| 基于多传感器融合的电喷汽油机过渡工况进气流速预测模型 |
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| 引用本文： | 侯志祥,吴义虎,申群太,袁翔. 基于多传感器融合的电喷汽油机过渡工况进气流速预测模型[J]. 高技术通讯, 2006, 16(1): 32-35 |
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| 作者姓名： | 侯志祥 吴义虎 申群太 袁翔 |
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| 作者单位： | 中南大学信息科学与工程学院,长沙,410083;长沙理工大学汽车与机械工程学院,长沙,410076;长沙理工大学汽车与机械工程学院,长沙,410076;中南大学信息科学与工程学院,长沙,410083 |
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| 摘 要： | 为了解决车用汽油发动机工作在过渡工况时,进气状态变化大,空气流量传感器的滞后响应严重影响空燃比控制精度的问题,提出了一种基于多传感器融合的过渡工况进气流速的预测模型,建立了过渡工况进气流速预测的径向基神经网络的拓朴结构,以HL495Q电喷汽油机加减速工况实验数据进行离线训练,仿真结果表明该预测模型能准确地预测过渡工况的空气进气流速,为精确及时地测试汽油机空气进气流量提供了一种新的方法.
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| 关 键 词： | 发动机 过渡工况 进气流速 神经网络 多传感器融合 预测 |
| 收稿时间： | 2005-04-05 |
| 修稿时间： | 2005-04-05 |
A forecasting model of induction air flow ratio for SI engine during transient conditions using multi-sensor data fusion |
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| Hou Zhixiang,Wu Yihu,Shen Quntai,Yuan Xiang. A forecasting model of induction air flow ratio for SI engine during transient conditions using multi-sensor data fusion[J]. High Technology Letters, 2006, 16(1): 32-35 |
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| Authors: | Hou Zhixiang Wu Yihu Shen Quntai Yuan Xiang |
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| Affiliation: | 1. School of Infoltnation Science and Engineering, Central South University, Changsha 410083;2. College of Automobile anti Mechanical Engineering, Changsha University of Science and Technology, Changsha 410076 |
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| Abstract: | For solving the problem that during transient conditions, the serious fluctuation of air induction state and tile lagging response of the air flow sensor seriously affect the accuracy of air fuel ratio control, the paper has provided a forecasting model of induction air flow ratio for SI engine during transient conditions using muhi-sensor data fusion, and established the topulogical stmcture of radial basis function neural network model for forecasting induction air flow ratio. The model was trained using the experiment datum of HIA95Q engine during transient conditions, and the simulation results showed that the model can accurntely forecast induction air flow ratio, which provided a new method of measuring the induction air flow accurately and timely. |
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| Keywords: | engine transient conditions induction air flow ratio neural networks multi-sensor data fusion forecasting |
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