{"id":"W4285399940","doi":"10.1149/ma2022-0162413mtgabs","title":"Supercapacitor State of Health Estimation for Vehicular Applications","year":2022,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Supercapacitor; Capacitance; Internal resistance; Robustness (evolution); Capacitor; Computer science; State of health; Observer (physics); Nonlinear system; Voltage; Control theory (sociology); Automotive engineering; Electronic engineering; Power (physics); Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000200729,0.0005011261,0.0004332181,0.0003797415,0.00019433,0.0006412608,0.000520266,0.000425013,0.001102987],"category_scores_gemma":[0.0008901924,0.0001725059,0.0002978136,0.0002316175,0.0001757411,0.0005337218,0.0003728474,0.0004818031,0.0002707395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004125345,"about_ca_system_score_gemma":0.0003596931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005489598,"about_ca_topic_score_gemma":0.004406254,"domain_scores_codex":[0.999861,0.00002153021,0.000008481707,0.00004499151,0.00004853055,0.00001545511],"domain_scores_gemma":[0.9997508,0.00006721258,0.00004746762,0.00003154492,0.00009302051,0.000009932564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002677621,0.00009776613,0.007124993,0.0002618334,0.00007257613,0.0001861734,0.0002264115,0.7000952,0.02808847,0.004378232,0.001800002,0.2574006],"study_design_scores_gemma":[0.000002488844,0.00002767698,0.0008442534,0.000008307047,0.000009019047,0.00002266908,0.00002049153,0.9944754,0.003261243,0.0008264835,0.0004949577,0.000007010255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04059908,0.0002830241,0.9556398,0.00006628094,0.00002965446,0.00003936439,0.0000936877,0.0008011612,0.002448068],"genre_scores_gemma":[0.9794747,0.0002024679,0.01850528,0.00001790788,0.00001419505,0.00003981773,0.0001288356,0.00001783391,0.001598941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005489598,"threshold_uncertainty_score":0.01091528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378277979300213,"score_gpt":0.2375095887026936,"score_spread":0.2237268089096915,"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."}}