{"id":"W4409327549","doi":"10.1109/tte.2025.3559705","title":"Comparative Study of Embedded Energy Management Methods Based on Machine Learning for Dual-Source Electric Vehicles","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Trường Đại học Bách Khoa Hà Nội","keywords":"Dual (grammatical number); Computer science; Automotive engineering; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002529657,0.0002457114,0.0003374686,0.0009709962,0.000218828,0.00001707988,0.0001451979,0.00011936,0.000006997506],"category_scores_gemma":[0.000003881405,0.0002675275,0.0001129706,0.001539023,0.00002396039,0.00007730783,2.873055e-8,0.0003317953,0.000001424811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253606,"about_ca_system_score_gemma":0.00002635652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003195746,"about_ca_topic_score_gemma":0.00006489168,"domain_scores_codex":[0.9985804,0.0001144235,0.0005080283,0.0003382925,0.000203662,0.0002551968],"domain_scores_gemma":[0.9991131,0.0003599963,0.0001102541,0.0002682912,0.0001183678,0.00002994425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003384155,0.0007503192,0.00003806126,0.00009921991,0.0002557633,6.240333e-7,0.0003761438,0.758204,0.1164826,0.0009086647,0.00003753034,0.1225086],"study_design_scores_gemma":[0.001120163,0.00065061,0.001227709,0.00002231623,0.0001871048,1.438413e-7,0.000224583,0.3653789,0.630691,0.0001559378,0.000185089,0.0001564804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1329481,0.0001341531,0.8652025,0.00003935919,0.0000703628,0.0007676585,0.000009783128,0.0006337622,0.0001943656],"genre_scores_gemma":[0.9950565,0.0001661683,0.00358278,0.00003099538,0.00000588962,0.0007243811,0.0000523548,0.00003279331,0.0003481088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8621084,"threshold_uncertainty_score":0.9999777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636635376081378,"score_gpt":0.2871568688390111,"score_spread":0.2707905150781973,"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."}}