Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1174-1185.doi: 10.13229/j.cnki.jdxbgxb20171041

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Cylinder pressure identification based on EKF and frequency⁃amplitude modulation Fourier series

De⁃jun WANG1,2(),Zhi⁃chao LYU1,2,Qi⁃ming WANG3,Jian⁃rui ZHANG4,Jian⁃nan DING2   

  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. College of Transportation, Jilin University, Changchun 130022, China
    4. R&D Center, China FAW Co. , Ltd. ，Changchun 130022, China
  • Received:2017-11-01 Online:2019-07-01 Published:2019-07-16

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Abstract:

To achieve a low?cost, real?time, non?invasive, high?accuracy soft cylinder pressure sensor, we propose a novel pressure identification method involving frequency?amplitude modulated Fourier series and an extended?Kalman?filter?optimized engine model, which is based on theories regarding explosion and burning together with Newton′s laws. The extended?Kalman?filter is used to estimate the optimal output of this iteration model. The optimal output of the speed iteration model is utilized to separately compute the frequency and amplitude of the cylinder pressure cycle?by?cycle. A standard engine working cycle, identified by the 24th order Fourier series, is determined. Using the frequency and the amplitude to modulate the Fourier series yields a complete pressure model. A commercial engine (EA211) provided by the China FAW Group corporate R&D center is used to verify the method. Test results show that this novel method possesses high accuracy and real?time capability, with an error percentage for speed below 10% and the percentage corresponding to power of pressure below 2% when Air?Fuel Ratio coefficient is setup at 0.85. Error percentage for speed below 1.5% and the percentage corresponding to power of pressure below 1.5% when Air?Fuel Ratio coefficient is setup at 0.95.Thus, the accuracy and feasibility of the proposed method are verified.

Key words: power machinery and engineering, engine cylinder pressure, extended Kalman filter(EKF), frequency modulation, amplitude modulation, speed iteration model

CLC Number: 

  • TK41

Fig.1

Flow chart of proposed method"

Fig.2

Schematic diagram of effective throttle valve"

Fig.3

Flow chart of engine energy transfer"

Fig.4

Relationship of speed in iteration model"

Fig.5

Schematic diagram of working cycle"

Fig.6

VW EA211 engine"

Fig.7

"

Fig.8

Speed tracking（λ=0.85）"

Fig.9

Error percentage of speed tracking（λ=0.85）"

Table 1

Parameters of the 24th order Fourier series(a)"

a0-a4 a5-a9 a10-a14 a15-a19 a20-a24
12.06 -1.215 -1.854 0.7365 -0.00471
-11.92 3.652 0.2843 -0.9149 0.3783
-3.768 -2.639 1.142 0.1323 -0.3231
9.395 0.1302 -1.264 0.5965 -0.00949
-4.813 1.882 0.324 -0.5427 0.257

Table 1

Parameters of the 24th order Fourier series(b)"

b1-b5 b6-b10 b11-b15 b16-b20 b21-b24
13.11 1.267 -1.775 0.291 0.2242
-11.71 1.927 0.9967 -0.8235 0.202
1.303 -2.787 0.3929 0.4077 -0.3184
5.68 1.365 -1.146 0.2995 0.1462
-5.136 0.8418 0.7361 -0.5088 -

Fig.10

Pressure identification（λ=0.85）"

Fig.11

Power of cylinder pressure（λ=0.85）"

Fig.12

Error percentage of power of cylinder pressure（λ=0.85）"

Fig.13

"

Fig.14

Speed tracking（λ=0.95）"

Fig.15

Error percentage of speed tracking（λ=0.95）"

Fig.16

Pressure identification（λ=0.95）"

Fig.17

Power of cylinder pressure（λ=0.95）"

Fig.18

Error percentage of power of cylinder pressure（λ=0.95）"

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