{"id":"W2127913013","doi":"10.4271/2013-01-0698","title":"Development of an Advanced Torque Vectoring Control System for an Electric Vehicle with In-Wheel Motors using Soft Computing Techniques","year":2013,"lang":"en","type":"article","venue":"SAE International journal of alternative powertrains","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive engineering; Torque; Control engineering; Soft computing; Electric vehicle; Engineering; Motor soft starter; Computer science; Mechanical engineering; Physics; Power (physics); Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003179008,0.0001874195,0.0003329869,0.0005497779,0.00004753257,0.00005572445,0.0004993519,0.00005842309,0.000002444436],"category_scores_gemma":[0.00004878153,0.000164539,0.00006039054,0.0001742478,0.00003320202,0.0007837336,0.00002047521,0.0002515423,3.844678e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006339201,"about_ca_system_score_gemma":0.0001133639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005821044,"about_ca_topic_score_gemma":0.00004534878,"domain_scores_codex":[0.9985361,0.00002790683,0.0006560256,0.0001496162,0.0003623672,0.0002679555],"domain_scores_gemma":[0.9986941,0.000102761,0.000370751,0.00009453168,0.0006678103,0.00007009464],"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":[0.0002444968,0.0001992074,0.004275099,0.00008351835,0.0005678418,0.00004574213,0.001633296,0.04089404,0.689231,0.0006908164,0.000003709921,0.2621313],"study_design_scores_gemma":[0.002707416,0.0009672624,0.0146675,0.0008780648,0.00002608055,0.0001757152,0.001303393,0.4197644,0.5587802,0.0002984409,0.00006350286,0.0003680117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8595663,0.0001334908,0.1395757,0.0000148046,0.0002235263,0.0002939176,0.000004895301,0.0001105199,0.00007681863],"genre_scores_gemma":[0.9435269,0.00001203369,0.05625175,0.00001224816,0.0001445543,0.00001469699,0.000002060605,0.000034516,0.000001188815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3788704,"threshold_uncertainty_score":0.6709704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303519792417356,"score_gpt":0.2660695755577721,"score_spread":0.2530343776335985,"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."}}