{"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"}