Neural Network Based Virtual Sensor for Throttle Valve Position Estimation in a SI Engine

Electronic throttle body (ETB) is commonly employed in an intake manifold of a spark ignition engine to vary the airflow quantity by adjusting the throttle valve in it. The actual position of the throttle valve is measured by means of a dual throttle position sensor (TPS) and the signal is feedback into the control unit for accomplishing the closed loop control in order handle the nonlinearities due to friction, limp-home position, aging, parameter variations. This work aims presents a neural networks based novel virtual sensor for the estimation of throttle valve position in the electronic throttle body. Proposed neural network model estimates the actual throttle position using three inputs such as reference throttle angle, angular error and the motor current. In the present work, the dynamic model of the electronic throttle body is used to calculate the current consumed by the motor for corresponding throttle valve movement. Proposed virtual sensor is tested for the sinusoidal and random driving cycle throttle angle input using a Bosch DVE5 electronic throttle body. Estimated throttle valve angle using the proposed neural network is found to closely follow the measured throttle valve angle using hardware TPS. Experimental results exhibit the accurate tracking capacity of the proposed virtual sensor for the throttle valve estimation in the event of hardware throttle sensor failure and it can be useful as an added redundancy in the electronic throttle control system.