{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:14:00Z","timestamp":1773778440701,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3007046","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T20:27:58Z","timestamp":1593808078000},"page":"122135-122146","source":"Crossref","is-referenced-by-count":80,"title":["Li-Ion Batteries Parameter Estimation With Tiny Neural Networks Embedded on Intelligent IoT Microcontrollers"],"prefix":"10.1109","volume":"8","author":[{"given":"Giulia","family":"Crocioni","sequence":"first","affiliation":[]},{"given":"Danilo","family":"Pau","sequence":"additional","affiliation":[]},{"given":"Jean-Michel","family":"Delorme","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6417-3750","authenticated-orcid":false,"given":"Giambattista","family":"Gruosso","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"372","author":"bishop","year":"1995","journal-title":"Neural Networks for Pattern Recognition"},{"key":"ref38","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref32","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv 1412 3555"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref30","article-title":"Long short term memory networks for anomaly detection in time series","author":"malhotra","year":"2015","journal-title":"Eur Symp on Artif Neural Netw Computat Intell Mach Learning"},{"key":"ref37","first-page":"1","article-title":"Modeling Li-ion battery capacity depletion in a particle filtering framework","author":"saha","year":"2009","journal-title":"Proc Annu Conf Prognostics Health Manage Soc (PHM)"},{"key":"ref36","author":"verma","year":"2019","journal-title":"Understanding Input and Output shapes in LSTM&#x2014;Keras"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766500"},{"key":"ref34","author":"ilango","year":"2019","journal-title":"Batch Normalization&#x2014;Speed Up Neural Network Training"},{"key":"ref28","article-title":"Training recurrent neural networks","author":"sutskever","year":"2013"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC.2018.8538530"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/VPPC.2009.5289803"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-7753(01)00887-4"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/en12224338"},{"key":"ref22","article-title":"Low-memory neural network training: A technical report","author":"nimit sohoni","year":"2019"},{"key":"ref21","article-title":"GPipe: Efficient training of giant neural networks using pipeline parallelism","author":"huang","year":"2018","journal-title":"arXiv 1811 06965"},{"key":"ref24","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","author":"zhu","year":"2017","journal-title":"arXiv 1710 01878"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref26","article-title":"Conditional time series forecasting with convolutional neural networks","author":"borovykh","year":"2017","journal-title":"arXiv 1703 04691"},{"key":"ref25","year":"2020","journal-title":"TensorFlow Lite for Microcontrollers"},{"key":"ref50","first-page":"246","article-title":"14.5 envision: A 0.26-to-10TOPS\/W subword-parallel dynamic-voltage-accuracy-frequency-scalable convolutional neural network processor in 28 nm FDSOI","author":"moons","year":"2017","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2461523"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2012.10.060"},{"key":"ref40","first-page":"1","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref12","first-page":"1180","article-title":"A li-ion battery management system based on CAN-bus for electric vehicle","author":"zheng","year":"2008","journal-title":"Proc 3rd IEEE Conf Ind Electron Appl"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2013.02.012"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2782224"},{"key":"ref15","author":"saha","year":"2019","journal-title":"Battery Data Set NASA Ames Prognostics Data Repository"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.09.182"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/452\/3\/032067"},{"key":"ref18","article-title":"Intelligent state-of-charge and state-of-health estimation framework for Li-ion batteries in electrified vehicles using deep learning techniques","author":"chemali","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/en11071820"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MIE.2013.2250351"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2012.12.004"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-62616-5.00020-6"},{"key":"ref5","first-page":"426","article-title":"A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries","volume":"2","author":"murnane","year":"2017","journal-title":"Analog Devices"},{"key":"ref8","year":"2010","journal-title":"Lithium-ion Battery Datasheet Battery Model LIR18650 2600mAh"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2920932"},{"key":"ref49","article-title":"Energy table for 45 nm process","author":"horowitz","year":"2014","journal-title":"Stanford VLSI wiki"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2015.01.129"},{"key":"ref46","year":"0","journal-title":"Online Documentation&#x2014;Command Line Interface X-CUBE-AI Expansion Package R1 2 - AI PLATFORM R5 0 0 (Embedded Inference Client API 1 1 0)&#x2014;Command Line Interface R1 2 0 2019 Available in X-CUBE-AI Expansion Package"},{"key":"ref45","article-title":"Quantization and training of neural networks for efficient integer-arithmetic-only inference","author":"jacob","year":"2017","journal-title":"arXiv 1712 05877"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2017.8335699"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/DSD.2018.00075"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","author":"breiman","year":"2001","journal-title":"Machine Learning"},{"key":"ref44","year":"2019","journal-title":"STWIN SensorTile Wireless Industrial Node Development Kit and Reference Design for Industrial IoT Applications Data brief Rev 1"},{"key":"ref43","author":"hale","year":"2019","journal-title":"Scale Standardize or Normalize With Scikit-Learn"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09133084.pdf?arnumber=9133084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T01:09:45Z","timestamp":1641949785000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9133084\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3007046","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}