{"id":"W4289260921","doi":"10.1016/j.egyai.2022.100194","title":"Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors","year":2022,"lang":"en","type":"article","venue":"Energy and AI","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Extended Kalman filter; Thermal runaway; Battery (electricity); Heat generation; Voltage; Computer science; Lithium-ion battery; Kalman filter; Calorimeter (particle physics); Fault (geology); Control theory (sociology); Simulation; Engineering; Artificial intelligence; Electrical engineering; Power (physics); Detector","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":[],"consensus_categories":[],"category_scores_codex":[0.00007508034,0.00005122832,0.00005610161,0.00003922778,0.0001193392,0.000007930128,0.0001364529,0.00001722145,0.000009400584],"category_scores_gemma":[0.00003200105,0.00004211455,0.000008764651,0.00003733379,0.00003478305,0.00007038527,0.0001613183,0.00009922627,3.006427e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001127234,"about_ca_system_score_gemma":0.000005575224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007460193,"about_ca_topic_score_gemma":0.000003258425,"domain_scores_codex":[0.9996361,0.000009582483,0.00007947726,0.0000992244,0.00007828699,0.00009736278],"domain_scores_gemma":[0.99957,0.0001971006,0.00001022921,0.0002009459,0.00001016802,0.00001158548],"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":[0.00001132289,0.00001015975,0.0003130114,0.00001540006,0.000008957461,0.000001438064,0.00001284018,0.9616871,0.002215778,0.00008790977,0.0004622349,0.0351738],"study_design_scores_gemma":[0.000152837,0.00004447936,0.0001131798,0.000005484086,0.00001101487,0.000002149711,0.00005748046,0.9877024,0.00876517,0.0001726268,0.002928793,0.00004442871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6504251,0.0009756555,0.3477398,0.000242061,0.0001401792,0.00007693248,0.000289084,0.0001013074,0.000009891142],"genre_scores_gemma":[0.999192,0.0002971349,0.0002545564,0.00004180372,0.00002806549,0.00007604936,0.00008947905,0.00001244433,0.000008473433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3487669,"threshold_uncertainty_score":0.1717381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325283474927408,"score_gpt":0.2543272516100381,"score_spread":0.231074416860764,"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."}}