000			03827nam a2200457 a 4500
005			20260818223752.0
008			140619s2011 th ad ||| 000 0 eng d
035			_a.b12133772
099		9	_aAIT Thesis no.ISE-11-09
100	0		_aSuradech Doungpummet
245	1	0	_aSystem identification of a spark ignition engine for natural gas injection rate control
260			_aPathum Thani : _bAsian Institute of Technology, _c2011
300			_a34 p. : _bill. (some col.), charts
490	1		_aThesis ; _vno. ISE-11-09
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2011
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Mechatronics, School of Engineering and Technology
520			_aNeural networks for identification and control of a spark ignition engine using natural gas can be improved its dynamic. Basically, converted engines result in a significant power loss. The fuel quality variation and low volumetric efficiency of natural gas require numerous modification and calibration efforts, but the optimum operations can not be guaranteed. The vehicle was op~rated on both a test bench and practical driving cycles. The parameters such as throttle position and fuel injection duration were excited, while an oxygen concentration was observed over the whole operating range. The neural network has capability to identify the relation between natural gas injection duration and the engine states. According to these experiments, the correctness of neural network is between 91.2% to 93.4%.Therefore, the injection duration for each operating point was calculated from the neural network model. A interpolated look-up table is suitable for a fuel injection control. In this case, a test vehicle is Toyota Altis 1.6 1, fourcylinder engine equipped with compressed natural gas components. In additional, I adopt an ARM 32-bit microcontroller as a rapid prototype. The embedded controller contained the look-up table that used during a transient period. For steady-state responses, the system was combined with a PI controller. Thus, the experimental results can be confirmed the effectiveness of vehicle responses and also reduction of the calibration efforts. Finally, this similar methodology can be applied to other kinds of engines or fuels and allow inexpensive adjustments.
650		0	_aSpark ignition engines
650		0	_aGas as fuel
650		0	_aSystem identification
700	0		_aManukid Parnichkun, _eChairperson
700	1		_aAfzulpurkar, Nitin V., _eExamination Committee
700	1		_aDailey, Mathew N., _eExamination Committee
710	2		_aNational Science and Technology Development Agency (NSTDA), Thailand, _eScholarship donor
710	2		_aRoyal Thai Government Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ISE-11-09
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B11722
907			_a.b12133772 _bmnait _cj
902			_a240404
998			_b0 _c140619 _dm _ea _fj _g0
962			_a000:001:PDF:b1213377:001533:0:0:0:0:0:0 _tAbstract--AIT Thesis no.ISE-11-09 _vn
945			_lmnait
945			_lmnait
945			_lmnarc
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