Paper Citation Record · LEDGER

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2506.19146.

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

pith.paper-citation-record.v1
2506.19146 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:42:03.590223Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T17:23:09.724774Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:24:56.978381Z

Reference resolution

19 of 19 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f2dbf4e-fdbc-46d2-8be7-0cb29c0e037e · outbound

This paper cites Parametrization of physics- based battery models from input–output data: A review of method- ology and current research,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Parametrization of physics- based battery models from input–output data: A review of method- ology and current research,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.807308Z

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Observation e565c5ac-440e-4fd2-9480-ff2642a629c3 · outbound

This paper cites Global sensitivity methods for design of experiments in lithium-ion battery context,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Global sensitivity methods for design of experiments in lithium-ion battery context,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.797563Z

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Observation b60fc510-b0fe-4f70-b20e-decc27d59b29 · outbound

This paper cites Development of experimental techniques for pa- rameterization of multi-scale lithium-ion battery models,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Development of experimental techniques for pa- rameterization of multi-scale lithium-ion battery models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.787935Z

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Observation 261839ce-8943-4ccb-88af-4c513194e6e1 · outbound

This paper cites Modeling and estimation for advanced battery management,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Modeling and estimation for advanced battery management,

Reference 4

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Observation 7159e00d-6e08-4fed-b4db-6e3d1137310e · outbound

This paper cites New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.766369Z

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Observation 83e7735f-4951-4f96-95cc-72258a1f2835 · outbound

This paper cites Optimal ex- perimental design for parameterization of an electrochemical lithium- ion battery model,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Optimal ex- perimental design for parameterization of an electrochemical lithium- ion battery model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.755457Z

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Observation 6d77fb66-3b1e-41ab-96ae-f17c1fae05bf · outbound

This paper cites Optimization of current excitation for identification of battery electrochemical parameters based on analytic sensitivity expression,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Optimization of current excitation for identification of battery electrochemical parameters based on analytic sensitivity expression,

Reference 7

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Observation f99a6e83-058a-4f12-a6f0-3eb00d1a6614 · outbound

This paper cites Input excitation optimization for estimating battery electrochemical parameters using reinforcement learning,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Input excitation optimization for estimating battery electrochemical parameters using reinforcement learning,

Reference 8

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Source-reported events for the cited work

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Observation f53c0624-6c95-4648-acc5-78e4bb1149bd · outbound

This paper cites Reinforcement learning of optimal input excitation for parameter estimation with application to li-ion battery,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Reinforcement learning of optimal input excitation for parameter estimation with application to li-ion battery,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.722459Z

Source-reported events for the cited work

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Observation 179fb506-bd8f-45bd-8ac7-489be196da5e · outbound

This paper cites Excitation optimization for estimat- ing battery health parameters using reinforcement learning considering information content and bias,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Excitation optimization for estimat- ing battery health parameters using reinforcement learning considering information content and bias,

Reference 10

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Source-reported events for the cited work

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Observation 753fd801-4ab7-4994-b9d2-98c39ab0eeb2 · outbound

This paper cites Deep reinforcement learning versus evolution strategies: A comparative survey,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Deep reinforcement learning versus evolution strategies: A comparative survey,

Reference 11

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Source-reported events for the cited work

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Observation b96e8a05-8466-424d-9600-8f5f2cda38ed · outbound

This paper cites Addressing function approxi- mation error in actor-critic methods,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Addressing function approxi- mation error in actor-critic methods,

Reference 12

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Observation b33b7cb6-0b0f-4ba7-8b01-0cc6b7df242c · outbound

This paper cites Improving aging identifiability of lithium-ion batteries using deep reinforcement learning,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Improving aging identifiability of lithium-ion batteries using deep reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.682799Z

Source-reported events for the cited work

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Observation ab1bdbb4-29c0-4b3c-9267-90b0da6ea95a · outbound

This paper cites Physics-based equivalent circuit model for lithium-ion cells via reduction and ap- proximation of electrochemical model,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Physics-based equivalent circuit model for lithium-ion cells via reduction and ap- proximation of electrochemical model,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.671475Z

Source-reported events for the cited work

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Observation b44dd560-e566-464f-8ba7-20fae65d38c1 · outbound

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Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Unresolved cited work

Reference 15

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Observation a0ad5bda-db12-4db4-91c6-ee0fec7b7cf9 · outbound

This paper cites Geometry of the cramer-rao bound,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Geometry of the cramer-rao bound,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.654297Z

Source-reported events for the cited work

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Observation 5a96b792-f2d6-46aa-9d91-118c81ca35ca · outbound

This paper cites Parameter identification for electrochem- ical models of lithium-ion batteries using sensitivity analysis,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Parameter identification for electrochem- ical models of lithium-ion batteries using sensitivity analysis,

Reference 17

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Observation c0193be7-3bfe-438e-a5b2-eb04f9d07ee4 · outbound

This paper cites Electro- thermal battery model identification for automotive applications,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Electro- thermal battery model identification for automotive applications,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.633506Z

Source-reported events for the cited work

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Observation f4e9cc2a-4772-4fd4-a7dd-42a49a10d257 · outbound

This paper cites Parameter identification for electrochemical models of lithium-ion batteries using bayesian optimization,.

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning Parameter identification for electrochemical models of lithium-ion batteries using bayesian optimization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:42:03.621668Z

Source-reported events for the cited work

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Pith citing papers

Observation 494bd7df-7edc-4275-bb9e-02172d0836d2 · inbound

RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses cites this paper.

RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning

Reference 9

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Observation 74a9e0ea-ee8c-449a-be65-1264748e6a15 · inbound

RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses cites this paper.

Reference 9

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Observation 311d71c0-64dd-44eb-bfdb-8884250f0f82 · inbound

RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses cites this paper.

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.