{"id":"W4413513896","doi":"10.1109/isie62713.2025.11124663","title":"A Multi-Objective Reinforcement Learning Based Energy Management Strategy for Electric Vehicles With Battery and Supercapacitor Integration","year":2025,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Supercapacitor; Reinforcement learning; Battery (electricity); Energy management; Computer science; Automotive engineering; Energy (signal processing); Engineering; Artificial intelligence; Capacitance; Power (physics); Electrode","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":[0.000593883,0.0007744409,0.0005875651,0.0002653813,0.0002967884,0.0005829991,0.0008959771,0.0006259334,0.001496238],"category_scores_gemma":[0.0007771938,0.0002535279,0.0003353076,0.0001511561,0.0004498441,0.0004164365,0.0006489695,0.000637914,0.0002428716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000479102,"about_ca_system_score_gemma":0.0006917604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003240491,"about_ca_topic_score_gemma":0.003471878,"domain_scores_codex":[0.9998073,0.00004540853,0.0000104474,0.00004404199,0.00005726164,0.00003551667],"domain_scores_gemma":[0.9997001,0.0001103478,0.00005016472,0.0000155277,0.0000890662,0.0000348656],"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":[0.0000594616,0.00004975087,0.0004539991,0.00004964859,0.00003814859,0.0001171568,0.00003953739,0.963173,0.003584819,0.004312887,0.0007108785,0.02741069],"study_design_scores_gemma":[0.000006583822,0.0000292907,0.0000511764,0.000002736368,0.000004717615,0.000009398674,0.000003157877,0.9987789,0.0003562014,0.0005179049,0.000237163,0.000002692701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03261657,0.0002131147,0.9590703,0.0002211697,0.00007755299,0.00006586037,0.00002370801,0.0004043516,0.0073074],"genre_scores_gemma":[0.9642184,0.00007931412,0.03169219,0.00009966966,0.00002537216,0.0000868687,0.00002894766,0.0000283294,0.003740891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003240491,"threshold_uncertainty_score":0.006443262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009333530286731697,"score_gpt":0.2109334355434315,"score_spread":0.2015999052566998,"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."}}