{"id":"W4200179869","doi":"10.1016/j.enconman.2021.115054","title":"Optimal drivetrain design methodology for enhancing dynamic and energy performances of dual-motor electric vehicles","year":2021,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Drivetrain; Automotive engineering; Particle swarm optimization; Electric vehicle; Envelope (radar); Engineering; Power (physics); Torque; Control theory (sociology); Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001818385,0.0001223853,0.0001991568,0.0001897559,0.00006946499,0.00001315903,0.00006149056,0.00006603477,0.000008944419],"category_scores_gemma":[0.00001387313,0.0001225951,0.00003419033,0.0001726854,0.00004008073,0.00006299987,0.00007290128,0.00004649921,2.451713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002795138,"about_ca_system_score_gemma":0.000009631561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001126724,"about_ca_topic_score_gemma":0.00001368973,"domain_scores_codex":[0.9993232,0.00003953162,0.0001596137,0.0001937267,0.00007117001,0.000212724],"domain_scores_gemma":[0.9996276,0.0001733103,0.0000350702,0.000101955,0.00002838642,0.00003366836],"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":[0.000114392,0.00004605451,0.00004993476,0.0006946311,0.0004629881,0.00005685481,0.0001387844,0.007726313,0.3304636,0.05694649,0.001868498,0.6014315],"study_design_scores_gemma":[0.0008513575,0.0002752775,0.000352491,0.00003871678,0.00008725675,0.00001935116,0.0004724066,0.4097506,0.570945,0.0006931247,0.01626845,0.0002459995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2554507,0.006920653,0.7366726,0.0001319431,0.0001388579,0.0000818761,0.000002107673,0.0002097061,0.0003915335],"genre_scores_gemma":[0.939887,0.02263814,0.03653886,0.00007013718,0.00001190843,0.00003476588,0.000006215622,0.00001509142,0.0007979187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7001338,"threshold_uncertainty_score":0.4999283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510324450914038,"score_gpt":0.2229321209878919,"score_spread":0.2078288764787515,"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."}}