{"id":"W2047337887","doi":"10.1109/tpel.2015.2397453","title":"Comprehensive DC Power Balance Management in High-Power Three-Level DC–DC Converter for Electric Vehicle Fast Charging","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Power (physics); Power Balance; Electric vehicle; Electrical engineering; Computer science; Power management; Engineering; Automotive engineering; Electronic 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001607682,0.0004368576,0.0004204624,0.0006110488,0.0001215647,0.00006017931,0.0005404279,0.0002439017,0.00009185181],"category_scores_gemma":[0.000006431918,0.0004752111,0.0001267175,0.0009303506,0.00006962546,0.0003442296,0.000008960315,0.001040467,0.0001480894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232904,"about_ca_system_score_gemma":0.00007553638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009243904,"about_ca_topic_score_gemma":0.00007165286,"domain_scores_codex":[0.9971501,0.0000323003,0.0004161843,0.0005659768,0.000456326,0.001379087],"domain_scores_gemma":[0.9988452,0.0001260797,0.00005150164,0.0006733043,0.0001622395,0.0001417067],"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":[0.002366912,0.002251107,0.0003479545,0.0006132826,0.002478921,0.0002909722,0.001996624,0.5547074,0.2420704,0.007071305,0.01109048,0.1747147],"study_design_scores_gemma":[0.01605245,0.003868039,0.002065703,0.0002652915,0.0001563723,0.00008335672,0.001349759,0.4657631,0.422065,0.008386485,0.07628842,0.003656098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1791124,0.000745905,0.8166561,0.0003864869,0.0006495745,0.001088202,0.0000577843,0.0006899816,0.0006136087],"genre_scores_gemma":[0.9967951,0.0003569172,0.001678878,0.0002166144,0.0000119633,0.0004309923,0.00000878407,0.0001348033,0.000365956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8176827,"threshold_uncertainty_score":0.99977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320502387450792,"score_gpt":0.2599376092880513,"score_spread":0.2367325854135433,"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."}}