{"id":"W4381186473","doi":"10.11159/ehst23.110","title":"Online State-Estimation of Lithium-Ion Battery’s Operational States Using the Electrochemical Model Based Nonlinear Kalman Filter","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Energy Harvesting, Storage, and Transfer","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Kalman filter; Extended Kalman filter; Ion; Battery (electricity); Computer science; Nonlinear system; Lithium (medication); State (computer science); Electrochemistry; State of charge; Estimation; Control theory (sociology); Lithium-ion battery; Engineering; Electrode; Algorithm; Chemistry; Physics; Artificial intelligence; Power (physics); Systems engineering; Thermodynamics; Physical chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001584922,0.0001464987,0.0001730816,0.0001609596,0.00005428196,0.00003886142,0.0004766196,0.00006258937,0.00001702802],"category_scores_gemma":[0.0001312265,0.0001056852,0.00005331568,0.0002345124,0.0002187369,0.0002896078,0.00007574134,0.0002015799,1.626541e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003889153,"about_ca_system_score_gemma":0.00005616949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003930446,"about_ca_topic_score_gemma":0.00001476408,"domain_scores_codex":[0.9988775,0.000006070441,0.0003567332,0.0001684294,0.0004184686,0.0001727434],"domain_scores_gemma":[0.9992026,0.00009496499,0.00006682664,0.00008802062,0.0005203719,0.00002720658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003334208,0.00003797044,0.0004552013,0.0001120747,0.00004547562,2.209556e-7,0.0001264866,0.421408,0.5741011,0.003037474,0.00004922272,0.0005934606],"study_design_scores_gemma":[0.0001527682,0.00002462262,0.0002826332,0.000106734,0.000008185308,0.000001845794,0.00004965466,0.6415207,0.3563544,0.001397908,0.00003575059,0.00006474172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8846979,0.00001824523,0.1143046,0.0004711453,0.00004973295,0.00008316335,0.000113089,0.0000657627,0.000196324],"genre_scores_gemma":[0.9929231,0.00007725221,0.006698582,0.00003230117,0.00001951495,0.00001352845,0.00005729902,0.00002141869,0.0001570157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2201128,"threshold_uncertainty_score":0.4309718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03323270581237808,"score_gpt":0.2684904260184507,"score_spread":0.2352577202060726,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}