{"id":"W3186024736","doi":"10.1109/tpel.2021.3098445","title":"Multi-fault Detection and Isolation for Lithium-Ion Battery Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Fault detection and isolation; Residual; Robustness (evolution); Voltage; Engineering; Kalman filter; State of charge; Fault indicator; Computer science; Electronic engineering; Control theory (sociology); Reliability engineering; Battery (electricity); Electrical engineering; Algorithm; Power (physics); Artificial intelligence","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":[0.0002457619,0.00049534,0.000425843,0.0007257101,0.0002477598,0.000337653,0.0004146818,0.0003176708,0.0004850014],"category_scores_gemma":[0.0010814,0.000144421,0.0002770773,0.0002345134,0.0002008157,0.0008426347,0.0005375113,0.0002919401,0.00008933835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003640439,"about_ca_system_score_gemma":0.0002477936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001304697,"about_ca_topic_score_gemma":0.001415642,"domain_scores_codex":[0.999728,0.00004715261,0.00001913185,0.00005053548,0.0001264494,0.00002867351],"domain_scores_gemma":[0.9996446,0.0001374301,0.00008511329,0.0000391896,0.000070212,0.00002343378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009821537,0.0002236517,0.0106464,0.0002439776,0.0001125174,0.0005193282,0.0002660728,0.3917116,0.1193758,0.003555709,0.0008880021,0.4714748],"study_design_scores_gemma":[0.000008133129,0.0001124785,0.002256234,0.000004347417,0.00001151774,0.0001069532,0.00001920763,0.9821742,0.01403769,0.001072122,0.0001874173,0.000009673336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2801315,0.0007182201,0.7157341,0.0001551345,0.0000546118,0.00004268409,0.00005504327,0.001355481,0.001753232],"genre_scores_gemma":[0.9902565,0.00004897825,0.009425207,0.00001231876,0.000006694319,0.00000783362,0.00002563321,0.000006827844,0.0002100074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001304697,"threshold_uncertainty_score":0.00264138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356217117891682,"score_gpt":0.2567630367274418,"score_spread":0.243200865548525,"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."}}