Virtual XCU Calibration with Neural Networks

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.