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Titolo:
Cylinder pressure-based spark advance control for SI engines
Autore:
Park, S; Yoon, P; Sunwoo, M;
Indirizzi:
Hanyang Univ, Dept Automot Engn, Sungdong Ku, Seoul 133791, South Korea Hanyang Univ Seoul South Korea 133791 dong Ku, Seoul 133791, South Korea Mando Corp, Pyung Taek 451821, Kyonggi, South Korea Mando Corp Pyung TaekKyonggi South Korea 451821 21, Kyonggi, South Korea
Titolo Testata:
JSME INTERNATIONAL JOURNAL SERIES B-FLUIDS AND THERMAL ENGINEERING
fascicolo: 2, volume: 44, anno: 2001,
pagine: 305 - 312
SICI:
1340-8054(200105)44:2<305:CPSACF>2.0.ZU;2-J
Fonte:
ISI
Lingua:
ENG
Keywords:
cylinder pressure; MBT(minimum spark advance for best torque); LPP(location of peak pressure); hook-back; feedforward neural network; individual cylinder MBT control;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
11
Recensione:
Indirizzi per estratti:
Indirizzo: Park, S Hanyang Univ, Dept Automot Engn, Sungdong Ku, 17 Hangdang Dong, Seoul 133791, South Korea Hanyang Univ 17 Hangdang Dong Seoul South Korea 133791 outh Korea
Citazione:
S. Park et al., "Cylinder pressure-based spark advance control for SI engines", JSME I J B, 44(2), 2001, pp. 305-312

Abstract

The introduction of inexpensive cylinder pressure sensors provides new opportunities for precise engine control. This paper presents a spark advance control Strategy based upon cylinder pressure in spark ignition engines. Itis well known that the location of peak pressure (LPP) reflects combustionphasing and can be used for controlling the spark advance. The well-known problems of the LPP-based spark advance control method are that many samples of data are required and there is loss of combustion phasing detection capability due to hook-back at late burn conditions. To solve these problems,a multi-layer feedforward neural network is employed. The LPP and hook-back are estimated, using the neural network, which needs only five output voltage samples from the pressure sensor. The neural network plays an important role in mitigating the A/D conversion load of an electronic engine controller by increasing the sampling interval from 1 degrees crank angle(CA) to 20 degrees CA. A proposed control algorithm does not need a sensor calibration and pegging (bias calculation) procedure because the neural network estimates the LPP from the raw sensor output voltage, The estimated LPP can beregarded as a good index for combustion phasing, and can also be used as an MBT control parameter. The feasibility of this methodology is closely examined through steady and transient engine operations to control individual cylinder spark advances. The experimental results have revealed a favorableagreement of optimal combustion phasing in each cylinder.

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Documento generato il 29/05/20 alle ore 19:52:58