{"id":"W2150022023","doi":"10.1109/secon.2012.6196966","title":"Intelligent control system for improving the efficiency of a series hybrid for the EcoCAR 2 challenge","year":2012,"lang":"en","type":"article","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Embry-Riddle Aeronautical University; U.S. Department of Energy","keywords":"Competition (biology); Architecture; Energy consumption; Model predictive control; Hybrid system; Efficient energy use; Control (management); Control system; Engineering; Computer science; Systems architecture; Work (physics); Systems engineering; Artificial intelligence; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004394773,0.000430298,0.0002716424,0.0003633292,0.0004829381,0.000636442,0.0007240781,0.0003434507,0.004402886],"category_scores_gemma":[0.0005436292,0.0001164549,0.000161316,0.000180092,0.0002007525,0.0004460646,0.0004771063,0.0003705632,0.00103882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689915,"about_ca_system_score_gemma":0.0005735849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002243438,"about_ca_topic_score_gemma":0.003672483,"domain_scores_codex":[0.9998273,0.00001714806,0.000009528783,0.00004125719,0.00008382702,0.00002094489],"domain_scores_gemma":[0.9997221,0.00003446354,0.00002273768,0.00003590445,0.0001545943,0.00003021331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009901132,0.0006502338,0.005949785,0.0002582507,0.00007929117,0.0002674579,0.0005434645,0.1133834,0.2397951,0.01550654,0.03128347,0.5912928],"study_design_scores_gemma":[0.0001606531,0.001043966,0.003581508,0.00002178654,0.00007659798,0.0001376634,0.0001161868,0.8862673,0.05400289,0.001640038,0.05290395,0.00004746923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.218801,0.000330371,0.7163244,0.000577595,0.0004182594,0.0005605007,0.0002163042,0.01115546,0.05161599],"genre_scores_gemma":[0.9331029,0.00007967896,0.051298,0.0001165259,0.00005084952,0.0001969611,0.0002305961,0.0001165877,0.01480788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004402886,"threshold_uncertainty_score":0.01472908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01386515776580328,"score_gpt":0.2150742139250926,"score_spread":0.2012090561592894,"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."}}