{"id":"W4401070202","doi":"10.1109/tte.2024.3434553","title":"Deep Learning-Powered Lifetime Prediction for Lithium-Ion Batteries Based on Small Amounts of Charging Cycles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Lithium (medication); Ion; Environmental science; Computer science; Materials science; Engineering physics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002067695,0.0002403388,0.00020521,0.0007459419,0.0001618901,0.00004416571,0.0001511728,0.0002317075,0.00005349564],"category_scores_gemma":[0.0000150652,0.0002654241,0.0001404516,0.0007103427,0.00006863075,0.0002664097,2.566121e-8,0.000524005,0.00002198887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000206524,"about_ca_system_score_gemma":0.00003787157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006001772,"about_ca_topic_score_gemma":0.00003024337,"domain_scores_codex":[0.998483,0.00003884096,0.0004786003,0.0003897172,0.0002981269,0.0003117232],"domain_scores_gemma":[0.9991976,0.0002960718,0.00006254818,0.0002620584,0.0001305097,0.00005123704],"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":[0.0001640178,0.00007566368,0.00002668431,0.0003449057,0.0000470659,9.205061e-7,0.0003078762,0.5356539,0.4042492,0.0001287024,0.00001492349,0.05898604],"study_design_scores_gemma":[0.0003156164,0.0003305622,0.001276364,0.0001285079,0.00004337438,6.120674e-7,0.00006976179,0.3388008,0.6582093,0.000132491,0.0005318887,0.0001606726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06232829,0.0001540862,0.9344638,0.000215404,0.0003893894,0.0007256999,0.0001800228,0.001469735,0.00007357006],"genre_scores_gemma":[0.9964505,0.0004162446,0.001932295,0.00001826663,0.00003811034,0.0005988799,0.0003372336,0.00008777697,0.0001206307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9341223,"threshold_uncertainty_score":0.9999798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371956734964843,"score_gpt":0.2485703573378651,"score_spread":0.2348507899882167,"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."}}