{"id":"W3197903489","doi":"10.1109/tie.2021.3108715","title":"A Balancing Current Ratio Based State-of-Health Estimation Solution for Lithium-Ion Battery Pack","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"High Value Manufacturing Catapult; Science, Technology and Innovation Commission of Shenzhen Municipality; Queen's University; Queen's University Belfast","keywords":"Current (fluid); Battery pack; Lithium (medication); Battery (electricity); State of health; Estimation; Ion; Lithium-ion battery; State (computer science); Electrical engineering; Automotive engineering; Computer science; Reliability engineering; Materials science; Engineering; Power (physics); Chemistry; Physics; Thermodynamics; Systems engineering; Algorithm; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003442511,0.0001905308,0.0002854457,0.0002398691,0.0001723683,0.00003209483,0.0001251642,0.0001711284,0.00002280919],"category_scores_gemma":[0.00006128733,0.0002195335,0.0001072161,0.000574955,0.00003813319,0.0001936225,0.000001440139,0.0008545683,0.000005972469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009787268,"about_ca_system_score_gemma":0.0007153606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005565782,"about_ca_topic_score_gemma":0.00004709319,"domain_scores_codex":[0.99831,0.00008993693,0.0005010429,0.0002627825,0.0002769803,0.0005591988],"domain_scores_gemma":[0.9991482,0.0002485272,0.00009786121,0.0003097757,0.0001369282,0.00005867436],"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":[0.00004480503,0.0000630583,0.000001788697,0.00007947107,0.00002255103,3.183145e-7,0.00002013233,0.5349255,0.01212154,0.00001530245,0.0003640056,0.4523416],"study_design_scores_gemma":[0.0009679312,0.0002748076,0.000005018295,0.0001057816,0.00001382755,0.000001961904,0.00001223171,0.5620822,0.4340293,0.0002917931,0.002066721,0.0001483627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0136799,0.0004571693,0.9831704,0.0006097958,0.001014586,0.0006096049,0.0001038704,0.0003454938,0.000009112959],"genre_scores_gemma":[0.9941131,0.0004111876,0.004974442,0.00003139781,0.00006601172,0.0002291441,0.00008177807,0.00005496819,0.0000379367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9804332,"threshold_uncertainty_score":0.8952317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04662434258566139,"score_gpt":0.3060751887124015,"score_spread":0.2594508461267401,"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."}}