Precise control of an internal combustion engine for the reduction of exhaust emissions benefits substantially from accurate measurement of the air–fuel or λ ratio. This paper describes the use of the spark plug as a combustion sensor. The time-varying spark-voltage profile is acquired and used to train a neural network. The neural network is able to estimate the λ ratio. Minimal additional instrumentation is required. Therefore, this method potentially provides a cheap and robust sensor for measuring the λ ratio. The work described here involved devising an appropriate data acquisition system and undertaking experiments to validate the sensor technique. Vectors representing the spark-voltage profile were acquired at three different λ ratio operating points and used to train and test the neural network to ascertain its ability to determine the λ ratio accurately and linearly over a range. The results have supported the application of the spark-voltage profile as a means of λ ratio measurement and the use of neural networks for rapid calibration of engine management systems. Potential benefits and problems of neural network analysis of spark-voltage vectors, as a means of λ ratio estimation by virtual sensing, have been discussed.

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Two very important areas in automotive engine design are low exhaust emissions and better fuel economy. As regulations become increasingly stringent, manufacturers of automotive engines are looking for ways to reduce the emission of pollutant gases from the exhaust. This paper describes a technique under development known as Spark Voltage Characterization, which involves capturing the time varying spark voltage waveform and analysing it using a neural network. The neural network associates spark voltage vectors and other engine parameters of interest with specijic air-fuel ratio values. The experimental work done so far hasproved that the sensor is able to accurately determine the air-fuel ratio. There are factors other than the air-fuel ratio that affect the spark voltage characteristics. The work presented in this paper aimed to investigate the effect of varying engine block temperature on the proposed sensor method. Data was captured at different engine block temperatures and air-fuel ratio settings and used to train and test the neural network The neural network analysis was conducted to test its ability to determine the air-fuel ratio using data for both trained and untrained engine block temperature settings.