摘要
[目的]为了给精确控制柴油机燃油喷射量提供基础支撑,针对预−主喷模式,提出一种以间隔时间、共轨压力和主喷脉宽为输入参数的主喷油量波动预测模型的构建方法。[方法]运用AMESim仿真模型进行模拟试验并采集数据,利用获取的数据得到单一工况下的间隔时间与主喷油量之间的关系;在上述基础上,考虑共轨压力和主喷脉宽对模型系数的影响,构建出完整的多工况主喷油量预测模型;使用多组不同组合的输入参数对所构建预测模型的准确性和可靠性进行验证。[结果]结果表明,构建的多工况主喷油量预测模型均方根误差为1.443 mm^(3),所需试验量可由n^(3)量级降至n^(2)量级。[结论]所提的预测模型具有较高的准确度,对基于模型的喷油量控制具有工程应用意义。
[Objectives]In order to provide the basis for accurate control of diesel engine fuel injection quantity,a method was proposed to establish the main injection quantity prediction model with input parameters as dwell time,common rail pressure and main injection pulse width for the pre-main injection mode.[Methods]AMESim simulation model was used to conduct simulation experiments and then collect data.Based on the data,the relationship between the dwell time and the main injection volume under single working condition was obtained.On this basis,the influence of common rail pressure and main injection pulse width on the model coefficient was introduced to build a complete multi-working condition main injection quantity prediction model.Multiple input parameter combinations were used to verify the accuracy and reliability of the prediction model of main injection quantity fluctuation.[Results]The results show that the rootmean-square error(RMSE)of the scheme of multi-working condition main injection quantity prediction model is 1.443 mm^(3).It can reduce the experiment amount required for modeling from n^(3) orders of magnitude to n^(2) orders of magnitude.[Conclusions]The proposed prediction model has sufficient accuracy,which is of great significance for engineering application of model-based fuel injection control.
作者
梁勇
费红姿
刘冰鑫
柳一林
朱腾飞
LIANG Yong;FEI Hongzi;LIU Bingxin;LIU Yilin;ZHU Tengfei(College of Power and Energy Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《中国舰船研究》
CSCD
北大核心
2021年第4期164-172,共9页
Chinese Journal of Ship Research
基金
国家自然科学基金资助项目(51879059)。
关键词
船用柴油机
高压共轨系统
主喷油量
油量波动
多工况
预测模型
marine diesel engine
high pressure common rail system
main injection quantity
fuel injection quantity fluctuation
multi-working condition
prediction model