{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499114,0.0003475618,0.0002918408,0.0002105901,0.0002733975,0.0004670283,0.000542069,0.0003153912,0.002242744],"category_scores_gemma":[0.0003207842,0.0001447272,0.0001750141,0.000145026,0.000150049,0.0002564078,0.0002106022,0.0003996034,0.0005492915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002453526,"about_ca_system_score_gemma":0.0007421146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003388487,"about_ca_topic_score_gemma":0.003730754,"domain_scores_codex":[0.999885,0.00001196634,0.00000828154,0.000023395,0.00005951444,0.00001191498],"domain_scores_gemma":[0.9998437,0.00002224922,0.00001556066,0.000012561,0.00009161763,0.00001411762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002210052,0.000260892,0.001767279,0.0003321996,0.00006747436,0.0003691513,0.0002362629,0.16744,0.2653718,0.006970053,0.004124593,0.5528393],"study_design_scores_gemma":[0.00008246487,0.0005290947,0.001396387,0.00002470704,0.00004224735,0.0001657143,0.00003976006,0.9326245,0.04827823,0.0005840999,0.01620481,0.0000280948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04159041,0.0001355462,0.9494666,0.0001306426,0.0001600663,0.0002408799,0.00005349961,0.00219822,0.006024159],"genre_scores_gemma":[0.6630775,0.0002357117,0.3221609,0.0001110215,0.00005214803,0.000303787,0.0001864887,0.00006880246,0.0138036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003388487,"threshold_uncertainty_score":0.007502735,"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."}}