{"id":"W2999369302","doi":"10.1016/j.est.2019.101144","title":"Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"State of charge; Battery (electricity); Observer (physics); Ion; State (computer science); Charge (physics); Estimation; Physics; Control theory (sociology); Electrical engineering; Engineering; Computer science; Power (physics); Artificial intelligence; Algorithm; Control (management); Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003152397,0.0004189394,0.0006453271,0.000278085,0.0002882756,0.0007809533,0.0004903154,0.0004455859,0.001123358],"category_scores_gemma":[0.0007900116,0.0002069441,0.000271013,0.0002303593,0.0001970442,0.0005690597,0.0004051468,0.0005546922,0.0002681469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003748217,"about_ca_system_score_gemma":0.0003958409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007233154,"about_ca_topic_score_gemma":0.00771799,"domain_scores_codex":[0.9998048,0.00002618469,0.00002367815,0.00004119893,0.00007591618,0.00002822005],"domain_scores_gemma":[0.9996483,0.0001028077,0.00005737508,0.00002553936,0.0001551349,0.00001083854],"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":[0.002080378,0.0003823931,0.008968554,0.0009852704,0.0001952376,0.000539144,0.0004678171,0.5381408,0.09131929,0.00324624,0.00449675,0.349178],"study_design_scores_gemma":[0.00002167572,0.0001222884,0.001522046,0.00001031016,0.00001977213,0.00002538591,0.00002172833,0.9905851,0.006826578,0.0002100671,0.0006259979,0.000009045025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.140187,0.0009485934,0.852218,0.000224476,0.0003188859,0.00008650359,0.0001161432,0.001616116,0.004284355],"genre_scores_gemma":[0.9934634,0.0001168541,0.005277069,0.00002423282,0.00001406649,0.00002218717,0.00006673333,0.000009386211,0.001006099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007233154,"threshold_uncertainty_score":0.01438206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183942583714703,"score_gpt":0.2499610045711662,"score_spread":0.2281215787340192,"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."}}