{"id":"W4404563814","doi":"10.1109/vppc63154.2024.10755412","title":"Development of an Energy Management Strategy for Multi-Stack Fuel Cell Hybrid Electric Vehicle Using Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Trois-Rivières","funders":"","keywords":"Reinforcement learning; Stack (abstract data type); Electric vehicle; Fuel cells; Energy management; Computer science; Reinforcement; Automotive engineering; Energy (signal processing); Engineering; Artificial intelligence; Power (physics); Structural engineering; Chemical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003874436,0.0004363706,0.0003896923,0.0001891306,0.0002282632,0.0004349277,0.0006279001,0.0005444003,0.001349841],"category_scores_gemma":[0.0005549827,0.0001950566,0.000266309,0.0001206999,0.0002683966,0.000478536,0.0005722667,0.0006001787,0.0001743266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004874353,"about_ca_system_score_gemma":0.0008507298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005690546,"about_ca_topic_score_gemma":0.00595601,"domain_scores_codex":[0.9999067,0.00001693581,0.000004862721,0.0000229319,0.0000270107,0.00002157541],"domain_scores_gemma":[0.9998316,0.0000492854,0.00002808481,0.000008867806,0.00006512016,0.00001711053],"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":[0.0000435603,0.00005782052,0.0006376002,0.00003843963,0.00002121043,0.00006606423,0.00002379791,0.9528713,0.004243578,0.002247855,0.0005111426,0.03923758],"study_design_scores_gemma":[0.000003202209,0.00001871242,0.00004484786,0.000001690295,0.000002249849,0.000004393596,0.000002841297,0.999164,0.0003601172,0.00025732,0.0001395865,0.000001225197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05813502,0.0003151552,0.9348448,0.000323019,0.00005997607,0.00007052506,0.00002935761,0.0003254752,0.005896779],"genre_scores_gemma":[0.9544396,0.00009406912,0.04346864,0.00009012671,0.00001316547,0.00006149538,0.00003280498,0.00001477427,0.001785237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005690546,"threshold_uncertainty_score":0.01131487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059856742992324,"score_gpt":0.2541927759944099,"score_spread":0.2235942085644866,"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."}}