{"id":"W4403999852","doi":"10.1016/j.jpowsour.2024.235450","title":"State-of-health estimation for lithium-ion batteries based on electrochemical impedance spectroscopy measurements combined with unscented Kalman filter","year":2024,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario","keywords":"Kalman filter; Dielectric spectroscopy; Lithium (medication); Extended Kalman filter; Ion; Electrochemistry; Electrical impedance; State (computer science); Spectroscopy; State of health; Materials science; Chemistry; Computer science; Battery (electricity); Electrical engineering; Engineering; Physics; Electrode; Power (physics); Algorithm; Medicine; Thermodynamics; Physical chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003773326,0.0001673199,0.0002980535,0.0003000469,0.00004377238,0.00005175626,0.0002085357,0.00004759005,0.00001766473],"category_scores_gemma":[0.00009208857,0.0001261405,0.00007382638,0.0002496298,0.00006651159,0.0001931315,0.00001317591,0.0003637403,0.000002122795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002560634,"about_ca_system_score_gemma":0.00008242008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.226267e-7,"about_ca_topic_score_gemma":0.000001636133,"domain_scores_codex":[0.9985904,0.00003296396,0.0004429382,0.0001351752,0.0004764169,0.0003220893],"domain_scores_gemma":[0.9993548,0.0001395181,0.0001526395,0.0001597917,0.0001369167,0.00005629804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002578256,0.000249952,0.001940806,0.001857877,0.0004232727,0.000029127,0.001046827,0.08280595,0.888962,0.00005294471,0.004934543,0.01511841],"study_design_scores_gemma":[0.0009504726,0.003728774,0.0009311804,0.0009714835,0.00001730898,0.00001631329,0.00008209833,0.06707297,0.9243906,0.0005008293,0.001161073,0.000176927],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.532435,0.0005408705,0.4652023,0.001208546,0.0001939832,0.0002367545,0.00001042503,0.0001356042,0.00003652202],"genre_scores_gemma":[0.9819298,0.00003451771,0.01788069,0.00005266565,0.00002685111,0.00001118652,0.000006606152,0.00004197563,0.00001571951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4494947,"threshold_uncertainty_score":0.5143861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809651323661908,"score_gpt":0.2912749433975367,"score_spread":0.2731784301609176,"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."}}