{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:29:07Z","timestamp":1776821347290,"version":"3.51.2"},"reference-count":28,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Science and Technology Project of Xuzhou","award":["KC18068"],"award-info":[{"award-number":["KC18068"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51504255"],"award-info":[{"award-number":["51504255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:p> In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge\u2013discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg\u2013Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation. <\/jats:p>","DOI":"10.1177\/1550147719894526","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T08:43:04Z","timestamp":1576572184000},"page":"155014771989452","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system"],"prefix":"10.1177","volume":"15","author":[{"given":"Shuo","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3018-3958","authenticated-orcid":false,"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"An","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"bibr1-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2852491"},{"key":"bibr2-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/MIE.2017.2649104"},{"key":"bibr3-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/627083"},{"key":"bibr4-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2008.924173"},{"key":"bibr5-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2012.2210736"},{"issue":"335","key":"bibr6-1550147719894526","first-page":"1395","volume":"58","author":"Muhammad A","year":"2016","journal-title":"Renew Sustain Energy Rev"},{"key":"bibr7-1550147719894526","first-page":"1","volume-title":"2014 IEEE 36th international telecommunications energy conference (INTELEC)","author":"Stan A"},{"key":"bibr8-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2545338"},{"key":"bibr9-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2010.2089647"},{"key":"bibr10-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2013.12.046"},{"key":"bibr11-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2016.2603229"},{"key":"bibr12-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2008.08.103"},{"key":"bibr13-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2012.01.009"},{"key":"bibr14-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.electacta.2015.12.001"},{"key":"bibr15-1550147719894526","first-page":"1","volume-title":"2010 IEEE vehicle power and propulsion conference","author":"Wu G"},{"key":"bibr16-1550147719894526","first-page":"1458","volume-title":"2015 IEEE 10th conference on industrial electronics and applications (ICIEA)","author":"Guo L"},{"key":"bibr17-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2010.2043035"},{"key":"bibr18-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2012.2235474"},{"key":"bibr19-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2014.2314333"},{"key":"bibr20-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2015.2427659"},{"key":"bibr21-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2017.2664726"},{"key":"bibr22-1550147719894526","first-page":"2022","volume-title":"2014 IEEE international conference on mechatronics and automation","author":"Dong C"},{"key":"bibr23-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2017.03.881"},{"key":"bibr24-1550147719894526","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.04.085"},{"key":"bibr25-1550147719894526","first-page":"5641","volume-title":"IECON 2014 - 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