{"id":"W2739334600","doi":"10.1109/tvt.2017.2728823","title":"Real-Time Backstepping Control for Fuel Cell Vehicle Using Supercapacitors","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Backstepping; Supercapacitor; Stability (learning theory); Control engineering; Control (management); Key (lock); Engineering; Control theory (sociology); Fuel cells; Computer science; Control system; Nonlinear system; Adaptive control; Capacitance; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000145188,0.000320695,0.0004266073,0.0004975922,0.0007124156,0.00008355057,0.0006766621,0.0007894262,0.00002492898],"category_scores_gemma":[0.00001692742,0.0003449191,0.0001972701,0.000222127,0.0003025395,0.0002319782,0.000003549852,0.0007331258,0.00007511929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001774301,"about_ca_system_score_gemma":0.00003187906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004439438,"about_ca_topic_score_gemma":0.0000139051,"domain_scores_codex":[0.9985047,0.00001532258,0.0003137894,0.0003871414,0.0001438376,0.0006351863],"domain_scores_gemma":[0.9986671,0.00008049475,0.00008049498,0.001016627,0.00009054605,0.0000647321],"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":[0.00005691269,0.0001490911,0.0001759548,0.0002690976,0.0002619124,0.0000375107,0.00003790491,0.07111167,0.9063001,0.0002886027,0.0002545226,0.0210567],"study_design_scores_gemma":[0.001791787,0.0002449545,0.00005687633,0.00005323465,0.0001464708,0.00003642559,0.00006797707,0.4050203,0.5897982,0.001125746,0.001153386,0.0005045958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6335501,0.0002615561,0.3615251,0.0004268401,0.0004135635,0.0005591479,0.00004761318,0.002674498,0.0005416289],"genre_scores_gemma":[0.9938577,0.0004282868,0.005258261,0.00002234768,0.00005232803,0.0001522808,0.000001477712,0.00009196604,0.0001353277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3603076,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208537973072711,"score_gpt":0.2260559739628434,"score_spread":0.2139705942321163,"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."}}