摘要
本文中采用主成分分析和模糊聚类相结合的行驶工况识别方法进行纯电动汽车续驶里程的估算。首先选取20个具有代表性的循环工况数据,将其划分为215个工况片段,并选用12个特征参数对其进行主成分分析、模糊C聚类分析和行驶工况识别;然后在MATLAB/Simulink下建立纯电动汽车整车模型,进行行驶工况识别、整车能量消耗和续驶里程仿真估算;最后在转鼓试验台上进行ECE15工况下实车测试验证,结果表明:续驶里程仿真估算值与测试值的最大绝对误差为1.905km,平均绝对误差为0.742km,相对误差小于3%。
In this paper, a driving cycle identification method is adopted, which combines principal compo-nent analysis with fuzzy clustering, to estimate the driving range of battery electric vehicle. Firstly twenty represent-ative driving cycle data are selected and divided into 215 cycle segments, and 12 characteristic parameters are cho-sen to conduct principal component analysis, fuzzy C-means clustering and driving cycle identification. Then a mod-el for battery electric vehicle is established with MATLAB/Simulink to perform driving cycle identification and the simulation estimations of vehicle energy consumption and driving range. Finally a real vehicle validation test is car-ried out on drum test bench with ECE15 cycle. The results show that compared with test data, the maximum abso-lute error of simulated estimates is 1. 905km, and the corresponding average absolute error and relative error are 0. 742km and less than 3% respectively.
出处
《汽车工程》
EI
CSCD
北大核心
2014年第11期1310-1315,共6页
Automotive Engineering
基金
国家"863"节能与新能源汽车重大专项(2012AA111401)
安徽省自然科学基金(1208085ME78)资助
关键词
纯电动汽车
续驶里程
行驶工况
主成分分析
模糊C聚类分析
battery electric vehicle
driving range
driving cycle
principal component analysis
fuzzy C-means clustering
