Abstract
AbstractA proper technology for measuring the volume of fuel is of utmost importance in the automotive sector. The fuel gauges are scandalously imprecise, showing empty when there are gallons left in the tank and showing full for the first 50 miles. In the current scenario, there is no direct volume measurement of the fuel present in the tank. If the tank is linear, the volume of the tank can be calculated in terms of level. But, the fuel tank present in automobiles is irregular in shape and highly nonlinear. Hence, there is a need to develop an indigenous technique to measure the volume of fuel in such irregular-shaped fuel tanks which actually rolls down to identifying an advanced machine learning technique to model the nonlinearity that exists in the measurement. This advanced technology employing the potentiometer can be used in any irregular-shaped tank in which the volume to voltage relationship is nonlinear in nature and also applied to any existing level sensors. The primary sensing technique, the calibration process, the training, validation, and testing of the nonlinear model for accurate measurement of volume in a fuel tank is discussed in this chapter.KeywordsFuel tankVolume measurementCalibrationNonlinearMachine learningNeural networksLabVIEW
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