{"id":"W2956063092","doi":"10.1016/j.egyr.2019.06.016","title":"Electric vehicle battery thermal management system with thermoelectric cooling","year":2019,"lang":"en","type":"article","venue":"Energy Reports","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":365,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Thermoelectric cooling; Computer cooling; Battery (electricity); Water cooling; Coolant; Materials science; Nuclear engineering; Air cooling; Thermoelectric effect; Condenser (optics); TEC; Automotive engineering; Mechanical engineering; Thermal management of electronic devices and systems; Power (physics); Engineering; Thermodynamics","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":[0.0001607684,0.0002870256,0.0004781323,0.0002800069,0.0003370998,0.0003442577,0.0007159103,0.0002527722,0.002566343],"category_scores_gemma":[0.0001723359,0.0001219184,0.0001661382,0.0003106129,0.0001530011,0.0005112901,0.0002665422,0.0002377345,0.0006243334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000336983,"about_ca_system_score_gemma":0.0004340411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009795757,"about_ca_topic_score_gemma":0.0008870892,"domain_scores_codex":[0.9998351,0.0000178607,0.000008680599,0.0000407977,0.00006326818,0.00003416977],"domain_scores_gemma":[0.9999081,0.000007490632,0.00000786805,0.00001654396,0.00005102434,0.000008967421],"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":[0.001080343,0.000396827,0.004411408,0.0005918758,0.00006729877,0.000582001,0.0002693266,0.01270176,0.8906854,0.001042958,0.002924161,0.08524656],"study_design_scores_gemma":[0.00011946,0.001706299,0.00781793,0.00002546006,0.00007082221,0.0004887632,0.0001533916,0.08048981,0.892336,0.0002499062,0.01650039,0.00004169232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706062,0.0003598718,0.02166963,0.0001040572,0.00009012425,0.0001539767,0.0002768661,0.0009073556,0.00583194],"genre_scores_gemma":[0.992847,0.00008989774,0.003792053,0.00002051084,0.000006701598,0.00007571643,0.0001316732,0.00002777054,0.003008585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002566343,"threshold_uncertainty_score":0.008585274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004034776204503199,"score_gpt":0.1864127140979547,"score_spread":0.1823779378934514,"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."}}