Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle

This paper presents an alternative tool for vehicle tuning applications by incorporating the use of artificial neural network (ANN) virtual sensors for a hydrogen-powered car. The objective of this study is to optimize simple engine process parameters to regulate the exhaust emissions. The engine process parameters (throttle position, lambda, ignition advance and injection angle) and the exhaust emission variables (CO, CO2, HC and NOx) form the basis of the virtual sensors. Experimental data were first obtained through a comprehensive experimental and tuning procedure for neural network training and validation. The optimization layer-by-layer neural network was used to construct two ANN virtual sensors; the engine and emissions models. The performance and accuracy of the proposed virtual sensors were found to be acceptable with the maximum predictive mean relative errors of 0.65%. With its accurate predictive capability, the virtual sensors were then employed and simulated as a measurement tool for vehicle tuning and optimization. Simulation results showed that the exhaust emissions can be regulated by optimizing simple engine process parameters. This study presents an alternative tool for vehicle tuning applications for a hydrogen-powered vehicle. In addition, this work also provided a tool to better understand the effects of various engine conditions on the exhaust emissions without the need for any vehicle modifications.