{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T20:14:24Z","timestamp":1778184864309,"version":"3.51.4"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1109\/access.2025.3621556","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T17:41:12Z","timestamp":1760463672000},"page":"184030-184045","source":"Crossref","is-referenced-by-count":1,"title":["Symbolic Regression for State Estimation of Lithium-Ion Battery"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5661-7501","authenticated-orcid":false,"given":"Anubhav","family":"Kamal","sequence":"first","affiliation":[{"name":"SAIT, Samsung Semiconductor India Research, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5494-1428","authenticated-orcid":false,"given":"Shubham","family":"Sambhaji Patil","sequence":"additional","affiliation":[{"name":"SAIT, Samsung Semiconductor India Research, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6133-2189","authenticated-orcid":false,"given":"Sagar","family":"Bharathraj","sequence":"additional","affiliation":[{"name":"SAIT, Samsung Semiconductor India Research, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3046-1292","authenticated-orcid":false,"given":"Ankur","family":"Deshwal","sequence":"additional","affiliation":[{"name":"SAIT, Samsung Semiconductor India Research, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2745-1384","authenticated-orcid":false,"given":"Shashishekar P.","family":"Adiga","sequence":"additional","affiliation":[{"name":"SAIT, Samsung Semiconductor India Research, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1149\/1.2054684"},{"issue":"6","key":"ref3","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1149\/1.2221597","article-title":"Modeling of galvanostatic charge and discharge of the lithium\/polymer\/insertion cell","volume":"140","author":"Doyle","year":"1993","journal-title":"J. Electrochem. Soc."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-019-0356-8"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0156-7"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.jpowsour.2012.05.001","article-title":"Platinum oxidation responsible for degradation of platinum\u2013cobalt alloy cathode catalysts for polymer electrolyte fuel cells","volume":"215","author":"Hidai","year":"2012","journal-title":"J. Power Sources"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aay2631"},{"key":"ref8","article-title":"Interpretable machine learning for science with PySR and SymbolicRegression. jl","author":"Cranmer","year":"2023","journal-title":"arXiv:2305.01582"},{"issue":"4","key":"ref9","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/en12040660","article-title":"A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm","volume":"12","author":"Khumprom","year":"2019","journal-title":"Energies"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2764869"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.energy.2018.08.071","article-title":"State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles","volume":"162","author":"Zahid","year":"2018","journal-title":"Energy"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1016\/j.jpowsour.2014.07.016","article-title":"State-of-charge estimation for battery management system using optimized support vector machine for regression","volume":"269","author":"Hu","year":"2014","journal-title":"J. Power Sources"},{"issue":"4","key":"ref13","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.3390\/en8042889","article-title":"Regression models using fully discharged voltage and internal resistance for state of health estimation of lithium-ion batteries","volume":"8","author":"Tseng","year":"2015","journal-title":"Energies"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2015.2512237"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"720","DOI":"10.4236\/sgre.2012.31007","article-title":"The SOC estimation of power Li-Ion battery based on ANFIS model","volume":"3","author":"Wu","year":"2012","journal-title":"Smart Grid Renew. Energy"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jpowsour.2015.04.166","article-title":"Online estimation of lithium-ion battery capacity using sparse Bayesian learning","volume":"289","author":"Hu","year":"2015","journal-title":"J. Power Sources"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5121\/acii.2018.5101"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2974907"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2020.228534","article-title":"Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter","volume":"476","author":"Li","year":"2020","journal-title":"J. Power Sources"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2014.2364554"},{"issue":"3","key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.geits.2023.100082","article-title":"Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model","volume":"2","author":"Fang","year":"2023","journal-title":"Green Energy Intell. Transp."},{"key":"ref23","article-title":"An efficient feature search approach for robust state of health estimation of Li-Ion battery","volume":"2025","author":"Chen","year":"2025","journal-title":"Green Energy Intell. Transp."},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2021.229723","article-title":"Accessing the current limits in lithium ion batteries: Analysis of propensity for unexpected power loss as a function of depth of discharge, temperature and pulse duration","volume":"494","author":"Bharathraj","year":"2021","journal-title":"J. Power Sources"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2022.232325","article-title":"Considering solid phase diffusion penetration depth to improve profile approximations: Towards accurate state estimations in lithium-ion batteries at low characteristic diffusion lengths","volume":"554","author":"Bharathraj","year":"2023","journal-title":"J. Power Sources"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1994-5"},{"key":"ref27","article-title":"State-of-charge estimation of a Li-Ion battery using deep forward neural networks","author":"de Lima","year":"2020","journal-title":"arXiv:2009.09543"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117951","article-title":"Adaptive neural network models for state of charge estimation under dynamic battery conditions","volume":"133","author":"Avanthika","year":"2025","journal-title":"J. Energy Storage"},{"issue":"6","key":"ref29","doi-asserted-by":"crossref","first-page":"332","DOI":"10.3390\/batteries9060332","article-title":"An optimized random forest regression model for Li-Ion battery prognostics and health management","volume":"9","author":"Wang","year":"2023","journal-title":"Batteries"},{"key":"ref30","first-page":"17429","article-title":"Discovering symbolic models from deep learning with inductive biases","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cranmer"},{"key":"ref31","first-page":"123443","article-title":"Graphtrail: Translating GNN predictions into human-interpretable logical rules","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"37","author":"Armgaan"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11202751.pdf?arnumber=11202751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:13:08Z","timestamp":1761930788000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11202751\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3621556","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}