Technical Paper
Use of convolutional neural network for sensor modeling and on-board diagnostic
2022-02-04
2021-36-0102
The use of physical based mathematical models has been the standard when it comes to model development in the powertrain domain. More specifically, substitute models and diagnostic models (also known as virtual sensors) used to identify sensor fault are applied widely in the engine control in order to comply with on-board diagnostic (OBD) requirements. In the early years of calibration, the amount of data was limited: nowadays with extensive and detailed validations demanded by the sector, the vehicles are submitted to a bigger span of tests before reaching the final costumer. It is in this context that a neural network (NN) thermodynamic model of the engine coolant temperature (ECT) of an internal combustion engine (ICE) was created: the main drawback of any machine learning method is the amount of data necessary, but with data not being a problem, the NN can be quickly implemented and generalized to other projects, reducing time of development and costs for them.
