{"id":"W4408160082","doi":"10.1016/j.applthermaleng.2025.126155","title":"Battery heating strategy to enhance fast-charge performance at low temperatures","year":2025,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; Information Technology Research Centre; Korea Institute of Energy Research","keywords":"Battery (electricity); Materials science; Charge (physics); Automotive engineering; Electrical engineering; Charge cycle; Engineering; Engineering physics; Mechanical engineering; Nuclear engineering; Automotive battery; Thermodynamics; Physics; Power (physics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009868765,0.0003312979,0.0002681069,0.0002727732,0.0001140733,0.00005560844,0.0004416453,0.0001317226,0.00008609789],"category_scores_gemma":[0.00002070506,0.0003493064,0.00004240653,0.0005623956,0.00002613899,0.000137319,0.000256655,0.0005281582,0.0002074194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002680947,"about_ca_system_score_gemma":0.00001386033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001787663,"about_ca_topic_score_gemma":0.000001003384,"domain_scores_codex":[0.9984371,0.000004331166,0.0002756306,0.0003525603,0.0001974965,0.000732878],"domain_scores_gemma":[0.9993237,0.00007642352,0.00001561166,0.0004727411,0.00002456767,0.00008697552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000890646,0.000004583137,0.00005167037,0.00015571,0.00002293174,0.000003516003,0.00003579135,0.4767884,0.5092305,0.0002359893,0.0001130948,0.01334893],"study_design_scores_gemma":[0.0001470625,0.00002899599,0.001015223,0.0001489285,0.000004275747,0.000003237165,0.00003773471,0.0976776,0.8992489,0.00001030043,0.001277537,0.0004002275],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742951,0.0002226397,0.01700104,0.00003960077,0.0002227818,0.0003776153,0.000008993876,0.001558562,0.006273616],"genre_scores_gemma":[0.9969724,0.00003905077,0.001856425,0.0001025911,0.00007640674,0.0002796516,0.000009577287,0.00007898842,0.0005848521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3900184,"threshold_uncertainty_score":0.9998959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004587216600645324,"score_gpt":0.2283483882583505,"score_spread":0.2237611716577052,"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."}}