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First, based on the second\u2010order RC equivalent circuit model and Grunwald\u2013Letnikov (G\u2010L) definition, the high\u2010precision fractional\u2010order hysteresis\u2010equivalent circuit model (FH\u2010ECM) is established considering the open\u2010circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA\u2010PSO). Third, a fractional\u2010order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium\u2010ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional\u2010order hysteresis\u2010equivalent circuit modeling method.<\/jats:p>","DOI":"10.1002\/cta.3767","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T07:15:56Z","timestamp":1691824556000},"page":"420-438","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improved fractional\u2010order hysteresis\u2010equivalent circuit modeling for the online adaptive high\u2010precision state of charge prediction of urban\u2010electric\u2010bus lithium\u2010ion batteries"],"prefix":"10.1002","volume":"52","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7954-3822","authenticated-orcid":false,"given":"Jiawei","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Information Engineering Southwest University of Science and Technology Mianyang 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