摘要
为实现车辆行驶时的工况在线识别,提出一种基于聚类分析的混合动力汽车在线工况识别方法。利用K-Means算法对车辆仿真软件ADVISOR文件库中所有工况进行分类,得到不同工况类别的行驶特征参数区间;在车辆运行过程中划分固定的时间间隔,周期性地计算车辆实时特征参数,通过与特征参数区间匹配得到当前行驶工况;在MATLAB/Simulink中搭建在线工况识别模块,嵌入某型串联式混合动力汽车的仿真模型模拟验证。结果表明:添加在线工况识别模块不影响车辆仿真模型正常运行,该模块对随机工况的在线识别效果较好,识别精准度达86%以上;相比于传统的电能消耗控制策略,基于在线工况识别方法提出的混合动力汽车控制优化策略减少了10.74%的等效燃油消耗量,提升了燃油经济性。
In order to realize the online driving condition recognition of vehicles,a method of online vehicle driving condition recognition of hybrid electric vehicle(HEV)based on cluster analysis was proposed.K-Means algorithm was used to classify all driving conditions in the document library of vehicle simulation software ADVISER,and the driving characteristic parameter intervals of different driving conditions were obtained.In the process of vehicle running,a fixed time interval was divided,the real-time characteristic parameters of the vehicle were calculated periodically,and the current driving condition was obtained by matching with the characteristic parameter interval.The online working recognition module was built in MATLAB/Simulink,and the simulation model of a series hybrid electric vehicle was embedded to simulate and verify.The results show that adding online identification module does not affect the normal running of the vehicle simulation model,the module of random condition online identification effect is good,recognition accuracy reaches more than 86%.Compared with the traditional charge depleting control strategy,the control optimization strategy of HEV based on this method can reduce the equivalent fuel consumption by 10.74%,and improves the fuel economy.
作者
牛礼民
徐瑞康
朱奋田
邓末芝
徐家义
NIU Limin;XU Ruikang;ZHU Fentian;DENG Mozhi;XU Jiayi(School of Mechanical Engineering,Anhui University of Technology,Maanshan 243032,China;School of Mechanical and Electrical Engineering,Soochow University,Suzhou 215137,China;School of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China)
出处
《安徽工业大学学报（自然科学版）》
CAS
2021年第4期385-392,共8页
Journal of Anhui University of Technology（Natural Science）
基金
浙江省激光加工机器人重点实验室/中国机械工业激光精细加工与检测技术重点实验室开放基金(Lzsy-07)
安徽省教学研究重点项目(2019jyxm0140)
安徽工业大学省级大学生创新创业项目(S201910360274)。
