Research on Control Strategy of APSO-Optimized Fuzzy PID for Series Hybrid Tractors

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC World Electric Vehicle Journal Pub Date : 2023-09-11 DOI:10.3390/wevj14090258
Liyou Xu, Yiting Wang, Yanying Li, Jinghui Zhao, Mengnan Liu
{"title":"Research on Control Strategy of APSO-Optimized Fuzzy PID for Series Hybrid Tractors","authors":"Liyou Xu, Yiting Wang, Yanying Li, Jinghui Zhao, Mengnan Liu","doi":"10.3390/wevj14090258","DOIUrl":null,"url":null,"abstract":"Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"20 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Electric Vehicle Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/wevj14090258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
串联混合动力拖拉机模糊PID优化控制策略研究
能源管理策略对于提高混合动力拖拉机的燃油经济性和减少废气排放至关重要。以系列柴油-电动混合动力拖拉机为研究对象,提出了一种多工作点模糊PID控制策略,以达到更好的燃油经济性和更好的控制性。为了进一步提高车辆的经济性,采用自适应粒子群优化方法对模糊PID控制器的关键参数进行优化。在AVL-Cruise和Matlab/Simulink环境下建立了耕作方式和运输方式的联合仿真模型。仿真结果表明,在两种运行模式下,自适应粒子群优化多工作点模糊PID控制策略(APSO-MOPFPCS)的等效油耗比发动机单工作点控制策略(ESOPCS)分别降低18.3%和15.0%。与建交部相比,分别减少了9.5%和4.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
期刊最新文献
Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network Parameter Compensation for the Predictive Control System of a Permanent Magnet Synchronous Motor Based on Bacterial Foraging Optimization Algorithm Subcooled Liquid Hydrogen Technology for Heavy-Duty Trucks Time-Sensitive Network Simulation for In-Vehicle Ethernet Using SARSA Algorithm Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1