华南理工大学学报(自然科学版) ›› 2011, Vol. 39 ›› Issue (1): 147-151.doi: 10.3969/j.issn.1000-565X.2011.01.027

• 汽车工程 • 上一篇    下一篇

柴油机EGR温度的智能控制策略

王惜慧 黄正展 赵荣超 黄旭为 刘珐   

  1. 华南理工大学机械与汽车工程学院,广东广州510640
  • 收稿日期:2010-04-21 修回日期:2010-07-30 出版日期:2011-01-25 发布日期:2010-12-01
  • 通信作者: 王惜慧(1974一),女,博士,讲师,主要从事动力系统建模及控制研究 E-mail:xhwangr@scut.edu.cn
  • 作者简介:王惜慧(1974一),女,博士,讲师,主要从事动力系统建模及控制研究
  • 基金资助:

    广东省自然科学基金资助项目(B21B6070440);华南理工大学SRP项目(Y10901 10 );国家大学生创新实验项目(081056102)

Intelligent Control Strategy of EGR Temperature for Diesel Engine

Wang Xi-hui Huang Zheng-zhan Zhao Rong-chao Huang Xu-wei Liu Xuan   

  1. South China university of technology, mechanical and automotive engineering school, guangdong guangzhou 510640
  • Received:2010-04-21 Revised:2010-07-30 Online:2011-01-25 Published:2010-12-01
  • Contact: 王惜慧(1974一),女,博士,讲师,主要从事动力系统建模及控制研究 E-mail:xhwangr@scut.edu.cn
  • About author:王惜慧(1974一),女,博士,讲师,主要从事动力系统建模及控制研究
  • Supported by:

    广东省自然科学基金资助项目(B21B6070440);华南理工大学SRP项目(Y10901 10 );国家大学生创新实验项目(081056102)

摘要: 利用改进的BP神经网络算法,建立了样本柴油机排气温度的神经网络模型,通过柴油机台架实验采集柴油机转速、负荷、油耗、排气温度等参数作为神经网络模型学习样本,使用实验数据对所建立模型进行训练,并对该神经网络模型进行了误差分析,结果表明,所建神经网络模型反映了实验样机的排气温度变化规律,在测试数据范围内,排气温度辨识误差小于1.0%,满足计算要求.最后将神经网络预测模型与模糊推理结合,实现了柴油机排气再循环温度的智能控制.

关键词: BP神经网络, 柴油机, 排气温度, 排气再循环

Abstract:

In this paper,first,a neural network model describing the exhaust temperature of a diesel engine sample is established based on the improved BP neural network algorithm.Next,some data of engine speed,engine power,fuel consumption and exhaust temperature are obtained from beach tests,which are then used to train the established model.Finally,an error analysis is performed to verify the model.The results indicate that the established neural network model well describes the variation of exhaust temperature,and that the errors of the identification results,which are all less than 1%,meet the requirements of calculation.In addition,the intelligent temperature control of exhaust gas recirculation(EGR) is realized by combining the BP neural network model with the fuzzy inference.

Key words: BP neural network, diesel engine, exhaust temperature, exhaust gas recirculation