{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:A24RM5J3N77MQO5SOWJDPSKMJD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"312d70aa5cfd3aaff4bb73647f5201fd5ab5e7fdd1185d1423ee74f2d7d232ad","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-04-12T20:47:58Z","title_canon_sha256":"b2d98b5ab1b9defdcc6ad2c401461cede531fb7f167f56b125a0a32bfdf807c3"},"schema_version":"1.0","source":{"id":"2604.10813","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10813","created_at":"2026-05-26T02:04:10Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10813v2","created_at":"2026-05-26T02:04:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10813","created_at":"2026-05-26T02:04:10Z"},{"alias_kind":"pith_short_12","alias_value":"A24RM5J3N77M","created_at":"2026-05-26T02:04:10Z"},{"alias_kind":"pith_short_16","alias_value":"A24RM5J3N77MQO5S","created_at":"2026-05-26T02:04:10Z"},{"alias_kind":"pith_short_8","alias_value":"A24RM5J3","created_at":"2026-05-26T02:04:10Z"}],"graph_snapshots":[{"event_id":"sha256:97e0fd9bb6de41d5082e972a9b7a4ae433a285cab5bd953643cb11d05f18a527","target":"graph","created_at":"2026-05-26T02:04:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Ensemble Kalman inversion achieves accurate parameter estimation with rapid convergence for nonlinear electro-thermal battery models in both simulation and experiments."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence."}],"snapshot_sha256":"f947b34adfec847d621ad0e0100bff1eafc693b95cb7ec1743865d94407a0905"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.10813/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Farzaneh Barat, Huazhen Fang, Huijeong Kim, Sara Wilson","cross_cats":["cs.SY"],"headline":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-04-12T20:47:58Z","title":"System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.10813","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T15:22:49.656430Z","id":"0a3b50fa-4695-4d5e-a024-33a9246ac2b3","model_set":{"reader":"grok-4.3"},"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","pith_extraction_headline":"Ensemble Kalman inversion identifies parameters in lithium-ion battery models with accurate results and rapid convergence.","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.","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."}},"verdict_id":"0a3b50fa-4695-4d5e-a024-33a9246ac2b3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4f3c81d262af5bcbb1f429245593da94bf64d68d811726f0c1e00255af8d7c6d","target":"record","created_at":"2026-05-26T02:04:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"312d70aa5cfd3aaff4bb73647f5201fd5ab5e7fdd1185d1423ee74f2d7d232ad","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-04-12T20:47:58Z","title_canon_sha256":"b2d98b5ab1b9defdcc6ad2c401461cede531fb7f167f56b125a0a32bfdf807c3"},"schema_version":"1.0","source":{"id":"2604.10813","kind":"arxiv","version":2}},"canonical_sha256":"06b916753b6ffec83bb2759237c94c48e7c87f7550135fccdbe2d9b7ed27a8c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06b916753b6ffec83bb2759237c94c48e7c87f7550135fccdbe2d9b7ed27a8c4","first_computed_at":"2026-05-26T02:04:10.535544Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:04:10.535544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0Ysv3vyBd0uKCawwme8o4hEqCwoVQNttk9y15Nr3oaEEprN/jDHe2AYDjCP/vCHs5PENQ4QLq5PFa0cSfEQ9Dg==","signature_status":"signed_v1","signed_at":"2026-05-26T02:04:10.536378Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.10813","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f3c81d262af5bcbb1f429245593da94bf64d68d811726f0c1e00255af8d7c6d","sha256:97e0fd9bb6de41d5082e972a9b7a4ae433a285cab5bd953643cb11d05f18a527"],"state_sha256":"674be6edfc92caa089b751fdbf6272c17ae9352118d26d6281afef966f323484"}