CAJ | 학술논문

提出了一种基于GPRS的道路行驶工况数据的远程采集方法,并将其应用在电动汽车的实际运行中,获得电动汽车道路试验原始数据库.同时将自组织映射(SOM)神经网络引入到行驶工况的自学习中,通过SOM网络对原始数据进行运动学片段的聚类分析,构建出了电动汽车在实际运行中的3种典型工况,为电动汽车基于行驶工况的自适应优化控制策略提供了基础环节.所构建的行驶工况和其他行驶工况相比具有一般规律,表明应用SOM网络能够很好地实现道路行驶工况的自学习功能.
제출료일충기우GPRS적도로행사공황수거적원정채집방법,병장기응용재전동기차적실제운행중,획득전동기차도로시험원시수거고.동시장자조직영사(SOM)신경망락인입도행사공황적자학습중,통과SOM망락대원시수거진행운동학편단적취류분석,구건출료전동기차재실제운행중적3충전형공황,위전동기차기우행사공황적자괄응우화공제책략제공료기출배절.소구건적행사공황화기타행사공황상비구유일반규률,표명응용SOM망락능구흔호지실현도로행사공황적자학습공능.
A methodology to collect the driving cycle data remotely based on GPRS was presented and applied to a running electric vehicle to build a driving cycle database for road test. The self-organizing map (SOM) network was introduced into self-learning of driving cycle,so the cluster analysis was performed to classify kinematic sequence of original data. Based on the classification of kinematic sequence ,three types of typical driving cycles of electric vehicle road test were constructed and provided foundation for self-adapt optimal control strategy for electric vehicle. Compared with other driving cycles,the constructed driving cycles have common regularity,which shows that self-learning of driving cycle is perfectly realized by the application of SOM network.