{"id":"W6890105150","doi":"10.34746/epe2025-0342","title":"Energy Management in Hybrid Energy Storage Systems for Electric Vehicles: A Reinforcement Learning Approach with Python-Simulink Integration","year":2025,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Reinforcement learning; Energy management; Python (programming language); Energy storage; Efficient energy use; Energy management system; Energy (signal processing); Power management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004499948,0.0004860842,0.0003894052,0.0001884669,0.0002665978,0.000603714,0.0007597734,0.0004368732,0.003859323],"category_scores_gemma":[0.0007591477,0.0002055991,0.0003693272,0.0001599664,0.0003743694,0.0004177695,0.0005328861,0.0007201061,0.0005130518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002947657,"about_ca_system_score_gemma":0.0007198862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004201741,"about_ca_topic_score_gemma":0.00302848,"domain_scores_codex":[0.9999055,0.00003228126,0.00000595594,0.0000134853,0.0000325988,0.00001030051],"domain_scores_gemma":[0.9997367,0.000132105,0.00003288159,0.00002302139,0.0000607838,0.00001445592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005004084,0.0000509641,0.0004447685,0.0001031503,0.00001720477,0.00005194227,0.00004505018,0.9760946,0.002790914,0.003747282,0.0004682897,0.01613571],"study_design_scores_gemma":[0.000008521243,0.00001675373,0.00005910241,0.00000483318,0.000003417558,0.000008552817,0.000004352175,0.9976565,0.0009743369,0.0006310634,0.0006303918,0.000002125437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03358641,0.0001263575,0.9534936,0.0001421609,0.00004188333,0.00008845224,0.0001157918,0.003127726,0.009277484],"genre_scores_gemma":[0.8366411,0.000189513,0.1567916,0.00006307689,0.00001613059,0.0003392988,0.0001423424,0.000313233,0.005503711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004201741,"threshold_uncertainty_score":0.01291066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006413848619261879,"score_gpt":0.188760436929253,"score_spread":0.1823465883099911,"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."}}