{"id":"W2967426021","doi":"10.1109/itec.2019.8790482","title":"Real-Time Optimal Energy Management of Electrified Powertrains with Reinforcement Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canada Research Chairs","keywords":"Powertrain; Reinforcement learning; Energy management; Computer science; Energy management system; Optimal control; Dynamic programming; Q-learning; Process (computing); Bellman equation; Control engineering; Energy (signal processing); Mathematical optimization; Engineering; Artificial intelligence; Torque; Algorithm; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00005870114,0.0001261434,0.0001682754,0.0001031987,0.00001901186,0.000009061417,0.0001378747,0.00004693147,0.000224776],"category_scores_gemma":[8.55512e-7,0.0001042661,0.00003087807,0.0002330213,0.00001726211,0.00005934875,0.0000254627,0.000102237,0.00004766684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000444037,"about_ca_system_score_gemma":0.000007381105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003395765,"about_ca_topic_score_gemma":0.000001372063,"domain_scores_codex":[0.9992824,0.000004782728,0.0001599728,0.0001290941,0.0001513841,0.0002723308],"domain_scores_gemma":[0.999716,0.00001489577,0.00003002914,0.0001944475,0.00002025016,0.00002439183],"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":[0.0001084698,0.00003792262,0.0008035814,0.0002068844,0.0006365323,0.00003454701,0.00008267789,0.7858963,0.07686441,0.09634822,0.001924013,0.03705642],"study_design_scores_gemma":[0.0018913,0.001816976,0.002099313,0.0001202386,0.00008157912,0.00002167257,0.0003705713,0.5288637,0.4566996,0.00008974381,0.007155905,0.0007894072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7244353,0.00007053329,0.02107487,0.00002497681,0.00001866271,0.0001507797,1.822313e-7,0.001205721,0.253019],"genre_scores_gemma":[0.9778768,0.0006165184,0.003337315,0.000007030917,0.000004661142,0.00001072225,0.000005652446,0.00002437986,0.01811697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3798352,"threshold_uncertainty_score":0.4251849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003051647502396452,"score_gpt":0.1709891564109987,"score_spread":0.1679375089086023,"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."}}