{"id":"W4403412837","doi":"10.1002/oca.3216","title":"Mixed‐Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management","year":2024,"lang":"en","type":"article","venue":"Optimal Control Applications and Methods","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Electric vehicle; Energy management; Integer programming; Integer (computer science); Control (management); Mathematical optimization; Reinforcement; Computer science; Energy (signal processing); Engineering; Artificial intelligence; Mathematics; Structural engineering; Physics","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":[0.001061543,0.0004806643,0.0004225165,0.0002634183,0.0004361587,0.0006596435,0.0006395872,0.0009734265,0.001791209],"category_scores_gemma":[0.001517843,0.0001876245,0.0003391713,0.0003185479,0.0005981287,0.0005608209,0.0006797457,0.0008801625,0.0001236359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006928665,"about_ca_system_score_gemma":0.0005380636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009227797,"about_ca_topic_score_gemma":0.008963626,"domain_scores_codex":[0.9997066,0.0001335627,0.00001274244,0.00004152812,0.00005828411,0.00004725297],"domain_scores_gemma":[0.9988065,0.0008970305,0.00005528645,0.00005258031,0.0001190098,0.00006964544],"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":[0.0001065792,0.0001449266,0.001302237,0.00006917687,0.00002422836,0.0003891962,0.0000481391,0.9776236,0.0006804028,0.005579172,0.0006831134,0.01334924],"study_design_scores_gemma":[0.00002158372,0.0000506322,0.0002438274,0.000003657198,0.000003840481,0.00002234495,0.00003136204,0.9965734,0.0005102,0.001917972,0.00061698,0.000004141089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6181065,0.00103693,0.3607291,0.001159663,0.0001002412,0.0002054276,0.0002059245,0.0004438758,0.01801223],"genre_scores_gemma":[0.9795374,0.00008542632,0.01847314,0.00003258425,0.000008909771,0.0000413522,0.00003864747,0.00001204452,0.001770595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009227797,"threshold_uncertainty_score":0.01834822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007282110080568093,"score_gpt":0.2674272158032656,"score_spread":0.2601451057226975,"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."}}