{"id":"W4389921776","doi":"10.1109/tvt.2023.3343704","title":"Optimal Energy Management Strategy Based on Driving Pattern Recognition for a Dual-Motor Dual-Source Electric Vehicle","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Dual (grammatical number); Automotive engineering; Electric vehicle; Energy management; Electric motor; Traction motor; Computer science; Energy (signal processing); Engineering; Electrical engineering; Power (physics); Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001589394,0.0003776754,0.0003273927,0.001996417,0.0002783887,0.00004491902,0.0002509734,0.0004786695,0.00002751777],"category_scores_gemma":[0.00000869771,0.0004178619,0.0001945413,0.002001644,0.00006917782,0.00008789869,0.000004169549,0.0005620732,0.0001362374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001712526,"about_ca_system_score_gemma":0.00002031916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001108551,"about_ca_topic_score_gemma":0.00001627196,"domain_scores_codex":[0.9980227,0.00002956109,0.0003592896,0.0005440056,0.0002522714,0.000792188],"domain_scores_gemma":[0.9991392,0.0001449561,0.00005852015,0.0005276248,0.00006442737,0.00006525762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003120282,0.0001459711,0.00001638819,0.00007629998,0.0001698335,0.0001047942,0.000006762144,0.4042114,0.02261056,0.00009352374,0.0004778375,0.5720554],"study_design_scores_gemma":[0.0009452471,0.0007538849,0.0001062869,0.00006838312,0.000087352,0.00002248251,0.00005949207,0.7405082,0.255093,0.0004084516,0.001525425,0.000421729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4121436,0.00005542714,0.5807527,0.0004207415,0.0001656434,0.0004326137,0.00002728558,0.005904407,0.00009754019],"genre_scores_gemma":[0.9970133,0.0003223479,0.0009344253,0.0001016037,0.00003830031,0.001209489,0.00002832002,0.0001234589,0.0002287221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5848697,"threshold_uncertainty_score":0.9998273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065799147366265,"score_gpt":0.2100495739719733,"score_spread":0.1993915824983107,"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."}}