{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:35:23Z","timestamp":1777703723677,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T00:00:00Z","timestamp":1528156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>In the interest of the hybrid electric vehicle(HEV) real-time road gradient and vehicle load(driving condition) effective identification during the running process, this work takes the series\u2013parallel HEV as the research object and studies on the dynamic identification mechanism of slope and load, based on the analysis of its structural parameters. Firstly, vehicle\u2019s driving condition identification model is developed, and the optimization goal function is established using the least square method. Secondly, six different kinds of particle swarm optimization(PSO) algorithm are used for the recognition of vehicle\u2019s driving condition, and the results show that hybrid PSO algorithm based on hybrid training algorithm has better calculation accuracy for this problem. Finally, Experiments are carried out to verify the driving condition recognition method based on PSO algorithm. Through the acquisition of a real vehicle data during the running process, road grade and vehicle mass are estimated by using the proposed method, and the effectiveness of the proposed method is proved through comparison of errors between recognition results and true value.<\/jats:p>","DOI":"10.3233\/jifs-169570","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T14:30:07Z","timestamp":1528209007000},"page":"87-98","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Study on HEV\u2019s driving condition recognition method based on PSO algorithm"],"prefix":"10.1177","volume":"35","author":[{"given":"Guo","family":"Hailong","sequence":"first","affiliation":[{"name":"School of Automobile and Construction Machinery, Guangdong Communication Polytechnic, Guangzhou, China"},{"name":"College of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"}]},{"given":"Wei","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,5]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.12.048"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"ChenZ. XiongR. WangC.et al. An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle Applied Energy(2016).","DOI":"10.1016\/j.apenergy.2016.01.071"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2009.2014385"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"SahlholmP. JohanssonK.H.Segmented road grade estimation for fuel efficient heavy duty vehicles Proc IEEE Conf Decis Control United States: IEEE 2010 pp. 1045\u20131050.","DOI":"10.1109\/CDC.2010.5717298"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"VahidiA. DruzhininaM. StefanopoulouA. Simultaneous Mass and Time-Varying Grade Estimation for Heavy-Duty Vehicles ProcAmControl Conf United States: IEEE 2003 pp. 4951\u20134956.","DOI":"10.1109\/ACC.2003.1242508"},{"key":"e_1_3_1_7_2","first-page":"1,0063","volume-title":"Towards real-time identification of electric vehicle mass. APAC 2013","author":"Wilhelm E.","year":"2013","unstructured":"WilhelmE., BornaticoR., WidmerR.Towards real-time identification of electric vehicle mass. APAC 2013. United States: SAE International, 2013, 1,0063."},{"issue":"11","key":"e_1_3_1_8_2","first-page":"1691","article-title":"Experimental research on recursive least squares estimation of vehicle mass","volume":"40","author":"Yuan F.","year":"2012","unstructured":"YuanF., ZhuopingY. and X.Lu, Experimental research on recursive least squares estimation of vehicle mass, Tongji Daxue Xuebao40(11)(2012), 1691\u20131697.","journal-title":"Tongji Daxue Xuebao"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"RhodeS. GauterinF.Vehicle mass estimation using least-squares approach IEEE Conf Intell Transport Syst Proc United States: IEEE 2012 pp. 1584\u20131589.","DOI":"10.1109\/ITSC.2012.6338638"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"FathyH.K. KangD. SteinJ.L.Online vehicle mass estimation using recursive least squares and supervisory data extraction Proc Am Control Conf. United States: IEEE 2008 pp. 1842\u20131848.","DOI":"10.1109\/ACC.2008.4586760"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21765-4_88"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"RyanJ. BevlyD. LuJ.Robust sideslip estimation using GPS road grade sensing to replace a pitch rate sensor Conf Proc IEEE Int Conf Syst Man Cybern United States: IEEE 2009 pp. 2026\u20132031.","DOI":"10.1109\/ICSMC.2009.5346320"},{"key":"e_1_3_1_13_2","unstructured":"BaeH.S. RyuJ. GerdesC.Road grade and vehicle parameter estimation for longitudinal control using GPS IEEE Conf Intell Transport SystProc ITSC United States: IEEE 2001 pp. 166\u2013171."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.09.047"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"KennedyJ. EberhartR.Particle swarm optimization Proc IEEE Int Neural Netw Conf 1995 pp. 1942\u20131948.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.985692"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"HongY.Y. LinF.J. ChenS.Y.et al. A novel adaptive elite-based particle swarm optimization applied to VAR optimization in electric power systems Math Probl Eng2014 761403.","DOI":"10.1155\/2014\/761403"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.07.013"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.05.025"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2006.889132"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169570","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169570","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:25Z","timestamp":1777455565000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,5]]},"references-count":19,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,7,27]]}},"alternative-id":["10.3233\/JIFS-169570"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169570","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,5]]}}}