{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:51:18Z","timestamp":1792515078076,"version":"4.5.0"},"reference-count":31,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle\u2019s driving range, prolonging the battery life, and ensuring the maximum efficiency of the whole battery pack. In this paper, the ternary Li-ion battery is taken as the research object, and the Dual Polarization (DP) equivalent circuit model with temperature-varying parameters is established. The parameters of the Li-ion battery model at ambient temperature are identified by the forgetting factor least square method. Based on the state space equation of power battery SOC, an adaptive Sliding Mode Observer is used to study the estimation of the State of Charge of the power battery. The SOC estimation results are fully verified at low temperature (0 \u00b0C), normal temperature (25 \u00b0C), and high temperature (50 \u00b0C). The simulation results of the Urban Dynamometer Driving Schedule (UDDS) show that the SOC error estimated at low temperature and high temperature is within 2%, and the SOC error estimated at normal temperature is less than 1%, The algorithm has the advantages of accurate estimation, fast convergence, and strong robustness.<\/jats:p>","DOI":"10.3390\/s22197678","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"7678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer"],"prefix":"10.3390","volume":"22","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710032, China"},{"name":"School of Electronic and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shurui","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"014302","DOI":"10.1063\/1.5057894","article-title":"Power battery state of charge estimation based on extended Kalman filter","volume":"11","author":"Wang","year":"2019","journal-title":"J. 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