Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1565-1574.doi: 10.13229/j.cnki.jdxbgxb20200603

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Prediction of gasoline engine steady state exhaust based on model group prediction method

Tao CHEN1(),Jing QIN1,2(),Hua ZHAO1,Qing-peng SU1,3,Yong LYU3,Kai ZHONG1,Ying-bo WANG1,Yi-qiang PEI1   

  1. 1.State Key Laboratory of Engines，Tianjin University，Tianjin 300072，China
    2.Internal Combustion Engine Research Institute，Tianjin University，Tianjin 300072，China
    3.GAC Automotive Research & Development Center，Guangzhou 511434，China
  • Received:2020-08-10 Online:2021-09-01 Published:2021-09-16
  • Contact: Jing QIN E-mail:chent_666@tju.edu.cn;qinjing@tju.edu.cn

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Abstract:

This paper aims to reduce the workload of gasoline engine bench test and improve the experimental efficiency by numerical simulation technology. The model group prediction method （optimized artificial neural network method） was used to model and predict the steady-state emissions of NOx， CO and HC in the process of gasoline engine bench test. The results showed that the model group prediction method has high reliability and may improve the accuracy of prediction results compared with the traditional single model prediction method. The training data set of neural network modeling may be appropriately reduced by using the method of separating points， and the better prediction ability may still be maintained. In the process of project development， only 30% of the test quantities were needed， and the test results may be used for the training of neural network model， which may better predict the emission of the remaining working conditions. Through the validation analysis of other models， the model group prediction method had a good universality in the prediction process of engine steady-state exhaust.

Key words: gasoline, neural networks, prediction, engine exhausts, bench test

CLC Number: 

  • TK411

Table 1

Structure parameters of verify model"

类型排量/L压缩比缸数缸径×行程/mm
汽油机A1.609.50479.7×81.1
汽油机B1.809.60482.5×84.1
汽油机C2.0011.65482.5×92.8
柴油机A7.7917.506115×125
柴油机B9.0017.006123×132

Table 2

Performance parameters of verify model"

类型最大功率/kW

最大功率转

速/（r·min-1）

最大转矩/（N·m）最大扭矩转速/（r·min-1）
汽油机A14056002402400~5200
汽油机B1185000~62002501500~4500
汽油机C1475100~60002801700~5000
柴油机A21924009981400
柴油机B294190019001100~1450

Fig.1

Neural network structure diagram"

Fig.2

Predict flow chart of single training optimal model"

Fig.3

Flow chart of model group predicting method"

Table 3

Selection of model training data by interval point method"

隔点取点法数据分类转速/（r·min-1）
间隔一组转速选取训练数据

训练组

转速选取

1000、1500、2000、2800、 3600、4500、5500

预测组

转速选取

1250、1750、2400、3200、4000、4500、5000
间隔两组转速选取训练数据

训练组

转速选取

1000、1750、2800、4000、5500

预测组

转速选取

1250、1500、2000、2400、3200、3600、4500、5000
间隔三组转速选取训练数据

训练组

转速选取

1000、2000、3600、5500

预测组

转速选取

1250、1500、1750、2400、2800、3200、4000、4500、5000

Table 4

Comparison of judgement coefficients of NOx emission modeling and prediction by different neural network methods"

项目传统BP神经网络方法训练最优 模型预测模型群预测法
建模0.988 230.989 790.95~0.983 7
预测0.953 750.968 530.983 59

Table 5

Comparison of judgement coefficients of CO emission modeling and prediction by different neural network methods"

项目传统BP神经网络方法

训练最优

模型预测

模型群预测法
建模0.963 890.964 860.95~0.952 6
预测0.822 910.791 230.959 45

Table 6

Comparison of judgement coefficients of HC emission modeling and prediction by different neural network methods"

项目传统BP神经网络方法

训练最优

模型预测

模型群预测法
建模0.952 140.972 030.95~0.957 0
预测0.934 290.793 060.960 72

Fig.4

Prediction results of emissions by different neural network methods"

Table 7

Prediction of individual models in model group on emission under operating conditions"

项目NOxCOHC
模型群中模型个数2000200309
优于模型群预测法预测结果的模型个数200
优于模型群预测法预测结果的模型占比/%0.1000

Fig.5

Comparison of prediction effect between single model prediction method and model group prediction method"

Table 8

Judgement coefficient of single model in model group for emission prediction under operating conditions"

项目NOxCOHC
A组数据建模预测0.976 520.936 090.954 50
B组数据建模预测0.972 700.931 230.955 89

Table 9

Universality analysis of model group prediction method in emission prediction of various models types"

项目NOxCOHC
汽油机A建模Rtrain20.9~0.927 40.9~0.995 50.9~0.967 5
汽油机A预测Rpredict20.827 890.989 340.868 72
该预测结果优于单个模型预测结果的概率（汽油机A）/%95.7095.7095.20
汽油机B建模Rtrain20.8~0.851 40.8~0.898 60.75~0.792 1
汽油机B预测Rpredict20.806 620.861 880.534 50
该预测结果优于单个模型预测结果的概率（汽油机B）/%99.6597.1075.45
汽油机C建模Rtrain20.9~0.993 70.9~0.990 10.9~0.984 5
汽油机C预测Rpredict20.842 110.635 770.893 86
该预测结果优于单个模型预测结果的概率（汽油机C）/%79.10100.0098.95
柴油机A建模Rtrain20.9~0.999 90.9~1.00.9~1.0
柴油机A预测Rpredict20.972 610.955 170.992 18
该预测结果优于单个模型预测结果的概率（柴油机A）/%98.6590.7099.68
柴油机B建模Rtrain20.9~0.991 3—0.9~0.996 9
柴油机B预测Rpredict20.969 19—0.945 68
该预测结果优于单个模型预测结果的概率（柴油机B）/%83.15—86.75
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