Paper Citation Record · LEDGER

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation

As of 23 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.23482.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.23482 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T21:12:25.282023Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation cd3c8109-b6f1-4bfb-8c4a-f9a478e407f3 · outbound

This paper cites The development and future of lithium ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation The development and future of lithium ion batteries

Reference 1

Resolution: verified exact; doi, observed 2026-07-30T21:16:27.323880Z

Source-reported events for the cited work

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Observation 19062b33-339d-4279-aa47-2a8969f1302a · outbound

This paper cites Critical review of state of health estimation methods of Li-ion batteries for real applications.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Critical review of state of health estimation methods of Li-ion batteries for real applications

Reference 2

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Source-reported events for the cited work

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Observation dfef55f3-143c-48a5-a167-e787163e11db · outbound

This paper cites A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations

Reference 3

Resolution: verified exact; doi, observed 2026-07-30T21:16:27.198835Z

Source-reported events for the cited work

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Observation 7695f18b-0d97-4920-af86-2756307ee290 · outbound

This paper cites Battery lifetime prognostics.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Battery lifetime prognostics

Reference 4

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Observation 695054cd-6fd8-4fc6-aa85-abc15f3bf5af · outbound

This paper cites A review on machinery diagnostics and prognostics implementing condition -based maintenance.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A review on machinery diagnostics and prognostics implementing condition -based maintenance

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.155786Z

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Observation f90f7670-fed9-4eba-9855-c2f08a063538 · outbound

This paper cites Charging protocols for lithium-ion batteries and their impact on cycle life —an experimental study with different 18650 high-power cells.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Charging protocols for lithium-ion batteries and their impact on cycle life —an experimental study with different 18650 high-power cells

Reference 6

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.995226Z

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Observation d98b9d9a-0612-4549-b1da-0f1ac289a683 · outbound

This paper cites Battery Management Systems.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Battery Management Systems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.167748Z

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Observation a79848c4-cef8-418b-8f90-15986817d0d2 · outbound

This paper cites Identify capacity fading mechanism in a commercial LiFePO4 cell.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Identify capacity fading mechanism in a commercial LiFePO4 cell

Reference 8

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Observation fb361c52-fcf9-4a10-859b-82bd75c87c37 · outbound

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

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation On-board state of health monitoring of lithium -ion batteries using incremental capacity analysis with support vector regression

Reference 9

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.553923Z

Source-reported events for the cited work

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Observation e98199ce-ebdb-43e6-9a01-c95b84c55494 · outbound

This paper cites Gaussian process regression for forecasting battery state of h ealth.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Gaussian process regression for forecasting battery state of h ealth

Reference 10

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.393968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ebc2dd4-0a05-4f9d-9b4c-9ab0fe02f3da · outbound

This paper cites Data-driven prediction of battery cycle life before capa city degradation.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Data-driven prediction of battery cycle life before capa city degradation

Reference 11

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Observation 4150971b-530b-49a1-9fa5-586060b95bfb · outbound

This paper cites Long short-term memory recurrent neural network for remaining useful life prediction of lithium - ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Long short-term memory recurrent neural network for remaining useful life prediction of lithium - ion batteries

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.195847Z

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Observation 042e991a-b617-4caa-8cf0-c60183f8a265 · outbound

This paper cites A deep learning method for online capacity estimation of lithium -ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A deep learning method for online capacity estimation of lithium -ion batteries

Reference 13

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Observation 339d2ba6-470f-478d-8aaf-477fec69e122 · outbound

This paper cites Convolutional Neural Network - Gated Recurrent Unit combined with Error Correction for Lithium Battery State of Health Estimation.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Convolutional Neural Network - Gated Recurrent Unit combined with Error Correction for Lithium Battery State of Health Estimation

Reference 14

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.219927Z

Source-reported events for the cited work

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Observation be306624-d601-4097-96e7-675cba32f679 · outbound

This paper cites Application of DBN -based KRL S method for RUL prediction of lithium -ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Application of DBN -based KRL S method for RUL prediction of lithium -ion batteries

Reference 15

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.024294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50e44651-bd23-4711-8dcb-42e4df76d901 · outbound

This paper cites A modified TimeGAN-based data augmentation ap proach for the state of health prediction of Lithium-Ion Batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A modified TimeGAN-based data augmentation ap proach for the state of health prediction of Lithium-Ion Batteries

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.214725Z

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Observation 5c14b668-e2d6-457a-8749-1bebbd89ef4c · outbound

This paper cites Machine learning pipeline for battery state -of-health estimati on.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Machine learning pipeline for battery state -of-health estimati on

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.219242Z

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Observation 171cb3b7-1193-4f98-88c5-28395d9f8102 · outbound

This paper cites Predicting the state of charge and health of batter ies using data-driven machine learni ng.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Predicting the state of charge and health of batter ies using data-driven machine learni ng

Reference 18

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.882302Z

Source-reported events for the cited work

correction dated 2020-06-15. Source: crossref record 10.1038/s42256-020-0191-4->10.1038/s42256-020-0156-7:correction, observed 2026-07-11T02:59:54.89275+00:00. This notice travels one citation hop only.

Observation 85ca5ee7-8d32-486a-a458-e599f8f201ba · outbound

This paper cites A quick on-line state of health estimation method for Li -ion battery with incremental capacity curves processed by Gaussian f ilter.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A quick on-line state of health estimation method for Li -ion battery with incremental capacity curves processed by Gaussian f ilter

Reference 19

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.737851Z

Source-reported events for the cited work

correction dated 2018-05-23. Source: crossref record 10.1016/j.jpowsour.2018.05.035->10.1016/j.jpowsour.2017.10.092:correction, observed 2026-07-11T03:16:24.985091+00:00. This notice travels one citation hop only.

Observation 3e780bad-b023-4acd-a55a-cec82b20641a · outbound

This paper cites Multi -kernel relevance vector machine with parameter optimization for cycling aging prediction of lithium-ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Multi -kernel relevance vector machine with parameter optimization for cycling aging prediction of lithium-ion batteries

Reference 20

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Observation 8bcee8ed-d2f2-4e66-93a5-947a802aa6ea · outbound

This paper cites Prediction of remaining useful life for lithium -ion battery with multiple health indicators.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Prediction of remaining useful life for lithium -ion battery with multiple health indicators

Reference 21

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.646679Z

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Observation 2f36ed80-c4e9-466d-87bf-70823240c41c · outbound

This paper cites Useful energy prediction mo del of a Lithium -ion cell operating on various duty cycles.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Useful energy prediction mo del of a Lithium -ion cell operating on various duty cycles

Reference 22

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.559250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a803a2e7-cf4b-49ea-af5e-146858dc69bc · outbound

This paper cites A study of the relationship betwe en coulombic efficiency and capacity degradation of commercial lithium-ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A study of the relationship betwe en coulombic efficiency and capacity degradation of commercial lithium-ion batteries

Reference 23

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.377546Z

Source-reported events for the cited work

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Observation e6f3c5ae-0bc8-467c-aef9-dbf7784712e2 · outbound

This paper cites Online state of health estimation for lithium-ion batteries based on support vector machine.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Online state of health estimation for lithium-ion batteries based on support vector machine

Reference 24

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.307455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b301a2b-08f8-47e7-9dba-1b24a2d591f6 · outbound

This paper cites Differential current in constant -voltage charging mode: a novel tool for state -of-health and state -of-charge estimation of lithium-ion batteries.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Differential current in constant -voltage charging mode: a novel tool for state -of-health and state -of-charge estimation of lithium-ion batteries

Reference 25

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This paper cites Battery data set, NASA Ames Prognostics Data Repository.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Battery data set, NASA Ames Prognostics Data Repository

Reference 26

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Observation a4d753c7-75e1-47bf-93c2-02b29197cce7 · outbound

This paper cites LightGBM: a highly efficient gradient boosting decision tree.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation LightGBM: a highly efficient gradient boosting decision tree

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.267491Z

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Observation 2fa2e5f3-0e41-4e87-8fac-2b05e1339761 · outbound

This paper cites A unified approach to interpreting model predictions.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A unified approach to interpreting model predictions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-30T21:12:25.272782Z

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Observation c12194c0-d68d-48ae-b436-552a4e0f4aa2 · outbound

This paper cites A data-driven predictive maintenance strategy based on accurate failure prognostics.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation A data-driven predictive maintenance strategy based on accurate failure prognostics

Reference 29

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.202070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e59f79d5-4cba-4e6f-b7cd-865984a78c2e · outbound

This paper cites Application of machine learning and rough set theory in lean maintenance decision support system development.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Application of machine learning and rough set theory in lean maintenance decision support system development

Reference 30

Resolution: verified exact; doi, observed 2026-07-30T21:16:25.104606Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 39ca4d32-bcdb-40ef-92ce-f3bec215fd59 · outbound

This paper cites an unresolved cited work.

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation Unresolved cited work

Reference 549

Resolution: verified exact; doi, observed 2026-07-30T21:16:26.742942Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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