{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T03:11:39Z","timestamp":1778555499939,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai MAHLE Thermal Systems Co., Ltd.","award":["23H00706"],"award-info":[{"award-number":["23H00706"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively.<\/jats:p>","DOI":"10.3390\/systems11080431","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T09:13:44Z","timestamp":1692350024000},"page":"431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-0931","authenticated-orcid":false,"given":"Debao","family":"Dai","sequence":"first","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Digital Management, Shanghai MAHLE Thermal Systems Co., Ltd., Shanghai 201206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhao","sequence":"additional","affiliation":[{"name":"SILC Business School, Shanghai University, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jclepro.2022.133708","article-title":"Forecasting the development trend of new energy vehicles in China by an optimized fractional discrete grey power model","volume":"372","author":"Liu","year":"2022","journal-title":"J. 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