Monitoring of NOx sensor drift in automotive fleets in a cloud/edge framework

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. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)