{"id":"W4292387828","doi":"10.1109/eeeic/icpseurope54979.2022.9854614","title":"Globally Optimal Energy Management in a Battery-Ultracapacitor Electric Vehicle","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; York University","funders":"","keywords":"Battery pack; Battery (electricity); Automotive engineering; Supercapacitor; Electric vehicle; Depth of discharge; Voltage; Driving cycle; Internal resistance; State of charge; Energy storage; Driving range; Range (aeronautics); Energy management; Automotive battery; Battery electric vehicle; Computer science; Power (physics); Electrical engineering; Engineering; Energy (signal processing); Capacitance; 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":[0.0005090801,0.0007767036,0.0009338169,0.0003596387,0.0004358499,0.001242319,0.0007714538,0.0006970058,0.00098999],"category_scores_gemma":[0.0006646969,0.0006023573,0.0004475218,0.0003744362,0.0006008848,0.0006725012,0.0007899495,0.000566496,0.0001493699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008103425,"about_ca_system_score_gemma":0.0008603796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401044,"about_ca_topic_score_gemma":0.008787204,"domain_scores_codex":[0.9997651,0.0000566066,0.000009283782,0.0000701643,0.00004057465,0.00005828358],"domain_scores_gemma":[0.9997527,0.0001118617,0.00004334516,0.00001458923,0.00005693376,0.00002051061],"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":[0.00002859222,0.0000141216,0.000240289,0.00001723467,0.00001503251,0.00005293804,0.00001590321,0.993314,0.0005660687,0.001055593,0.0001390923,0.004541212],"study_design_scores_gemma":[0.000006057722,0.0000237453,0.0001075288,0.000002268104,0.000006139957,0.000007629758,0.00001311638,0.9985731,0.0001988996,0.0009158733,0.0001426213,0.000003111315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.281141,0.001049741,0.6995199,0.0004802241,0.00007873467,0.0001104203,0.0002081227,0.0006308852,0.01678102],"genre_scores_gemma":[0.9892041,0.0001083786,0.008653586,0.00003614541,0.000006787915,0.000043524,0.00004310769,0.00001729242,0.001887113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01401044,"threshold_uncertainty_score":0.02785778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03677819482234191,"score_gpt":0.2362498984371294,"score_spread":0.1994717036147875,"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."}}