{"id":"W4408858329","doi":"10.1109/access.2025.3554720","title":"Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Lithium (medication); Fault tolerance; Ion; Fault (geology); Machine learning; Artificial intelligence; Reliability engineering; Distributed computing; Engineering; Chemistry; Psychology","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":[0.0004103543,0.000803283,0.0006726352,0.0004713286,0.0003161058,0.0005742252,0.0008661831,0.0004733297,0.0008531474],"category_scores_gemma":[0.001356279,0.0002098043,0.0003526704,0.0002831373,0.0002405438,0.0006739435,0.0004659611,0.0007127526,0.0001946336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00074434,"about_ca_system_score_gemma":0.0009276713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364297,"about_ca_topic_score_gemma":0.01242524,"domain_scores_codex":[0.9998486,0.00001686652,0.00001365555,0.00004313129,0.00004660577,0.00003100802],"domain_scores_gemma":[0.9995598,0.0001641835,0.00006699791,0.00002819401,0.0001521605,0.00002865051],"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":[0.0001458123,0.00008093191,0.003325035,0.00004543494,0.00002659691,0.0001211041,0.00003530528,0.9123969,0.002283307,0.0005716865,0.001272197,0.07969563],"study_design_scores_gemma":[0.000001878956,0.00001153561,0.000167169,0.000001570019,0.000002432461,0.00000512472,0.000002390409,0.9991508,0.0003607638,0.0002451125,0.0000494224,0.000001795542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2650519,0.001402112,0.7257308,0.0005778593,0.000133432,0.00007984012,0.0003753453,0.002699279,0.00394934],"genre_scores_gemma":[0.9880194,0.0001296932,0.01073467,0.00005520878,0.00001904389,0.00003267305,0.0001909363,0.00001546532,0.0008027549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01364297,"threshold_uncertainty_score":0.02712709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0520027574436679,"score_gpt":0.353012418392857,"score_spread":0.3010096609491891,"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."}}