Abstract
Nitrogen oxides (NOx) are one of the main pollutants from both SI and CI engines. In recent years, regulation focus has moved from type-approval certification over known driving cycles to real-life verification of the emission level. In this paper, a system for NOx in-service emission monitoring based on ASSIST-IoT cloud/edge architecture is presented, and the effect of sensor bias on the emission level estimate discussed. The use of an additional high-fidelity sensor in a sample of the fleet is proposed as a method for estimating series sensor drift through a distributed learning approach, able to provide local and global models for the sensor.
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