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Träfflista för sökning "LAR1:hh ;srt2:(1995-1999);pers:(Larsson Magnus)"

Sökning: LAR1:hh > (1995-1999) > Larsson Magnus

  • Resultat 1-4 av 4
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1.
  • Hellring, Magnus, et al. (författare)
  • Robust AFR estimation using the ion current and neural networks
  • 1999
  • Ingår i: SAE transactions. - New York : Society of Automotive Engineers. - 0096-736X. ; 108:03, s. 1585-1589
  • Tidskriftsartikel (refereegranskat)abstract
    • A robust air/fuel ratio "soft sensor" is presented based on non-linear signal processing of the ion current signal using neural networks. Care is taken to make the system insensitive to amplitude variations, due to e.g. fuel additives, by suitable preprocessing of the signal. The algorithm estimates the air/fuel ratio to within 1.2% from the correct value, defined by a universal exhaust gas oxygen (UEGO) sensor, when tested on steady state test-bench data and using the raw ion current signal. Normalizing the ion current increases robustness but also increases the error by a factor of two. The neural network soft sensor is about 20 times better in the case where the ion current is not normalized, compared with a linear model. On normalized ion currents the neural network model is about 4 times better than the corresponding linear model. Copyright © 1999 Society of Automotive Engineers, Inc.
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2.
  • Hellring, Magnus, et al. (författare)
  • Spark advance control using the ion current and neural soft sensors
  • 1999
  • Ingår i: SAE transactions. - New York : Society of Automotive Engineers. - 0096-736X. ; 108:03, s. 1590-1595
  • Tidskriftsartikel (refereegranskat)abstract
    • Two spark advance control systems are outlined; both based on feedback from nonlinear neural network soft sensors and ion current detection. One uses an estimate on the location of the pressure peak and the other uses an estimate of the location of the center of combustion. Both quantities are estimated from the ion current signal using neural networks. The estimates are correct within roughly two crank angle degrees when evaluated on a cycle to cycle basis, and roughly within one crank angle degree when the quantities are averaged over consecutive cycles.The pressure peak detection based control system is demonstrated on a SAAB 9000 car, equipped with a 2.3 liter low-pressure turbo charged engine, during normal highway driving. © 1998 Society of Automotive Engineers, Inc.
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3.
  • Wickström, Nicholas, et al. (författare)
  • Estimating pressure peak position and air-fuel ratio using the ionization current and artificial neural networks
  • 1997
  • Ingår i: IEEE Conference on Intelligent Transportation Systems. - Piscataway, N.J. : IEEE. - 0780342690 ; , s. 927-977
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • We propose two artificial neural network models which use the ionization current for estimation of the position of the pressure peak and the air-fuel ratio. The pressure peak position model produces estimates on a cycle-by-cycle basis for each of the cylinders. These estimates are twice as good as estimates obtained from a linear model. The air-fuel ratio model uses the universal exhaust gas oxygen sensor as reference; it produces estimates that are ten times better than estimates obtained fi om a linear model.
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4.
  • Wickström, Nicholas, 1970-, et al. (författare)
  • Neural Virtual Sensors — Estimation of Combustion Quality in SI Engines using the Spark Plug
  • 1998
  • Ingår i: ICANN 98. - London : Springer. - 9783540762638 - 9781447115991 ; , s. 215-220
  • Konferensbidrag (refereegranskat)abstract
    • We propose two virtual sensors which estimate the location of the pressure peak and the air-fuel ratio from measurements of the ionization current across the spark plug gap.The location of pressure peak virtual sensor produces estimates on a cycle-by-cycle basis for each of the cylinders. These estimates are twice as good as estimates obtained from a linear model.The air-fuel ratio virtual sensor uses the universal exhaust gas oxygen sensor as reference; it produces estimates that are ten times better than estimates obtained from a linear model.
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  • Resultat 1-4 av 4

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