{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:10:21Z","timestamp":1775229021658,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key R&amp;D Program of China","award":["2018YFB0106100"],"award-info":[{"award-number":["2018YFB0106100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB\/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.<\/jats:p>","DOI":"10.3390\/s21165370","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T09:03:53Z","timestamp":1628499833000},"page":"5370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction"],"prefix":"10.3390","volume":"21","author":[{"given":"Ming","family":"Ye","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China"}]},{"given":"Kai","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9768-328X","authenticated-orcid":false,"given":"Yonggang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"ref_1","first-page":"51","article-title":"Key Problems and Research Progress of Energy Saving Optimization for Intelligent Connected Vehicles","volume":"1","author":"Hong","year":"2021","journal-title":"China J. 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