Article(id=1325194268725359238, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194266577875587, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2023.03.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1672675200000, receivedDateStr=2023-01-03, revisedDate=1684771200000, revisedDateStr=2023-05-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880534583, onlineDateStr=2026-11-05, pubDate=1692028800000, pubDateStr=2023-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880534583, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880534583, creator=13701087609, updateTime=1793880534583, updator=13701087609, issue=Issue{id=1325194266577875587, tenantId=1146029695717560320, journalId=1319690209941618849, year='2023', volume='4', issue='3', pageStart='1', pageEnd='70', issueExtLink='null', onlineDate='null', pubDate='1692028800000', pubDateStr='2023-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880534070, creator='13701087609', updateTime=1793880534070, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=14, endPage=23, ext={EN=ArticleExt(id=1325194269199315591, articleId=1325194268725359238, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on the energy control optimized strategy of electric tractor composite power supply based on improved genetic algorithm, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming to solve the problems of insufficient capacity and high consumption of electric tractors, an optimal control strategy of composite power supply system based on battery plus super capacitor was proposed. By analyzing the topological structure of the electric tractor complex power system and the resistance characteristics of the electric tractor under various operating conditions, a complex power system structure scheme suitable for electric tractor was selected and a test condition suitable for electric tractor was designed. At the same time, the power demand of the electric tractor drive motor under the test condition was determined. According to the charging and discharging characteristics of the main power components of the electric tractor composite power system, the relevant mathematical model was established, and the simulation model of the electric tractor complex power system was built in Simulink. In addition, according to the load demand of the electric tractor under the test condition and the discharge characteristics of the complex power supply system, a fuzzy control strategy of the complex power supply system was formulated. Taking the minimum current at the maximum power discharge of the battery as the optimization goal, the improved genetic algorithm was used to optimize the fuzzy control parameters. The models of the composite power supply system under different conditions such as single power supply and logic threshold control strategy was built for comparative simulation. The results showed that: compared with the single power supply, logic threshold control strategy and fuzzy control strategy, the peak current battery with the improved genetic algorithm can be reduced by 63%, 62.9% and 43.9% respectively, the average current was respectively reduced 24.8%, 3.2% and 8.5%. It is also shown that this strategy is beneficial to reduce the peak current battery and prolong the service life of the battery.

, authors=Qiang YU¹, Xueyan LI¹, Xinjia PAN¹, Xionglin HE¹, Wentao FAN², Fang WANG², Yu WANG², authorsList=Qiang YU, Xueyan LI, Xinjia PAN, Xionglin HE, Wentao FAN, Fang WANG, Yu WANG, 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=1325194269446779528, articleId=1325194268725359238, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=基于改进遗传算法的电动拖拉机复合电源优化控制策略研究, columnId=null, journalTitle=智能化农业装备学报（中英文）, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对目前电动拖拉机续航能力不足、电源系统能耗高等问题，提出了一种基于蓄电池加超级电容辅能的复合电源系统能量优化控制策略。通过分析电动拖拉机复合电源系统的拓扑结构以及电动拖拉机多种作业工况阻力特性，选择了一种适合电动拖拉机的复合电源系统结构方案并设计了一种适合电动拖拉机的待测试工况，同时确定了该待测试工况下电动拖拉机驱动电机的功率需求。根据电动拖拉机复合电源系统主要动力组成部件的充放电特性，建立其相关数学模型，并在Simulink中搭建了电动拖拉机复合电源系统的仿真模型。另外，根据电动拖拉机在待测试工况下的负荷需求及复合电源系统的放电特性制定了复合电源系统能量控制模糊策略，并以蓄电池最大功率放电时的电流最低为优化目标，利用改进的遗传算法对模糊控制参数进行了优化，分别搭建了采用单电源、逻辑门限值控制策略等不同情况下复合电源系统的模型进行了对比仿真。结果表明：与采用单电源、逻辑门限值控制策略、模糊控制策略对比，利用改进遗传算法优化的模糊控制策略的蓄电池最大电流分别降低了63%、62.9%及43.9%；平均电流分别降低了24.8%、3.2%及8.5%，证明了该控制策略在降低蓄电池大电流放电、延长蓄电池使用寿命上的有效性。

, authors=余强¹, 李雪妍¹, 潘信佳¹, 何雄林¹, 范文涛², 王芳², 王玉², authorsList=余强, 李雪妍, 潘信佳, 何雄林, 范文涛, 王芳, 王玉, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=OLVX0bbZMoVrMkYSQM0E5A==, magXml=3fekQ3F/8YxD2x/ciTpSrw==, pdfUrl=null, pdf=FVDkYJS4A0BhFBY/Ol+VfA==, pdfFileSize=1090630, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=BNxjvncDOIVdrigJcJIuTA==, mapNumber=null, fund=null)}, authors=[Author(id=1325194298970473318, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194268725359238, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=yuqiang_21st@sina.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1325194299054359400, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194268725359238, authorId=1325194298970473318, language=EN, stringName=Qiang YU, firstName=Qiang, middleName=null, lastName=YU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1325194299180188521, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194268725359238, authorId=1325194298970473318, language=CN, stringName=余强, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.中国农业大学信息与电气工程学院，北京市，100083, bio={"content":"

余强，男，1984年生，湖南岳阳人，博士，教授，博导；研究方向为农村配电网运行控制、电动农机及优化控制。E-mail：yuqiang_21st@sina.com

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余强，男，1984年生，湖南岳阳人，博士，教授，博导；研究方向为农村配电网运行控制、电动农机及优化控制。E-mail：yuqiang_21st@sina.com

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Modeling and simulation research on hybrid energy storage system for pure electric vehicles [D]. Jinzhou: Liaoning University of Technology, 2016., articleTitle=null, refAbstract=null), Reference(id=1325194310706135992, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194268725359238, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=35, authorNames=null, journalName=null, refType=null, unstructuredReference=KERMANI S, DELPRAT S, GUERRA T M, et al. Predictive energy management for hybrid vehicle [J]. 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