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To shorten the charging time and optimize the charging strategy for new energy vehicles, the paper transforms the charging strategy optimization into a constrained path-finding problem using machine learning algorithms. The ant colony algorithm is employed to achieve rapid convergence, enabling the global co-optimization of the charging strategy and thermal management system capability. A segmented charging control strategy is proposed by comprehensively accounting for the effects of cell temperature rise and the vehicle thermal management system. Real-vehicle test results demonstrate that the optimized fast-charging time is reduced from 30 min to 27.5 min without battery overheating, verifying the effectiveness of the proposed strategy and providing a reference for advancing fast-charging technology in electric vehicles.

, authors=Junwei Rao, Cuina Jiang, Dan Wei, authorsList=Junwei Rao, Cuina Jiang, Dan Wei, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639101989610334, articleId=1309639099959567195, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=一种基于机器学习算法的充电优化策略, columnId=1309639101444358696, journalTitle=汽车文摘, columnName=新能源汽车充电技术专题, runingTitle=null, highlight=null, articleAbstract=

为了缩短新能源汽车充电时间、优化充电策略，基于机器学习算法，将充电策略优化问题转化为带约束条件的路径寻优问题，利用蚁群算法实现快速收敛，完成充电策略与热管理系统能力的全局协同优化。综合考虑电芯温升与整车热管理系统的影响，提出分段式充电控制策略。实车测试结果表明：优化后的快充时间由30 min缩短至27.5 min，且电池未出现超温现象，验证了所提优化策略的有效性，为推动电动汽车快充技术发展提供参考。

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