pith. sign in

arxiv: 2602.02866 · v3 · submitted 2026-02-02 · 📡 eess.SY · cs.SY

Estimation of Cell-to-Cell Variation and State of Health for Battery Modules with Parallel-Connected Cells

Qinan Zhou , Jing Sun This is my paper

Pith reviewed 2026-05-16 07:48 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords battery modulescell-to-cell variationstate of healthincremental capacity analysisdifferential voltage analysisparallel-connected cells
0
0 comments X

The pith

A unified framework estimates cell-to-cell variation and state of health for parallel battery modules from module-level ICA and DVA signals alone.

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

The paper establishes a method to estimate both cell-to-cell variation and overall state of health in battery modules made of parallel-connected cells when only module-level signals can be measured. It extracts features from incremental capacity analysis and differential voltage analysis to support these estimates. The approach separates the two tasks so that each can use its own algorithm without the outputs affecting each other. This separation gives designers freedom to choose different metrics for variation and still obtain accurate results. Tests on three-cell modules confirm that the estimates hold across charge rates and run with low computation suitable for real-time use.

Core claim

The framework accurately estimates both CtCV and SoH for modules using only module-level information extracted from ICA and DVA, with estimations decoupled into two separate tasks allowing dedicated algorithms without mutual interference.

What carries the argument

The unified framework that extracts module-level features from ICA and DVA to decouple and solve CtCV and SoH estimations as independent tasks.

Load-bearing premise

Module-level ICA and DVA signals contain sufficient separable information to estimate individual cell variations and overall SoH without direct access to cell-level data or models of parallel-cell dynamics.

What would settle it

Directly measure the actual voltages and capacities of the individual cells in a parallel module during charge and discharge; if the framework's estimated cell-to-cell variation differs substantially from these measurements, the claim of accurate estimation from module signals fails.

Figures

Figures reproduced from arXiv: 2602.02866 by Jing Sun, Qinan Zhou.

Figure 1
Figure 1. Figure 1: Overview of Proposed Framework for Estimation of Cell-to-Cell Variation and Module-Level State of Health [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Definitions of Incremental Capacity (IC) and Differential Voltage (DV) Features [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: IC Curve Distortions Caused by CtCVs. Note that, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 1
Figure 1. Figure 1: This work employs the original RVR algorithm [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of Cell-to-Cell Variations and Module [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example M-IC/DV Curves and Related Features for [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Feature Relevance for Cell-to-Cell Variation and Module-Level SoH Estimations [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Feature Redundancy for Cell-to-Cell Variation and Module-Level SoH Estimations [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Feature Complementarity for Cell-to-Cell Variation and Module-Level SoH Estimations [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Cell-to-Cell Variation Estimation Performance under Different Numbers of Features Used [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Module-Level SoH Estimation Performance under Different Numbers of Features Used [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution between Estimation and Ground Truth [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
read the original abstract

Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules composed of parallel-connected cells is challenging when only module-level signals are measurable and individual cell behaviors remain unobserved. Although progress has been made in SoH estimation, CtCV estimation remains unresolved in the literature. This paper proposes a unified framework that accurately estimates both CtCV and SoH for modules using only module-level information extracted from incremental capacity analysis (ICA) and differential voltage analysis (DVA). With the proposed framework, CtCV and SoH estimations can be decoupled into two separate tasks, allowing each to be solved with dedicated algorithms without mutual interference and providing greater design flexibility. The framework also exhibits strong versatility in accommodating different CtCV metrics, highlighting its general-purpose nature. Experimental validation on modules with three parallel-connected cells demonstrates that the proposed framework can systematically select optimal module-level features for CtCV and SoH estimations, deliver accurate CtCV and SoH estimates with high confidence and low computational complexity, remain effective across different C-rates, and be suitable for onboard implementation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a unified framework to estimate cell-to-cell variation (CtCV) and state of health (SoH) for battery modules with parallel-connected cells using only module-level incremental capacity analysis (ICA) and differential voltage analysis (DVA) features. The approach decouples the two estimation tasks to permit independent algorithms, accommodates multiple CtCV metrics, and is validated experimentally on three-cell modules, reporting accurate estimates across C-rates with low computational cost suitable for onboard use.

Significance. If the central claims hold, the work would provide a practical advance for battery management systems by enabling non-invasive CtCV and SoH monitoring in parallel modules without cell-level sensors or explicit parallel-connection models. The decoupling and feature-selection approach offers design flexibility, and the reported experimental results on real hardware suggest potential for electric-vehicle and stationary-storage applications.

major comments (2)
  1. [Experimental validation] Experimental validation section: the manuscript states that the framework delivers accurate CtCV and SoH estimates with high confidence and low error across C-rates, yet provides neither the explicit feature-selection algorithm, the full set of performance metrics (RMSE, MAE, confidence intervals), nor the raw data or code, rendering the quantitative support for the central claim unverifiable.
  2. [Framework description] Framework description: no equations or pseudocode are supplied showing how module-level ICA/DVA peaks or curves are mapped to individual-cell variation parameters or to SoH; without this mapping it is impossible to confirm that the claimed decoupling truly eliminates mutual interference between the two tasks.
minor comments (2)
  1. Add a table summarizing the selected ICA/DVA features for each task and their corresponding performance metrics to improve clarity and reproducibility.
  2. Clarify the exact definition of the CtCV metric(s) used in the experiments, as the abstract claims versatility but does not specify which metric was employed in the reported results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to improve clarity and verifiability.

read point-by-point responses

  1. Referee: [Experimental validation] Experimental validation section: the manuscript states that the framework delivers accurate CtCV and SoH estimates with high confidence and low error across C-rates, yet provides neither the explicit feature-selection algorithm, the full set of performance metrics (RMSE, MAE, confidence intervals), nor the raw data or code, rendering the quantitative support for the central claim unverifiable.

    Authors: We agree that the current experimental section lacks sufficient detail for full verifiability. In the revised manuscript we will add the explicit feature-selection algorithm, report the complete performance metrics (RMSE, MAE, and confidence intervals) for all C-rates and modules, and include pseudocode together with a link to the analysis code. Raw data will be provided as supplementary material or upon reasonable request. revision: yes

  2. Referee: [Framework description] Framework description: no equations or pseudocode are supplied showing how module-level ICA/DVA peaks or curves are mapped to individual-cell variation parameters or to SoH; without this mapping it is impossible to confirm that the claimed decoupling truly eliminates mutual interference between the two tasks.

    Authors: We acknowledge the need for explicit mathematical detail. The revised manuscript will include the governing equations and pseudocode that map module-level ICA/DVA features to the CtCV parameters and SoH, together with a step-by-step explanation of how the two estimation tasks are decoupled to remove mutual interference. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes a unified framework decoupling CtCV and SoH estimation from module-level ICA/DVA signals into separate tasks, validated experimentally on three-cell modules across C-rates with reported low errors and feature selection. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or description that reduce any claimed result to its inputs by construction. The separability assumption is directly tested via systematic experiments rather than assumed or imported, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on domain assumptions about the information content of module-level ICA/DVA signals for parallel cells; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Module-level ICA and DVA signals contain separable information sufficient to estimate individual cell variations and module SoH
    Invoked to justify the decoupling framework and feature extraction from combined signals.

pith-pipeline@v0.9.0 · 5489 in / 1167 out tokens · 29349 ms · 2026-05-16T07:48:08.534844+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Experimental Characterization Data for Battery Modules with Parallel-Connected Cells across Diverse Module-Level State of Health and Cell-to-Cell Variations

    eess.SY 2026-04 accept novelty 7.0

    The paper provides an experimental dataset of 78 battery modules with parallel-connected cells, spanning module state of health from 100% to 80.98% and cell-to-cell variations from 0% to 9.31%, including raw time-seri...

Reference graph

Works this paper leans on

58 extracted references · 58 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    G. L. Plett,Battery Management Systems, Volume II: Equivalent- Circuit Methods. Boston, MA: Artech House, 2015

    work page 2015

  2. [2]

    A review of battery state of health estimation methods: Hybrid electric vehicle challenges,

    N. Noura, L. Boulon, and S. Jeme ¨ı, “A review of battery state of health estimation methods: Hybrid electric vehicle challenges,”World Electric Vehicle Journal, vol. 11, no. 4, p. 66, 2020

    work page 2020

  3. [3]

    Critical review of state of health estimation methods of li-ion batteries for real applications,

    M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. V . Mierlo, and P. den Bossche, “Critical review of state of health estimation methods of li-ion batteries for real applications,”Renewable and Sustainable Energy Reviews, vol. 56, pp. 572–587, 2016

    work page 2016

  4. [4]

    Data standardization requirements for 2026 and subsequent model year light-duty zero emission vehicles and plug-in hybrid electric vehicles,

    “Data standardization requirements for 2026 and subsequent model year light-duty zero emission vehicles and plug-in hybrid electric vehicles,” California Code of Regulations, title. 13 § 1962.5

    work page 2026

  5. [5]

    A review of lithium-ion battery state of health estimation and prediction methods,

    L. Yao, S. Xu, A. Tang, F. Zhou, J. Hou, Y . Xiao, and Z. Fu, “A review of lithium-ion battery state of health estimation and prediction methods,”World Electric Vehicle Journal, vol. 12, no. 3, p. 113, 2021

    work page 2021

  6. [6]

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

    C. Weng, Y . Cui, J. Sun, and H. Peng, “On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression,”Journal of Power Sources, vol. 235, pp. 36–44, 2013

    work page 2013

  7. [7]

    Model parametrization and adaptation based on the invariance of support vectors with applications to battery state-of-health monitoring,

    C. Weng, J. Sun, and H. Peng, “Model parametrization and adaptation based on the invariance of support vectors with applications to battery state-of-health monitoring,”IEEE Transactions on Vehicular Technology, vol. 64, no. 9, pp. 3908–3917, 2015

    work page 2015

  8. [8]

    A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring,

    C. Weng, J. Sun, and H. Peng, “A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring,”Journal of Power Sources, vol. 258, no. 9, pp. 228–237, 2014

    work page 2014

  9. [9]

    On-board state of health estimation of lifepo4 battery pack through differential voltage analysis,

    L. Wang, C. Pan, L. Liu, Y . Cheng, and X. Zhao, “On-board state of health estimation of lifepo4 battery pack through differential voltage analysis,”Applied Energy, vol. 168, pp. 465–472, 2016

    work page 2016

  10. [10]

    Sensitivity analysis of support vector regression-based incremental capacity analysis for battery state of health estimations,

    Q. Zhou, E. Hellstr ¨om, D. Anderson, and J. Sun, “Sensitivity analysis of support vector regression-based incremental capacity analysis for battery state of health estimations,” in2023 IEEE Conference on Control Technology and Applications (CCTA), Bridgetown, Barbados, 2023, pp. 1122–1127

    work page 2023

  11. [11]

    Incremen- tal capacity analysis and close-to-equilibrium ocv measurements to quantify capacity fade in commercial rechargeable lithium batteries,

    M. Dubarry, V . Svoboda, R. Hwu, and B. Y . Liaw, “Incremen- tal capacity analysis and close-to-equilibrium ocv measurements to quantify capacity fade in commercial rechargeable lithium batteries,” Electrochemical and Solid-State Letters, vol. 9, no. 10, p. A454, 2006

    work page 2006

  12. [12]

    Incre- mental capacity analysis as a state of health estimation method for lithium-ion battery modules with series-connected cells,

    A. Krupp, E. Ferg, F. Schuldt, K. Derendorf, and C. Agert, “Incre- mental capacity analysis as a state of health estimation method for lithium-ion battery modules with series-connected cells,”Batteries, vol. 7, no. 1, p. 2, 2021

    work page 2021

  13. [13]

    Integrated incremen- tal capacity analysis and differential thermal analysis for improved robustness in li-ion battery state of health estimation,

    D. Stephens, Q. Zhou, H. Hofmann, and J. Sun, “Integrated incremen- tal capacity analysis and differential thermal analysis for improved robustness in li-ion battery state of health estimation,” in2024 IEEE Conference on Control Technology and Applications (CCTA), New- castle upon Tyne, UK, 2024, pp. 729–734

    work page 2024

  14. [14]

    Parameter variations within li-ion battery packs – theoretical investigations and experimental quantification,

    M. Baumann, L. Wildfeuer, S. Rohr, and M. Lienkamp, “Parameter variations within li-ion battery packs – theoretical investigations and experimental quantification,”Journal of Energy Storage, vol. 18, pp. 295–307, 2018

    work page 2018

  15. [15]

    Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells,

    X. Gong, R. Xiong, and C. C. Mi, “Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells,”IEEE Transactions on Industry Applications, vol. 51, no. 2, pp. 1872–1879, 2015

    work page 2015

  16. [16]

    The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs,

    X. Liu, W. Ai, M. N. Marlow, Y . Patelb, and B. Wu, “The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs,”Applied Energy, vol. 248, pp. 489–499, 2019

    work page 2019

  17. [17]

    Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems,

    K. Kim and J.-I. Choi, “Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems,” Applied Energy, vol. 352, p. 121888, 2023

    work page 2023

  18. [18]

    Inhomogeneities and cell-to-cell variations in lithium-ion batteries, a review,

    D. Beck, P. Dechent, M. Junker, D. U. Sauer, and M. Dubarry, “Inhomogeneities and cell-to-cell variations in lithium-ion batteries, a review,”Energies, vol. 14, no. 11, p. 3276, 2021

    work page 2021

  19. [19]

    State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking,

    C. Weng, X. Feng, J. Sun, and H. Peng, “State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking,”Applied Energy, vol. 180, pp. 360–368, 2016

    work page 2016

  20. [20]

    Mechanisms for the evolution of cell-to-cell variations and their impacts on fast-charging performance within a lithium-ion battery pack,

    Y . Lu, X. Chen, X. Han, D. Guo, Y . Wang, X. Feng, and M. Ouyang, “Mechanisms for the evolution of cell-to-cell variations and their impacts on fast-charging performance within a lithium-ion battery pack,”Journal of Energy Chemistry, vol. 99, pp. 11–22, 2024

    work page 2024

  21. [21]

    Quantifiability of inherent cell-to- cell variations of commercial lithium-ion batteries,

    L. Wildfeuer and M. Lienkamp, “Quantifiability of inherent cell-to- cell variations of commercial lithium-ion batteries,”eTransportation, vol. 9, p. 100129, 2021

    work page 2021

  22. [22]

    Ageing inhomogeneity of long-term used bev-batteries and their reusability for 2nd-life applications,

    M. J. Brand, D. Quinger, G. Walder, A. Jossen, and M. Lienkamp, “Ageing inhomogeneity of long-term used bev-batteries and their reusability for 2nd-life applications,” inAElectric Vehicle Symposium and Exhibition (EVS 26), Los Angeles, US, 2012, p. 1–7

    work page 2012

  23. [23]

    A study of cell-to-cell interactions and degradation in parallel strings: implications for the battery management system,

    C. Pastor-Fern ´andez, T. Bruen, W. Widanage, M. Gama-Valdez, and J. Marco, “A study of cell-to-cell interactions and degradation in parallel strings: implications for the battery management system,” Journal of Power Sources, vol. 329, p. 574–585, 2016

    work page 2016

  24. [24]

    A study of cell-to-cell variation of capacity in parallel-connected lithium-ion battery cells,

    Z. Song, X.-G. Yang, N. Yang, F. P. Delgado, H. Hofmann, and J. Sun, “A study of cell-to-cell variation of capacity in parallel-connected lithium-ion battery cells,”eTransportation, vol. 7, p. 100091, 2021

    work page 2021

  25. [25]

    Progression of cell-to-cell variation within battery modules under different cooling structures,

    Z. Song, N. Yang, X. Lin, F. P. Delgado, H. Hofmann, and J. Sun, “Progression of cell-to-cell variation within battery modules under different cooling structures,”Applied Energy, vol. 312, p. 118836, 2022

    work page 2022

  26. [26]

    Cell sorting for parallel lithium-ion battery systems: Evaluation based on an electric circuit model,

    F. An, J. Huang, C. Wang, Z. Li, J. Zhang, S. Wang, and P. Li, “Cell sorting for parallel lithium-ion battery systems: Evaluation based on an electric circuit model,”Journal of Energy Storage, vol. 6, pp. 195– 203, 2016

    work page 2016

  27. [27]

    State of health estimation of battery modules via differential voltage analysis with local data symmetry method,

    L. Wang, X. Zhao, L. Liu, and C. Pan, “State of health estimation of battery modules via differential voltage analysis with local data symmetry method,”Electrochimica Acta, vol. 256, pp. 81–89, 2017

    work page 2017

  28. [28]

    State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations,

    Q. Zhou, D. Anderson, and J. Sun, “State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations,”eTransportation, vol. 22, p. 100346, 2024

    work page 2024

  29. [29]

    State of health estimation based on inconsistent evolution for lithium-ion battery module,

    A. Tang, X. Wu, T. Xu, Y . Hu, S. Long, and Q. Yu, “State of health estimation based on inconsistent evolution for lithium-ion battery module,”Energy, vol. 286, p. 129575, 2024

    work page 2024

  30. [30]

    Battery state of health estimation and incremental capacity analysis under dynamic charging profile using neural networks,

    Q. Zhou, G. Vuylsteke, R. D. Anderson, and J. Sun, “Battery state of health estimation and incremental capacity analysis under dynamic charging profile using neural networks,”IEEE Transactions on Industrial Electronics, 2026, early access. [Online]. Available: https://doi.org/10.1109/TIE.2026.3665970

    work page doi:10.1109/tie.2026.3665970 2026

  31. [31]

    Integrated frame- work for battery cell state-of-health estimation in complex modules: Combining current distribution analysis and novel terminal voltage estimation l-ekf modeling,

    Y . Fan, J. Zhao, Y . Li, J. Wang, F. Yang, and X. Tan, “Integrated frame- work for battery cell state-of-health estimation in complex modules: Combining current distribution analysis and novel terminal voltage estimation l-ekf modeling,”Energy, vol. 314, p. 134258, 2025. 13

    work page 2025

  32. [32]

    Differential voltage analysis and patterns in parallel- connected pairs of imbalanced cells,

    C. Wong, A. Weng, S. Pannala, J. Choi, J. B. Siegel, and A. Ste- fanopoulou, “Differential voltage analysis and patterns in parallel- connected pairs of imbalanced cells,” in2024 American Control Conference (ACC), Toronto, Canada, 2024

    work page 2024

  33. [33]

    A method of cell-to-cell variation evaluation for battery packs in electric vehicles with charging cloud data,

    Y . Lu, K. Li, X. Han, X. Feng, Z. Chu, L. Lu, P. Huang, Z. Zhang, Y . Zhang, F. Yin, X. Wang, F. Dai, M. Ouyang, and Y . Zheng, “A method of cell-to-cell variation evaluation for battery packs in electric vehicles with charging cloud data,”eTransportation, vol. 6, p. 100077, 2020

    work page 2020

  34. [34]

    Lithium-ion battery degradation indicators via incremental capacity analysis,

    D. Anse ´an, V . M. Garc ´ıa, M. Gonz ´alez, C. Blanco-Viejo, J. C. Viera, Y . F. Pulido, and L. S´anchez, “Lithium-ion battery degradation indicators via incremental capacity analysis,”IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2992–3002, 2019

    work page 2019

  35. [35]

    State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis,

    R. Zhou, R. Zhu, C.-G. Huang, and W. Peng, “State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis,”Journal of Energy Storage, vol. 51, p. 104560, 2022

    work page 2022

  36. [36]

    Differential voltage analyses of high- power, lithium-ion cells: 1. technique and application,

    I. Bloom, A. N. Jansen, D. P. Abraham, J. Knuth, S. A. Jones, V . S. Battaglia, and G. L. Henriksen, “Differential voltage analyses of high- power, lithium-ion cells: 1. technique and application,”Journal of Power Sources, vol. 139, no. 1-2, pp. 295–303, 2005

    work page 2005

  37. [37]

    Feature selection: A data perspective,

    J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, “Feature selection: A data perspective,”ACM Computing Surveys, vol. 50, no. 6, p. 1–45, 2017

    work page 2017

  38. [38]

    Bayesian inference: An introduction to principles and practice in machine learning,

    M. E. Tipping, “Bayesian inference: An introduction to principles and practice in machine learning,”Advanced Lectures on Machine Learning, vol. 3176, p. 41–62, 2003

    work page 2003

  39. [39]

    Bayesian interpolation,

    D. J. C. MacKay, “Bayesian interpolation,”Neural Computation, vol. 4, no. 3, p. 415–447, 1992

    work page 1992

  40. [40]

    Estimating mutual information for discrete-continuous mixtures,

    W. Gao, S. Kannan, S. Oh, and P. Viswanath, “Estimating mutual information for discrete-continuous mixtures,” inAdvances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, US, 2017, p. 5988–5999

    work page 2017

  41. [41]

    Conditional mutual information es- timation for mixed, discrete and continuous data,

    O. C. Mesner and C. R. Shalizi, “Conditional mutual information es- timation for mixed, discrete and continuous data,”IEEE Transactions on Information Theory, vol. 67, no. 1, pp. 464–484, 2021

    work page 2021

  42. [42]

    T. M. Cover and J. A. Thomas,Elements of Information Theory. Hoboken, US: Wiley, 2006

    work page 2006

  43. [43]

    Estimating mutual information,

    A. Kraskov, H. St ¨ogbauer, and P. Grassberger, “Estimating mutual information,”Physical Review E, vol. 69, p. 066138, 2004

    work page 2004

  44. [44]

    Normalized mutual information feature selection,

    P. A. Estevez, M. Tesmer, C. A. Perez, and J. M. Zurada, “Normalized mutual information feature selection,”IEEE Transactions on Neural Networks, vol. 20, no. 2, pp. 189–201, 2009

    work page 2009

  45. [45]

    On normalized mutual information: Measure deriva- tions and properties,

    T. O. Kv ˚alseth, “On normalized mutual information: Measure deriva- tions and properties,”Entropy, vol. 19, no. 11, p. 631, 2017

    work page 2017

  46. [46]

    Conditional independence testing based on a nearest- neighbor estimator of conditional mutual information,

    J. Runge, “Conditional independence testing based on a nearest- neighbor estimator of conditional mutual information,” inProceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR, Playa Blanca, Canary Islands, 2018, pp. 938– 947

    work page 2018

  47. [47]

    Comparative study of techniques for large-scale feature selection,

    F. Ferri, P. Pudil, M. Hatef, and J. Kittler, “Comparative study of techniques for large-scale feature selection,”Machine Intelligence and Pattern Recognition, vol. 16, pp. 403–413, 1994

    work page 1994

  48. [48]

    Conditional like- lihood maximisation: a unifying framework for information theoretic feature selection,

    G. Brown, A. Pocock, M.-J. Zhao, and M. Lujan, “Conditional like- lihood maximisation: a unifying framework for information theoretic feature selection,”Journal of Machine Learning Research, vol. 13, pp. 27–66, 2012

    work page 2012

  49. [49]

    A review of feature selection methods based on mutual information,

    J. R. Vergara and P. A. Est ´evez, “A review of feature selection methods based on mutual information,”Neural Computing and Applications, vol. 24, p. 175–186, 2014

    work page 2014

  50. [50]

    Sparse bayesian learning and the relevance vector machine,

    M. E. Tipping, “Sparse bayesian learning and the relevance vector machine,”Journal of Machine Learning Research, vol. 1, pp. 211– 244, 2001

    work page 2001

  51. [51]

    The relevance vector machine,

    M. E. Tipping, “The relevance vector machine,” inAdvances in Neural Information Processing Systems 12 (NIPS 1999), Denver, US, 1999, pp. 652–658

    work page 1999

  52. [52]

    Robert,The Bayesian Choice: From Decision-Theoretic Founda- tions to Computational Implementation

    C. Robert,The Bayesian Choice: From Decision-Theoretic Founda- tions to Computational Implementation. Berlin, Germany: Springer Science and Business Media, 2007

    work page 2007

  53. [53]

    Experimental design for effi- cient identification of gene regulatory networks using sparse bayesian models,

    F. Steinke, M. Seeger, and K. Tsuda, “Experimental design for effi- cient identification of gene regulatory networks using sparse bayesian models,”BMC Systems Biology, vol. 1, p. 51, 2007

    work page 2007

  54. [54]

    Bayesian learning for neural networks,

    R. M. Neal, “Bayesian learning for neural networks,” inLecture Notes in Statistics. Berlin, Germany: Springer Science and Business Media, LLC, 1996, vol. 118

    work page 1996

  55. [55]

    Design strategies for high power vs. high energy lithium ion cells,

    M. J. Lain, J. Brandon, and E. Kendrick, “Design strategies for high power vs. high energy lithium ion cells,”Batteries, vol. 5, no. 4, p. 64, 2019

    work page 2019

  56. [56]

    Experimental Characterization Data for Battery Modules with Parallel-Connected Cells across Diverse Module-Level State of Health and Cell-to-Cell Variations

    Q. Zhou, D. Stephens, and J. Sun, “Experimental characteriza- tion data for battery modules with parallel-connected cells across diverse module-level state of health and cell-to-cell variations,” arXiv:2604.16769, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  57. [57]

    Dataset for modules with parallel-connected inhomogeneous cells,

    “Dataset for modules with parallel-connected inhomogeneous cells,” 2026. [Online]. Available: https://data.mendeley.com/datasets/ ssrgfmb8vw

    work page 2026

  58. [58]

    Cross-validation,

    D. Berrar, “Cross-validation,”researchgate.net, 2019. 14

    work page 2019