{"id":"W2145006691","doi":"10.1109/ias.1997.628973","title":"Efficiency optimization of EV drive using fuzzy logic","year":2002,"lang":"en","type":"article","venue":"","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vector control; Control theory (sociology); Torque; Fuzzy logic; Fuzzy control system; Direct torque control; Computer science; Propulsion; Rotor (electric); Battery (electricity); Induction motor; Control engineering; Power (physics); Control system; Electric vehicle; Engineering; Control (management); Voltage; Physics; Electrical engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000334726,0.0002760984,0.0003252262,0.0002444618,0.0002293002,0.0005219263,0.0003297061,0.0003033162,0.0008799474],"category_scores_gemma":[0.0004978893,0.0001472223,0.0002967443,0.0001479806,0.0002329405,0.000374905,0.0001432443,0.0001983505,0.0001090711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005805229,"about_ca_system_score_gemma":0.0003365318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004173055,"about_ca_topic_score_gemma":0.001990106,"domain_scores_codex":[0.9998897,0.0000279623,0.00000473173,0.00001273649,0.00004913682,0.00001566244],"domain_scores_gemma":[0.9998966,0.0000459632,0.00001475824,0.000006802486,0.00003199139,0.000003871654],"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":[0.00003582413,0.00001657701,0.0002833372,0.00001980883,0.000007733648,0.00002696329,0.00002351027,0.9839334,0.00236411,0.004008072,0.00009134953,0.009189266],"study_design_scores_gemma":[0.000005259526,0.00001770032,0.00008010415,0.000001752715,0.000002611122,0.000004783705,0.000003166652,0.9982548,0.0006896775,0.0007112062,0.0002271496,0.000001864078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2772947,0.000310706,0.7022282,0.0001486933,0.00002267307,0.00006067693,0.00006756772,0.000388888,0.01947793],"genre_scores_gemma":[0.984848,0.00007834111,0.01369303,0.000009468998,0.000003063109,0.00002295497,0.00002117425,0.00001028458,0.001313747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004173055,"threshold_uncertainty_score":0.008297503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185670594795447,"score_gpt":0.2045889406679171,"score_spread":0.1860218811883724,"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."}}