Virtual air-fuel ratio sensors for engine control and diagnostics

Virtual air-fuel ratio sensors for an internal combustion engine using recurrent neural and wavelet networks have been developed. A nonlinear state-space modeling strategy is proposed for the architecture of the stated recurrent neural network which is trained using some variants of real time recurrent learning (RTRL) algorithm. A two-stage training approach is proposed for improving the accuracy of the RNN topology. Additionally, wavelets as activation functions have been employed to construct a single-layer network called wavenet. The wavenet is used to model the exhaust air-fuel ratio that has proved a more challenging task in a purely neural net-based architecture using sigmoid activation functions. The methodology has been implemented in a V8 spark ignition engine through rapid prototyping tools for the real time generalization and performance evaluation. Observations and comments are made on the test patterns used for the training. Some of the limitations of such a data driven approach are highlighted. Representative experimental results for the 8-cylinder engine test data are listed. The virtual sensor may be used for more precise average air-fuel ratio control and enhanced reliability engendered through the diagnostic capabilities of the sensor.