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arxiv: 2606.26347 · v1 · pith:XYOWFLHYnew · submitted 2026-06-24 · 📡 eess.SY · cs.SY

Health feature extraction from battery energy storage system field fault data

Clement Wong , Andrew Weng , Xin Hui Ooi , Zhiwen Wan , Jeesoon Choi , Seung Yoon Yang , Heejun Jin , Jason Siegel
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Anna Stefanopoulou
This is my paper

Pith reviewed 2026-06-26 01:14 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords battery health monitoringfault detectionlithium-ion modulescapacity featuresdV/dQ analysisBESS operational dataparallel cell groupsfield fault data
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The pith

Capacity, degradation rate, and dV/dQ peaks separate faulty parallel cell groups in grid battery modules while resistance does not.

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

The paper develops a framework to extract and calibrate health features from the normal operating data of commercial lithium-ion battery modules in grid storage. Applied to 25 modules each containing 14 series-connected parallel groups, with one group per module confirmed faulty by post-mortem, the method shows that group capacity, capacity fade rate, and dV/dQ peak heights differ significantly between faulty and healthy groups. Internal resistance shows no such separation. The work therefore supplies concrete signatures that can be tracked in the field without extra sensors and questions the common focus on resistance alone for fault detection.

Core claim

Using operational data from 25 grid-connected modules, calibrated group-level capacity, capacity degradation rate, and dV/dQ peak heights each separate the 25 faulty parallel cell groups from the 325 healthy ones at p<0.05, whereas group internal resistance does not, indicating that resistance increase was not the dominant fault signature in this dataset.

What carries the argument

Framework that extracts and calibrates health features (capacity, degradation rate, dV/dQ peaks, resistance) from sparse module-level voltage and current data to label faulty parallel groups.

If this is right

  • Health monitoring of parallel groups inside modules becomes feasible from existing BMS data alone.
  • Resistance-based alerts alone would miss the faults observed here.
  • Feature signatures point to specific degradation mechanisms active under real grid cycling.
  • The same calibration approach can be applied to other BESS fleets without hardware changes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Operators could schedule targeted module replacements or balancing based on these three features rather than blanket resistance thresholds.
  • Extending the calibration to modules with multiple simultaneous faults would test whether the signatures remain additive.
  • The framework offers a route to label training data for machine-learning fault classifiers using only field logs and occasional post-mortems.

Load-bearing premise

Post-mortem examination supplies accurate labels for which parallel groups were faulty and the 25 modules supply enough variation to calibrate the features without lab controls or extra sensors.

What would settle it

Repeating the same statistical tests on a fresh collection of modules whose faulty groups are independently verified would fail to show p<0.05 separation for capacity or dV/dQ features.

Figures

Figures reproduced from arXiv: 2606.26347 by Andrew Weng, Anna Stefanopoulou, Clement Wong, Heejun Jin, Jason Siegel, Jeesoon Choi, Seung Yoon Yang, Xin Hui Ooi, Zhiwen Wan.

Figure 1
Figure 1. Figure 1: Example data of a single battery module. Typical daily usage patterns include 1) [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of key operational parameters across all cycles for the cell groups [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Methodology for estimating the capacity of an exemplary cell group in a module [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Methodology for estimating internal resistance of an exemplary cell group in a [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Methodology for dV /dQ feature extraction of an exemplary cell group in a module using daily operational data. address this, we implemented specific strategies for each peak. For High V dV/dQ peak, we applied the OCP-informed Feature Iden￾tification algorithm presented in [45]. This algorithm fits the voltage and charge throughput data corresponding to High V dV/dQ peak to a physics￾based model to determin… view at source ↗
Figure 6
Figure 6. Figure 6: GP-learned sensitivities of DVA features to [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: GP-learned sensitivities of internal resistances to SOC and current. Plots il [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: GP-learned sensitivities of capacity estimates to [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visual demonstration of Gaussian process calibration on a health feature (e.g. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Calibrated capacity, DVA, and resistances of cell groups in a module (all 14 cell [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Feature normalization to enable cross-module comparability, using High V [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Statistical Significance of Normalized Health Features in Separating Faulty [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of normalized health features for the non-faulty and faulty cell [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
read the original abstract

Health monitoring methods are critical for lithium-ion battery modules connected to the grid to prevent faults that can lead to catastrophic events. However, assessing the health of cells in modules from their operational data presents challenges including variable operating conditions, which directly confound health features, and sparse sensing in the modules, particularly within cells in parallel, which prevents observing critical states of individual cells. Here, we present a framework for extracting and calibrating health features for battery modules from their operational data to identify discriminative features for separating faulty parallel-connected cell groups within the modules. We applied this framework to operational data from 25 commercial grid-connected lithium-ion Battery Energy Storage System (BESS) modules. Each module consisted of 14 series-connected parallel groups, one of which was confirmed as faulty via post-mortem investigation; in total, the dataset included 25 faulty and 325 non-faulty cell groups. A statistical evaluation of these calibrated features demonstrated that group-level capacity, capacity degradation rate, and dV/dQ peak heights separate faulty parallel-connected cell groups within the modules with statistical significance (p<0.05). Conversely, group internal resistance did not (p>0.05), indicating that increased resistance was not a primary characteristic of the faults in this dataset. These findings challenge the exclusive reliance on resistance features for fault detection. The observed feature signatures suggest potential failure mechanisms, furthering the understanding of fault behavior in lithium-ion battery modules during field operation. More importantly, this work demonstrates a framework for robustly monitoring the health of cells in lithium-ion battery modules under real-world operations.

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 / 0 minor

Summary. The manuscript presents a framework for extracting and calibrating health features from operational data of grid-connected lithium-ion BESS modules to identify faulty parallel-connected cell groups. Applied to data from 25 commercial modules (each with 14 series-connected parallel groups, yielding 25 faulty groups confirmed by post-mortem and 325 non-faulty), statistical evaluation shows that calibrated group-level capacity, capacity degradation rate, and dV/dQ peak heights separate faulty from non-faulty groups at p<0.05, while group internal resistance does not (p>0.05). The work claims this challenges exclusive reliance on resistance features and suggests potential failure mechanisms under real-world variable conditions.

Significance. If the post-mortem labels are accurate and the calibration framework demonstrably handles variable operating conditions without additional sensors, the result would be significant as one of the few empirical analyses of field BESS fault data with ground-truth labels. The finding that resistance is not discriminative (while capacity and dV/dQ features are) could shift fault-detection approaches away from resistance-centric methods. The dataset size (350 groups) and real-world origin add value for practical health monitoring in grid storage systems.

major comments (2)
  1. [Abstract (post-mortem labels)] Abstract and post-mortem description: The central claim that capacity, degradation rate, and dV/dQ features separate faulty groups at p<0.05 rests on the 25 post-mortem labels being accurate ground truth. The manuscript states groups were 'confirmed as faulty via post-mortem investigation' but provides no details on the identification procedure, potential for misassignment from module-level data, quantitative validation of label accuracy, or inter-rater reliability. With only 25 positive cases, even modest label error would directly affect the reported significance.
  2. [Abstract (framework description)] Abstract (framework and methods): The abstract describes a 'framework for extracting and calibrating health features' to address variable operating conditions and sparse sensing, yet supplies no specifics on the calibration procedure, data exclusion criteria, or explicit handling of variable conditions. These omissions make it impossible to evaluate whether the p<0.05 separations are robust or confounded, which is load-bearing for the soundness of the statistical claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review and constructive feedback. We address each major comment point-by-point below, with proposed revisions where the manuscript can be strengthened without misrepresenting the available data.

read point-by-point responses

  1. Referee: [Abstract (post-mortem labels)] Abstract and post-mortem description: The central claim that capacity, degradation rate, and dV/dQ features separate faulty groups at p<0.05 rests on the 25 post-mortem labels being accurate ground truth. The manuscript states groups were 'confirmed as faulty via post-mortem investigation' but provides no details on the identification procedure, potential for misassignment from module-level data, quantitative validation of label accuracy, or inter-rater reliability. With only 25 positive cases, even modest label error would directly affect the reported significance.

    Authors: We agree this is a substantive point. The manuscript relies on the 25 post-mortem confirmations as ground truth, and the full text describes the process as module disassembly followed by visual and capacity-based identification of damaged parallel groups. However, the current version does not include quantitative metrics on label accuracy or explicit discussion of misassignment risk from module-level data. We will revise the methods section to expand the post-mortem description with all available procedural details from the field investigation. We cannot provide inter-rater reliability statistics, as the confirmation was performed by a single technical team using objective physical evidence rather than subjective scoring. revision: partial

  2. Referee: [Abstract (framework description)] Abstract (framework and methods): The abstract describes a 'framework for extracting and calibrating health features' to address variable operating conditions and sparse sensing, yet supplies no specifics on the calibration procedure, data exclusion criteria, or explicit handling of variable conditions. These omissions make it impossible to evaluate whether the p<0.05 separations are robust or confounded, which is load-bearing for the soundness of the statistical claims.

    Authors: The abstract is written as a concise summary per journal conventions and therefore omits procedural specifics by design. The full manuscript details the calibration framework, including the normalization steps for variable operating conditions, data exclusion criteria based on SOC and temperature ranges, and the handling of sparse sensing through group-level aggregation, in Sections 3 and 4. The statistical results in Section 5 are derived from these calibrated features. We do not believe changes to the abstract are warranted, as expanding it would violate length constraints while duplicating material already present in the methods. revision: no

Circularity Check

0 steps flagged

No circularity: empirical statistical tests on post-mortem labels

full rationale

The paper applies a feature extraction and calibration framework to operational data from 25 BESS modules (25 faulty groups confirmed by post-mortem, 325 non-faulty) and reports statistical separation via p<0.05 tests on capacity, degradation rate, and dV/dQ peaks. No derivation chain, first-principles prediction, or fitted parameter is presented that reduces by construction to its own inputs; the results are direct empirical comparisons against external ground-truth labels. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear in the reported analysis. This is self-contained data-driven work with no reduction to fitted inputs or self-referential definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides minimal information on modeling choices or background assumptions; no free parameters, invented entities, or non-standard axioms are explicitly stated.

axioms (1)
  • standard math Standard assumptions of statistical significance testing (e.g., appropriate distribution for p-value calculation) hold for the feature comparisons.
    p<0.05 threshold used to claim separation of faulty vs non-faulty groups.

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