{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A24RM5J3N77MQO5SOWJDPSKMJD","short_pith_number":"pith:A24RM5J3","schema_version":"1.0","canonical_sha256":"06b916753b6ffec83bb2759237c94c48e7c87f7550135fccdbe2d9b7ed27a8c4","source":{"kind":"arxiv","id":"2604.10813","version":2},"attestation_state":"computed","paper":{"title":"System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Farzaneh Barat, Huazhen Fang, Huijeong Kim, Sara Wilson","submitted_at":"2026-04-12T20:47:58Z","abstract_excerpt":"System identification remains an intriguing challenge for lithium-ion batteries, as many models are nonlinear, exhibit multi-physics coupling, and involve a large number of parameters. In this paper, we address this challenge using the ensemble Kalman inversion (EnKI) method for battery system identification. EnKI performs maximum a posteriori parameter estimation through successive local Gaussian approximations, enabling an iterative and incremental search for unknown parameters. The search combines Monte Carlo sampling with Kalman-type updates to evolve an ensemble of samples, thereby offeri"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2604.10813","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-04-12T20:47:58Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"b2d98b5ab1b9defdcc6ad2c401461cede531fb7f167f56b125a0a32bfdf807c3","abstract_canon_sha256":"312d70aa5cfd3aaff4bb73647f5201fd5ab5e7fdd1185d1423ee74f2d7d232ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:10.536378Z","signature_b64":"0Ysv3vyBd0uKCawwme8o4hEqCwoVQNttk9y15Nr3oaEEprN/jDHe2AYDjCP/vCHs5PENQ4QLq5PFa0cSfEQ9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06b916753b6ffec83bb2759237c94c48e7c87f7550135fccdbe2d9b7ed27a8c4","last_reissued_at":"2026-05-26T02:04:10.535544Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:10.535544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Farzaneh Barat, Huazhen Fang, Huijeong Kim, Sara Wilson","submitted_at":"2026-04-12T20:47:58Z","abstract_excerpt":"System identification remains an intriguing challenge for lithium-ion batteries, as many models are nonlinear, exhibit multi-physics coupling, and involve a large number of parameters. In this paper, we address this challenge using the ensemble Kalman inversion (EnKI) method for battery system identification. EnKI performs maximum a posteriori parameter estimation through successive local Gaussian approximations, enabling an iterative and incremental search for unknown parameters. The search combines Monte Carlo sampling with Kalman-type updates to evolve an ensemble of samples, thereby offeri"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The results demonstrate that the proposed approach achieves accurate parameter estimation with rapid iterative convergence, and it shows strong potential for application to other battery models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the local Gaussian approximations in ensemble Kalman inversion remain adequate for the strongly nonlinear and multi-physics battery dynamics, and that the collected data sufficiently excites all relevant parameters without unmodeled disturbances.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Ensemble Kalman inversion achieves accurate parameter estimation with rapid convergence for nonlinear electro-thermal battery models in both simulation and experiments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f947b34adfec847d621ad0e0100bff1eafc693b95cb7ec1743865d94407a0905"},"source":{"id":"2604.10813","kind":"arxiv","version":2},"verdict":{"id":"0a3b50fa-4695-4d5e-a024-33a9246ac2b3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:22:49.656430Z","strongest_claim":"The results demonstrate that the proposed approach achieves accurate parameter estimation with rapid iterative convergence, and it shows strong potential for application to other battery models.","one_line_summary":"Ensemble Kalman inversion achieves accurate parameter estimation with rapid convergence for nonlinear electro-thermal battery models in both simulation and experiments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the local Gaussian approximations in ensemble Kalman inversion remain adequate for the strongly nonlinear and multi-physics battery dynamics, and that the collected data sufficiently excites all relevant parameters without unmodeled disturbances.","pith_extraction_headline":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10813/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2604.10813","created_at":"2026-05-26T02:04:10.535631+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.10813v2","created_at":"2026-05-26T02:04:10.535631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10813","created_at":"2026-05-26T02:04:10.535631+00:00"},{"alias_kind":"pith_short_12","alias_value":"A24RM5J3N77M","created_at":"2026-05-26T02:04:10.535631+00:00"},{"alias_kind":"pith_short_16","alias_value":"A24RM5J3N77MQO5S","created_at":"2026-05-26T02:04:10.535631+00:00"},{"alias_kind":"pith_short_8","alias_value":"A24RM5J3","created_at":"2026-05-26T02:04:10.535631+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD","json":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD.json","graph_json":"https://pith.science/api/pith-number/A24RM5J3N77MQO5SOWJDPSKMJD/graph.json","events_json":"https://pith.science/api/pith-number/A24RM5J3N77MQO5SOWJDPSKMJD/events.json","paper":"https://pith.science/paper/A24RM5J3"},"agent_actions":{"view_html":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD","download_json":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD.json","view_paper":"https://pith.science/paper/A24RM5J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.10813&json=true","fetch_graph":"https://pith.science/api/pith-number/A24RM5J3N77MQO5SOWJDPSKMJD/graph.json","fetch_events":"https://pith.science/api/pith-number/A24RM5J3N77MQO5SOWJDPSKMJD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD/action/storage_attestation","attest_author":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD/action/author_attestation","sign_citation":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD/action/citation_signature","submit_replication":"https://pith.science/pith/A24RM5J3N77MQO5SOWJDPSKMJD/action/replication_record"}},"created_at":"2026-05-26T02:04:10.535631+00:00","updated_at":"2026-05-26T02:04:10.535631+00:00"}