Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (1): 49-62.doi: 10.13229/j.cnki.jdxbgxb20190906

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Data-driven modeling and receding optimization control of diesel engine combustion process

Yun-feng HU1,2(),Yi-tong DING2,Zhi-xin ZHAO3,Bing-jing JIANG2,Jin-wu GAO1,2()   

  1. 1.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
    3.School of Mathematics，Changchun Normal University，Changchun 130032，China
  • Received:2019-09-23 Online:2021-01-01 Published:2021-01-20
  • Contact: Jin-wu GAO E-mail:huyf@jlu.edu.cn;gaojw@jlu.edu.cn

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Abstract:

Aiming at the problems of diesel engine combustion process complexity， mechanism modeling difficulty， dynamic coupling and constraints， a data-driven modeling and rolling optimization control method for diesel engine combustion process was proposed to reduce fuel consumption and torque tracking， and to limit NOx emission and control volume. First， a 2L diesel engine model in GT-suite is chose as a plant. The influences of VGT and EGR control valve opening， injection angle and injection amount on fuel consumption rate， NOx emission and crankshaft output torque are analyzed. Second， using input and output data， a control-oriented data-driven prediction model is obtained by using subspace identification method. Third， to reduce the fuel consumption rate and torque tracking error， and the NOx emission and control input are considered as constraint， the optimal control input is obtained by solving a multi-objective optimal control problem. Finally， the co-simulation based on MATLAB and GT-suite is presented to verify the effectiveness and superiority of the data-driven receding optimization control method.

Key words: power machinery engineering, diesel engine, subspace identification, data-driven, receding optimization control

CLC Number: 

  • TK411

Fig.1

2 L diesel engine model in GT-suite"

Fig.2

Input data of kinetic analysis"

Fig.3

Output data of kinetic analysis"

Fig.4

Data-driven rolling optimization algorithm"

Fig.5

Input data of model validation"

Fig.6

Comparison of model validation results"

Fig.7

Error curve of mold validation"

Fig.8

Engine speed under ETC condition"

Fig.9

Torque under ETC condition"

Fig.10

Control input (urban working condition)"

Fig.11

System output (urban working condition)"

Fig.12

Torque tracking error comparison"

Fig.13

Control input (rural working condition)"

Fig.14

System output (rural working condition)"

Fig.15

Torque tracking error comparison"

Fig.16

Control input (expressway working condition)"

Fig.17

System output (expressway working condition)"

Fig.18

Torque tracking error comparison"

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