Upstream Oxygen Sensor Signal Improvement Using the DFSS and Virtual Validation Approach

Combustion stability and emission control remain key challenges for gasoline engines, requiring robust oxygen sensing strategies. The primary function of the upstream exhaust oxygen sensor is to detect the oxygen concentration in exhaust gas for accurate air–fuel ratio control. However, poor signal visibility from individual cylinders across engine speeds can lead to improper combustion prediction and reduced engine efficiency. This work applies a Design for Six Sigma (DFSS) approach to optimize the upstream oxygen sensor configuration in a 2.0 L four-stroke gasoline engine. Conventionally, sensor placement is completed by iterative testing and calibration, which is both time-consuming and cost intensive. The DFSS framework uses input, output, control, and noise factors. Exhaust gas mass flow rate from engine cylinders at different speeds is treated as the input, while the detected oxygen mass fraction is the output. Design parameters such as pipe length, pipe diameter, sensor orientation, insertion depth, and location are considered control factors. Sensor element position and ambient temperature serve as noise factors, as they cannot be controlled directly by the engineer. The analysis is performed using three-dimensional computational fluid dynamics (CFD) and confirmed through Design of Experiments (DoE) simulations. The optimized configuration achieved improved sensor signal stability and cylinder visibility, enabling more reliable combustion control. This structured approach demonstrates how virtual analysis combined with DFSS principles can guide robust oxygen sensor placement strategies, reducing validation effort while enhancing engine efficiency and emissions performance.