deep learning provides tools and methods to address common problems of the automotive industry in new and revolutionary ways. in addition, simulation and virtual models allow flexibility and speed-up in the development and testing process of ecu functions.
in this presentation, an ecu development process is described, which uses data-driven engine temperature models trained with nonlinear autoregressive neural networks with external input (narx) and matlab® in the cloud to overcome the obstacles of traditional physical modeling approaches.
along with xcu controller code compiled by simulink real-time™ as executable for virtual calibration on a desktop computer, calibration and test engineers can use the previously generated data-driven models and typical engineering products like etas® inca to tune and validate that xcu controller code on a completely virtual environment, saving costs, increasing agility, and accelerating the development and validation process of ecu functions.
