M. Attia, A. Bills, F. B. Planella, P. Dechent, G. Dos Reis, M. Dubarry, P. Gasper, R. Gilchrist, S. Greenbank, D. Howey, et al., “knees” in lithium-ion battery aging trajectories, Journal of The Elec- trochemical Society 169 (6) (2022) 060517

    2022

  15. [22]

    Xiong, L

    R. Xiong, L. Li, Z. Li, Q. Yu, H. Mu, An electrochemical model based degradation state identification method of lithium-ion battery for all- climate electric vehicles application, Applied energy 219 (2018) 264–275

    2018

  16. [23]

    J. Kim, H. Chun, M. Kim, S. Han, J.-W. Lee, T.-K. Lee, Effective and practical parameters of electrochemical li-ion battery models for degradation diagnosis, Journal of Energy Storage 42 (2021) 103077. 36

    2021

  17. [24]

    G. Fan, D. Lu, M. S. Trimboli, G. L. Plett, C. Zhu, X. Zhang, Nonde- structive diagnostics and quantification of battery aging under different degradation paths, Journal of Power Sources 557 (2023) 232555

    2023

  18. [25]

    Teliz, C

    E. Teliz, C. F. Zinola, V. D ´ ıaz, Identification and quantification of age- ing mechanisms in li-ion batteries by electrochemical impedance spec- troscopy., Electrochimica Acta 426 (2022) 140801

    2022

  19. [26]

    Barzacchi, M

    L. Barzacchi, M. Lagnoni, R. Di Rienzo, A. Bertei, F. Baronti, Enabling early detection of lithium-ion battery degradation by linking electro- chemical properties to equivalent circuit model parameters, Journal of Energy Storage 50 (2022) 104213

    2022

  20. [27]

    Dubarry, C

    M. Dubarry, C. Truchot, B. Y. Liaw, Synthesize battery degradation modes via a diagnostic and prognostic model, Journal of Power Sources 219 (2012) 204–216

    2012

  21. [28]

    Zhang, Q

    Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming, A. A. Lee, Identi- fying degradation patterns of lithium ion batteries from impedance spec- troscopy using machine learning, Nature communications 11 (1) (2020) 1706

    2020

  22. [29]

    K. A. Severson, P. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M. H. Chen, M. Aykol, P. K. Herring, D. Fraggedakis, et al., Data-driven pre- diction of battery cycle life before capacity degradation, Nature Energy 4 (5) (2019) 383–391

    2019

  23. [30]

    C. R. Birkl, M. R. Roberts, E. McTurk, P. G. Bruce, D. A. Howey, Degradation diagnostics for lithium ion cells, Journal of Power Sources 341 (2017) 373–386

    2017

  24. [31]

    Dubarry, V

    M. Dubarry, V. Svoboda, R. Hwu, B. Y. Liaw, Incremental capacity analysis and close-to-equilibrium ocv measurements to quantify capacity fade in commercial rechargeable lithium batteries, Electrochemical and solid-state letters 9 (10) (2006) A454

    2006

  25. [32]

    Bloom, A

    I. Bloom, A. N. Jansen, D. P. Abraham, J. Knuth, S. A. Jones, V. S. Battaglia, G. L. Henriksen, Differential voltage analyses of high-power, lithium-ion cells: 1. technique and application, Journal of Power Sources 139 (1-2) (2005) 295–303. 37

    2005

  26. [33]

    Andersson, M

    M. Andersson, M. Streb, J. Y. Ko, V. L. Klass, M. Klett, H. Ekstr¨ om, M. Johansson, G. Lindbergh, Parametrization of physics-based battery models from input–output data: A review of methodology and current research, Journal of Power Sources 521 (2022) 230859

    2022

  27. [34]

    Barai, K

    A. Barai, K. Uddin, M. Dubarry, L. Somerville, A. McGordon, P. Jen- nings, I. Bloom, A comparison of methodologies for the non-invasive characterisation of commercial li-ion cells, Progress in Energy and Com- bustion Science 72 (2019) 1–31

    2019

  28. [35]

    Vetter, P

    J. Vetter, P. Nov´ ak, M. R. Wagner, C. Veit, K.-C. M¨ oller, J. Besenhard, M. Winter, M. Wohlfahrt-Mehrens, C. Vogler, A. Hammouche, Ageing mechanisms in lithium-ion batteries, Journal of power sources 147 (1-2) (2005) 269–281

    2005

  29. [36]

    Pastor-Fern´ andez, W

    C. Pastor-Fern´ andez, W. D. Widanage, J. Marco, M.-´A. Gama-Valdez, G. H. Chouchelamane, Identification and quantification of ageing mech- anisms in lithium-ion batteries using the EIS technique, in: 2016 IEEE Transportation Electrification Conference and Expo (ITEC), IEEE, 2...

    2016

  30. [37]

    IEEE Power and Energy Society, IEEE Recommended Prac- tice for Sizing Lead-Acid Batteries for Stationary Applications, IEEE Std 485-2020 (Revision of IEEE Std 485-2010) (2020) 1– 69doi:10.1109/IEEESTD.2020.9103320

    2020

  31. [39]

    W. Gao, Z. Cao, Y. Fu, C. Turchiano, N. V. Kurdkandi, J. Gu, C. Mi, Comprehensive study of the aging knee and second-life potential of the nissan leaf e+ batteries, Journal of Power Sources 613 (2024) 234884

    2024

  32. [40]

    Martinez-Laserna, E

    E. Martinez-Laserna, E. Sarasketa-Zabala, I. V. Sarria, D.-I. Stroe, M. Swierczynski, A. Warnecke, J.-M. Timmermans, S. Goutam, N. Omar, P. Rodriguez, Technical viability of battery second life: A study from the ageing perspective, IEEE Transactions on Industry Ap- plications ...

    2018

  33. [41]

    Kirkaldy, M

    N. Kirkaldy, M. A. Samieian, G. J. Offer, M. Marinescu, Y. Patel, Lithium-ion battery degradation: Comprehensive cycle ageing data and analysis for commercial 21700 cells, Journal of Power Sources 603 (2024) 234185

    2024

  34. [42]

    Zhang, F

    H. Zhang, F. Altaf, T. Wik, Scenario-aware machine learning pipeline for battery lifetime prediction, in: 2024 European Control Conference (ECC), IEEE, 2024, pp. 212–217

    2024

  35. [43]

    Ouyang, J

    D. Ouyang, J. Weng, M. Chen, J. Wang, Z. Wang, Sensitivities of lithium-ion batteries with different capacities to overcharge/over- discharge, Journal of Energy Storage 52 (2022) 104997

    2022

  36. [44]

    Raissi, Deep hidden physics models: Deep learning of nonlinear par- tial differential equations, Journal of Machine Learning Research 19 (25) (2018) 1–24

    M. Raissi, Deep hidden physics models: Deep learning of nonlinear par- tial differential equations, Journal of Machine Learning Research 19 (25) (2018) 1–24

    2018

  37. [45]

    J. Yu, L. Lu, X. Meng, G. E. Karniadakis, Gradient-enhanced physics- informed neural networks for forward and inverse pde problems, Com- puter Methods in Applied Mechanics and Engineering 393 (2022) 114823

    2022

  38. [46]

    A. G. Baydin, B. A. Pearlmutter, A. A. Radul, J. M. Siskind, Auto- matic differentiation in machine learning: a survey, Journal of machine learning research 18 (153) (2018) 1–43

    2018

  39. [47]

    T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowl- edge discovery and data mining, 2016, pp. 785–794

    2016

  40. [48]

    S. Song, C. Fei, H. Xia, Lithium-ion battery soh estimation based on xgboost algorithm with accuracy correction, Energies 13 (4) (2020) 812

    2020

  41. [49]

    J. Sun, C. Fan, H. Yan, Soh estimation of lithium-ion batteries based on multi-feature deep fusion and xgboost, Energy 306 (2024) 132429

    2024

  42. [50]

    Jafari, Y.-C

    S. Jafari, Y.-C. Byun, Xgboost-based remaining useful life estimation model with extended kalman particle filter for lithium-ion batteries, Sen- sors 22 (23) (2022) 9522. 39

    2022

  43. [51]

    K. Liu, Q. Peng, Y. Che, Y. Zheng, K. Li, R. Teodorescu, D. Widanage, A. Barai, Transfer learning for battery smarter state estimation and age- ing prognostics: Recent progress, challenges, and prospects, Advances in Applied Energy 9 (2023) 100117

    2023

  44. [52]

    Mayemba, R

    Q. Mayemba, R. Mingant, A. Li, G. Ducret, P. Venet, Aging datasets of commercial lithium-ion batteries: A review, Journal of Energy Storage 83 (2024) 110560

    2024

  45. [53]

    Sulzer, P

    V. Sulzer, P. Mohtat, A. Aitio, S. Lee, Y. T. Yeh, F. Steinbacher, M. U. Khan, J. W. Lee, J. B. Siegel, A. G. Stefanopoulou, et al., The challenge and opportunity of battery lifetime prediction from field data, Joule 5 (8) (2021) 1934–1955

    2021

  46. [54]

    Akiba, S

    T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next- generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discov- ery & data mining, 2019, pp. 2623–2631

    2019

  47. [55]

    Grandini, E

    M. Grandini, E. Bagli, G. Visani, Metrics for multi-class classification: an overview, arXiv preprint arXiv:2008.05756 (2020)

    2020 arXiv

  48. [56]

    F. Wang, Z. Zhai, Z. Zhao, Y. Di, X. Chen, Physics-informed neural net- work for lithium-ion battery degradation stable modeling and prognosis, Nature Communications 15 (1) (2024) 4332

    2024

  49. [57]

    R. Xu, Y. Wang, Z. Chen, A hybrid approach to predict battery health combined with attention-based transformer and online correction, Jour- nal of Energy Storage 65 (2023) 107365

    2023

  50. [58]

    J. Zhao, Z. Wang, Y. Wu, A. F. Burke, Predictive pretrained trans- former (ppt) for real-time battery health diagnostics, Applied Energy 377 (2025) 124746

    2025

  51. [59]

    Naseri, S

    F. Naseri, S. Gil, C. Barbu, E. C ¸ etkin, G. Yarimca, A. Jensen, P. G. Larsen, C. Gomes, Digital twin of electric vehicle battery systems: Com- prehensive review of the use cases, requirements, and platforms, Renew- able and Sustainable Energy Reviews 179 (2023) 113280

    2023

  52. [60]

    Mathews, B

    I. Mathews, B. Xu, W. He, V. Barreto, T. Buonassisi, I. M. Peters, Technoeconomic model of second-life batteries for utility-scale solar con- sidering calendar and cycle aging, Applied Energy 269 (2020) 115127. 40

    2020

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.