Modeling Diesel Oxidation Catalyst Upstream and Downstream Exhaust Gas Temperatures Using LSTM RNN

Abstract

Sensors are essential in powertrain engineering and exhaust aftertreatment systems due to the increasing need for high performance and fewer emissions. Mostly in diesel oxidation catalysts, the upstream and downstream temperature sensors are attached to the vehicle until their models get calibrated then removed in the end-user version. The modeling process is both extravagant and time-consuming as it requires engine and chassis dynamometers. In fact, these temperature models are used in the monitoring of CO emission level and as inputs to calculate the other exhaust aftertreatment system components' efficiencies. The purpose of this paper is to investigate the use of long short-term memory networks in the automotive sector by generating a model for the diesel oxidation catalyst upstream and downstream temperatures as an example. Measurements from engine sensors and actuators position feedback were recorded from a vehicle and used as training and validation data. After sufficient training, the model was utilized to evaluate and predict the modeled oxidation catalyst upstream and downstream temperatures.