Hardware Supported Data-Driven Modeling for ECU Function Development 2020-01-1366
The powertrain module is being introduced to embedded System on Chips (SoCs) designed to increase available computational power. These high-performance SoCs have the potential to enhance the computational power along with providing on-board resources to support unexpected feature growth and on-demand customer requirements. This project will investigate the radial basis function (RBF) using the Gaussian process (GP) regression algorithm, the ETAS ASCMO tool, and the hardware accelerator Advanced Modeling Unit (AMU) being introduced by Infineon AURIX 2nd Generation. ETAS ASCMO is one of the solutions for data-driven modeling and model-based calibration. It enables users to accurately model, analyze, and optimize the behavior of complex systems with few measurements and advanced algorithms. Both steady state and transient system behaviors can be captured. The AMU computation engine is a floating-point unit designed to speed up the performance of critical applications by off-loading the computation from the central processing unit (CPU). This paper will share insight of addressable applications in automotive with focus on powertrain by utilizing ETAS ASCMO to provide the RBF model for the microcontroller integrated AMU hardware feature. We will illustrate the tool and hardware performance, and additionally, we will lay down the path to utilize AMU within the software environment.