{"id":"W1598277387","doi":"10.1109/iecon.2014.7048960","title":"Modelling of temperature dependent impedance in lithium ion polymer batteries and impact analysis on electric vehicles","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Battery (electricity); Lithium (medication); Electrical impedance; Automotive engineering; Propulsion; Internal resistance; Materials science; Power (physics); Work (physics); Nuclear engineering; Electric vehicle; Range (aeronautics); Thermal; Electrical engineering; Environmental science; Engineering; Mechanical engineering; Composite material; Aerospace engineering; Thermodynamics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001401671,0.0005186536,0.0003817442,0.0002878546,0.0002279259,0.0005501833,0.0006090364,0.0008001023,0.00119802],"category_scores_gemma":[0.0005722258,0.0002618498,0.0005734934,0.0003486601,0.0005108426,0.0009287565,0.0005130287,0.0004072176,0.0003481385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004617863,"about_ca_system_score_gemma":0.0003825208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003618318,"about_ca_topic_score_gemma":0.001696203,"domain_scores_codex":[0.9998822,0.00002980197,0.00000501723,0.00001866302,0.00004654613,0.00001767504],"domain_scores_gemma":[0.9999114,0.00004265697,0.00001356676,0.000008767091,0.00001932923,0.000004293645],"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":[0.0000256161,0.00001275967,0.0003680079,0.00002701646,0.000006049496,0.00008160833,0.00003812411,0.9887154,0.005556169,0.002181879,0.0001015784,0.002885921],"study_design_scores_gemma":[0.00000371746,0.00002168072,0.0002052878,0.000003124465,0.000003855735,0.00002282114,0.00001044153,0.9967315,0.001694518,0.0007393757,0.0005596808,0.000004045526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3367953,0.001128158,0.6312203,0.0003495853,0.00008652245,0.0001124763,0.0003067389,0.0006522159,0.02934882],"genre_scores_gemma":[0.9866296,0.0005338609,0.003474208,0.00002301868,0.00001456002,0.00006176707,0.00007285864,0.00005435902,0.00913578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003618318,"threshold_uncertainty_score":0.007194519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104121548520844,"score_gpt":0.2535159652338361,"score_spread":0.2424747497486276,"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."}}