中国机械工程2017，Vol.28Issue(22)：2695-2700,6.DOI:10.3969/j.issn.1004-132X.2017.22.008
基于工况识别与多元非线性回归优化的能量管理策略
Energy Management Strategy Based on Type Recognition and Multivariate Nonlinear Regression Optimization
摘要
Abstract
The type recognition algorithm of driving conditions was studied based on LVQ neural network,to provide the basis for the intelligent management strategy of hybrid electric vehicles.First,11 characteristic parameters were extracted from 4 typical road type conditions and the 3 kinds of standard cycle conditions to train the data.Then,the LVQ neural network type recognition algorithm of driving condition was developed.Based on this,a hybrid power system was as an example,which combined with multiple nonlinear regression analysis to develop the corresponding control strategy.Finally,LVQ neural network type recognition simulation model of driving condition was established based on the Simulink simulation platform,type recognition tests were carried on under the Chinese city typical cycle road conditions,standard condition recognition tests were carried on by constructing UDDS+NYCC+UDDS driving conditions.The results show that the established LVQ neural network may accurately identify the type of driving condition types and the control effectiveness of the energy management strategy is improved effectively.关键词
学习向量化神经网络/工况识别/循环工况/能量管理Key words
learning vector quantization(LVQ) neural network/type recognition/driving cycle type/energy management分类
交通工程引用本文复制引用
孙蕾,林歆悠,林国发..基于工况识别与多元非线性回归优化的能量管理策略[J].中国机械工程,2017,28(22):2695-2700,6.基金项目
国家自然科学基金资助项目(51505086) （51505086）
