Research on online correction algorithm with neural network multi-environment factors for CO detection of motor vehicle exhaust

Abstract

The influence of temperature, humidity and pressure on the measurement of exhaust gas CO concentration after pretreatment is analyzed. An on-line correction algorithm with multi-environment factors of neural network for the vehicle exhaust CO detection has been proposed. First, the exhaust gas sample data has been trained offline to build the BP neural network model, and then the real-time measured temperature, humidity, pressure and decimal absorption value of the samples have been put into the model for its online correction. Then the corrected CO concentration has been achieved, so the measurement error of the NDIR sensor caused by environmental changes has been solved. Through the prototype experiment, the simulation experiment and the comparison with SEMTECH-EcoStar, the maximum relative deviation of the CO with the concentration from 0 to 0.2% is 4.8% when the temperature range is from 30 to 50 degrees C, relative humidity is from 25 to 40%, the pressure is from 95 to 115 kPa. The experiments have been carried out in the vehicle field to get the correction factor between 0.8 and 1, which verifies the necessity and reliability of the method and provided effective technical support for the detection of the CO concentration of the high-temperature exhaust gas from motor vehicles.