Objective Determination of Degradation of Lambda Sensor Using Recursive Least Square Method

The closed loop air fuel ratio control is one of the essential algorithm to control emissions in internal combustion engine-based vehicle fleets. In order to address this, system and component level diagnostics need to explore extensively. In the presented work, a data-driven method is proposed and implemented to monitor the health of switch type oxygen sensor (lambda sensor). As the response of the lambda sensor present in a vehicle varies over the mile-age which will directly affect the tailpipe emission of the vehicle. Lambda sensor monitoring is the key challenge in OBD-II regulations. A simplified linear model for lambda sensor is derived which includes the lambda sensor gain K_L. The recursive least square estimation algorithm is employed to search the value of the lambda sensor gain K_L for which the model can estimate the lambda sensor output signal. The lambda gain has the correlation with state of health of the lambda sensor. That will be used as an index for age monitoring of the sensor. Proposed algorithm is tested and validated for real city drive condition data set and the results show that the gain K_L converges at different values for different sensor responses. This is an ECU implementable method for real time estimation of sensor gain K_L which lead us to objective determination of degradation in lambda sensor. The estimated value of K_L is validated with three differently aged lambda sensors and mapped to objectively diagnose the health of the sensor.