{"id":"W4412795740","doi":"10.1109/tte.2025.3594553","title":"A Federated Transfer Learning Framework for Lithium-Ion Battery State of Health Estimation Based on Fast-Charging Segments","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Transfer of learning; Computer science; Lithium (medication); State (computer science); Estimation; State of health; Battery (electricity); Transfer (computing); Lithium-ion battery; Ion; Artificial intelligence; Chemistry; Engineering; Medicine; Algorithm; Systems engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003434385,0.0002488297,0.0002980337,0.00081926,0.0003251482,0.00003406787,0.0001252637,0.0001758955,0.00001812293],"category_scores_gemma":[0.0000199753,0.0002890768,0.0001134156,0.001068094,0.00004349243,0.0002157783,2.185347e-8,0.0006382578,0.000005848976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218689,"about_ca_system_score_gemma":0.0001175278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001011223,"about_ca_topic_score_gemma":0.00002142149,"domain_scores_codex":[0.9981629,0.00008889644,0.0006943868,0.0003663457,0.000311239,0.0003762299],"domain_scores_gemma":[0.9991124,0.00036951,0.00009099139,0.0002180019,0.0001637597,0.00004535336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001790687,0.0001144539,0.00001516577,0.0002888736,0.00003967151,1.781537e-7,0.0002008599,0.7021029,0.0949953,0.0001811448,0.000008463257,0.2018739],"study_design_scores_gemma":[0.0005882847,0.0002552101,0.0008853503,0.0002525741,0.00001959567,1.053183e-7,0.0000589486,0.3780153,0.6189568,0.0007932191,0.00002854687,0.0001460217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05460303,0.00004147919,0.9426371,0.0005750913,0.00020401,0.001145341,0.00009811993,0.00066622,0.00002954657],"genre_scores_gemma":[0.9892997,0.0001375955,0.009488376,0.0001167476,0.000005456026,0.0005623411,0.0002415639,0.00005436689,0.00009390369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9346966,"threshold_uncertainty_score":0.9999561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157731931490884,"score_gpt":0.2895229916620526,"score_spread":0.2737497985129642,"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."}}