{"id":"W3016438359","doi":"10.1109/tie.2020.2984980","title":"A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial neural network; Fault detection and isolation; Battery (electricity); Residual; Computer science; Thermal; Fault (geology); Control engineering; Artificial intelligence; Engineering; Power (physics); Algorithm; Actuator","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":[0.0002384421,0.000547519,0.000357963,0.000485806,0.000269512,0.0004576188,0.0006555208,0.0006950953,0.001232341],"category_scores_gemma":[0.0009390443,0.0002029181,0.0002576691,0.0004338382,0.0002021325,0.0006337666,0.0003237234,0.0005372702,0.0002967646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004382308,"about_ca_system_score_gemma":0.0003327949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003478508,"about_ca_topic_score_gemma":0.003835161,"domain_scores_codex":[0.9998373,0.00002745637,0.00001188594,0.0000419597,0.00006477554,0.00001661295],"domain_scores_gemma":[0.9998147,0.00005664786,0.00002670736,0.00001316117,0.00008264277,0.000006202826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002188095,0.0001286589,0.001589816,0.0001984849,0.00007649924,0.0001277944,0.00006878706,0.3057995,0.05800598,0.003108243,0.002005597,0.6286719],"study_design_scores_gemma":[0.000003131218,0.00002509555,0.0003577672,0.000006299613,0.00000807744,0.00002983084,0.000004419939,0.9924915,0.006125856,0.000484289,0.000455476,0.000008231035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02415544,0.0006994322,0.9717452,0.00009631168,0.00008961992,0.00003670597,0.00005976787,0.001144788,0.001972637],"genre_scores_gemma":[0.7354997,0.0006103355,0.2558193,0.0001477735,0.0000773475,0.0001498687,0.0002072299,0.00009312093,0.00739529],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003478508,"threshold_uncertainty_score":0.006916523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0418270811872883,"score_gpt":0.2867165777550972,"score_spread":0.2448894965678089,"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."}}