{"id":"W2995104800","doi":"10.3390/batteries6010001","title":"Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares","year":2019,"lang":"en","type":"article","venue":"Batteries","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fault (geology); Fault detection and isolation; CUSUM; Battery (electricity); Recursive least squares filter; Computer science; Degradation (telecommunications); Control theory (sociology); Voltage; Real-time computing; Reliability engineering; Algorithm; Engineering; Power (physics); Control (management); Electrical engineering; Mathematics; Statistics; 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":[0.0003321134,0.0006450355,0.000495997,0.0002614073,0.000244543,0.0004936015,0.0004355023,0.0004184856,0.0004203257],"category_scores_gemma":[0.001597907,0.0002637,0.0003664238,0.0002146615,0.0002560418,0.0004907409,0.0003714511,0.000610815,0.0001261107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003996339,"about_ca_system_score_gemma":0.0005242419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007696754,"about_ca_topic_score_gemma":0.006034552,"domain_scores_codex":[0.9997734,0.00005469754,0.00001771527,0.00005825911,0.00007357664,0.00002231174],"domain_scores_gemma":[0.9995897,0.0001886874,0.00008953881,0.00003219655,0.00009170474,0.000008117275],"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":[0.0001735964,0.00009988018,0.002864651,0.0001454223,0.00006921493,0.0001129427,0.0001785576,0.799762,0.02931168,0.00130826,0.000578975,0.1653948],"study_design_scores_gemma":[0.000003919372,0.00004028319,0.0004848873,0.000002853779,0.000007569896,0.00001480779,0.000009025312,0.9952801,0.003782257,0.000233602,0.0001351296,0.000005516547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08830492,0.0003473092,0.9088983,0.000119505,0.00002750888,0.00003765832,0.00002029775,0.001328902,0.0009155865],"genre_scores_gemma":[0.9587678,0.0001443333,0.04028497,0.0000283315,0.000008429855,0.00003104042,0.00004540634,0.00003138724,0.0006582403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007696754,"threshold_uncertainty_score":0.01530391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698229692882783,"score_gpt":0.2572485790956098,"score_spread":0.2402662821667819,"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."}}