{"id":"W2167253572","doi":"10.1109/tvt.2007.896967","title":"Comprehensive Efficiency Modeling of Electric Traction Motor Drives for Hybrid Electric Vehicle Propulsion Applications","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Traction motor; Automotive engineering; Propulsion; Electric vehicle; Engineering; Induction motor; Inverter; Drivetrain; Torque; Single-phase electric power; Motor drive; Stator; Electric motor; Traction (geology); AC motor; Brushed DC electric motor; Electrical engineering; Voltage; Power (physics); Power factor; Mechanical engineering; Aerospace engineering; Physics","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":[0.0003098755,0.0006134406,0.0005427726,0.000412684,0.0002167996,0.0006048261,0.0007818185,0.0004565043,0.001741178],"category_scores_gemma":[0.0006803932,0.000234806,0.0004899799,0.0003798391,0.0001423292,0.0007387523,0.0002024481,0.0002508084,0.0006698652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005610117,"about_ca_system_score_gemma":0.0003554884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00504772,"about_ca_topic_score_gemma":0.003282223,"domain_scores_codex":[0.9997949,0.00003462587,0.000009903822,0.00002733036,0.0001136252,0.00001966892],"domain_scores_gemma":[0.9998503,0.00004524894,0.00001722853,0.00001998495,0.00006300883,0.000004189968],"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":[0.00002752826,0.00002393903,0.0005792274,0.00002986785,0.00001272988,0.0000412605,0.00002785565,0.98186,0.002345416,0.001852641,0.0004022831,0.01279734],"study_design_scores_gemma":[0.000002251007,0.00001752218,0.0005163916,0.000002689428,0.000004565718,0.0000136657,0.000006366,0.9972947,0.0007514683,0.0005560176,0.0008316846,0.00000262388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2121553,0.0007259608,0.7495738,0.0001413186,0.00002557388,0.0001379812,0.0006052697,0.0008740164,0.03576078],"genre_scores_gemma":[0.9739974,0.0004163721,0.01477848,0.00001474777,0.000009535614,0.0001375202,0.0003980423,0.00009206488,0.01015589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00504772,"threshold_uncertainty_score":0.01003665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009774185807775362,"score_gpt":0.2299394420485176,"score_spread":0.2201652562407423,"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."}}