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Currently, traditional identification algorithms and integer\u2010order models are poorly adaptive and unable to respond to the dynamic characteristics of batteries in real time, whereas unscented Kalman filtering is often accompanied by the problem that the covariance matrix is not positively determined, leading to algorithmic collapse. This study combines the multi\u2010innovation theory with online identification and provides an improved fractional\u2010order multi\u2010innovation double unscented Kalman filter (DFOMIUKF) joint estimation algorithm. The algorithm contains a multi\u2010timescale framework that allows full parametric identification of fractional\u2010order models, which guarantees the algorithm's ability to cope with complex working conditions, and the problem of failure of the Kalman filtering algorithm is solved by modifying the covariance matrix in real time by singular value decomposition (SVD). On the basis of the fractional RC model, the FOMIUKF<jats:sub>1<\/jats:sub> algorithm allows real\u2010time updating of model parameters on a macro scale, and the obtained parameters at the microscale are passed to the FOMIUKF<jats:sub>2<\/jats:sub> algorithm for real\u2010time updating of the charge level of the lithium battery. Then, the DFOMIUKF algorithm is validated under different working environments, respectively. The findings indicate that the proposed algorithm predicts the SOC with the maximum values of root mean square error (RMSE) and mean absolute error (MAE) not exceeding 0.77% and 0.66%, respectively, and maintains high accuracy even at low temperatures. It is illustrated that the proposed algorithm solves the problems of the above offline algorithms with high precision and robustness.<\/jats:p>","DOI":"10.1002\/cta.4404","type":"journal-article","created":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T22:30:40Z","timestamp":1735511440000},"page":"5110-5131","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Online State of Charge Estimation for Battery Based on the Improved Double Fraction\u2010Order Multi\u2010Innovation Unscented Kalman Filter With Full Parameters Identification and Self\u2010Rectification of Covariance Matrices"],"prefix":"10.1002","volume":"53","author":[{"given":"Likun","family":"Xing","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering Anhui University of Science and Technology  Huainan China"},{"name":"Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center Anhui University of Science &amp; Technology  Huainan China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0007-1522-1606","authenticated-orcid":false,"given":"Hengqi","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering Anhui University of Science and Technology  Huainan China"}]},{"given":"Zhenyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering Hefei University  Hefei China"}]}],"member":"311","published-online":{"date-parts":[[2024,12,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e21587"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/en16010220"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2019.02.169"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4038075"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.136246"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2020.101976"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2022.03.036"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2021.102572"},{"key":"e_1_2_9_10_1","unstructured":"K. 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