{"id":"W3175561862","doi":"10.1016/j.energy.2021.121266","title":"Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data","year":2021,"lang":"en","type":"article","venue":"Energy","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Thermal runaway; Battery (electricity); False alarm; Warning system; Fault (geology); Voltage; Robustness (evolution); Engineering; Reliability engineering; Reliability (semiconductor); Computer science; Battery pack; Fault detection and isolation; Real-time computing; Electrical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113173,0.0001603297,0.0002057213,0.0001015811,0.0001172329,0.00007367312,0.0007466478,0.00008455111,0.00006224903],"category_scores_gemma":[0.0001641354,0.000165936,0.00001919223,0.0002526239,0.00006179162,0.0005135948,0.001538682,0.0001964677,0.000002253569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006728883,"about_ca_system_score_gemma":0.00002783628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009907416,"about_ca_topic_score_gemma":0.0005291756,"domain_scores_codex":[0.9987248,0.00003072646,0.0001883798,0.0004846409,0.0001535259,0.0004179458],"domain_scores_gemma":[0.99801,0.0003325864,0.00002890894,0.001527885,0.00003856708,0.00006204333],"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":[0.0000263088,0.0000816771,0.01728554,0.0002713927,0.0003512372,0.0002715734,0.0001099539,0.3726218,0.1618933,0.001183211,0.016851,0.4290529],"study_design_scores_gemma":[0.0003220264,0.00001537054,0.001316071,0.00006521917,0.00002173826,0.000007580066,0.00009834923,0.8301941,0.03657267,0.000123935,0.130982,0.000281021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8225315,0.002522903,0.168513,0.0008801079,0.0005262449,0.0002461196,0.001575661,0.001264622,0.001939887],"genre_scores_gemma":[0.9700395,0.00134946,0.02670891,0.0001103675,0.0002230793,0.00005658534,0.001135442,0.00009541805,0.0002811693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4575723,"threshold_uncertainty_score":0.6766675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08502434423089643,"score_gpt":0.3313691192724954,"score_spread":0.246344775041599,"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."}}