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An IMMPF is applied to different state equations. The battery capacity degradation model is very important in the prediction of the RUL of Li-ion batteries. The IMMPF method is applied to the estimation of the RUL of Li-ion batteries using the three improved models. Three case studies are provided to validate the proposed method. The experimental results show that the one-dimensional state equation particle filter (PF) is more suitable for estimating the trend of battery capacity in the long term. The proposed method involving interacting multiple models demonstrated a stable and high prediction accuracy, as well as the capability to narrow the uncertainty in the PDF of the RUL prediction for Li-ion batteries.<\/jats:p>","DOI":"10.1177\/0142331220961576","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T01:10:06Z","timestamp":1606353006000},"page":"3049-3063","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter"],"prefix":"10.1177","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5117-7856","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and Informatics, Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, China"}]},{"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, China"}]},{"given":"Lifei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, China"}]},{"given":"Xiaochen","family":"Zhang","sequence":"additional","affiliation":[{"name":"NARI Technology Co., Ltd., China"}]},{"given":"Michael","family":"Pecht","sequence":"additional","affiliation":[{"name":"CALCE, University of Maryland, USA"}]}],"member":"179","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.08.138"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/ip-rsn:20030741"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.10.095"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-7753(02)00305-1"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2009.05.036"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIM.2008.4579269"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2011.08.040"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.03.086"},{"issue":"4","key":"e_1_3_3_10_1","first-page":"2645","article-title":"Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling","volume":"63","author":"Hu X","year":"2016","unstructured":"Hu X, Jiang J, Cao D, Egardt B (2016) Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling. 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