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Originally developed for the service robot industry, the Robot Operating System (ROS) has lately received a lot of attention from the automotive sector with use cases, especially, in the area of advanced driver assistance systems and autonomous driving (ADAS/AD). Introduced as communication framework on top a of a host operating system, the value proposition of ROS is to simplify the software development in large-scale heterogeneous computing systems. Developers can focus on the application layer and let ROS handle the discovery of all participants in the system and establish communication in-between them. Despite the recent success of ROS, standardized automotive communication protocols such as the Universal Measurement and Calibration Protocol (XCP) are still dominant in the electronic control unit (ECU) development of traditional vehicle subsystems like engine, transmission, braking system, etc.

Today's powertrain control systems are not only quite complex, but often powertrain calibration engineers must effectively deal with many conflicting design goals. This calibration process typically involves resolving many differing calibration trade-offs as well as multiple time-consuming iterations comparing differing calibration scenarios and their respective effects on the overall system performance. By providing powertrain calibration engineers with a tool specifically designed to streamline this time consuming task, overall powertrain calibration efficiency and productivity can be greatly improved. The end result for the powertrain manufacturer is faster time to market and better product quality since additional time is made available for more thorough prove-out and testing.

The use of automated test tools for automotive ECU development has been increasing in recent years, in particular for software testing, system validation and verification, and regression testing. One of the challenges for the ECU development community is that test cases created at a specific phase of software development cannot be easily re-used upstream or downstream in the development process. For example, test cases developed for a model-in-the-loop (MiL) test environment can not be easily re-used on a hardware-in-the-loop (HiL) tests system. This results in significant costs associated with re-engineering test cases and/or poor software quality. At ETAS, we understand that a critical aspect of test case design is the ability to share and re-use test scripts on different HiL hardware platforms and across different development environments (e.g. MiL, SiL, and HiL).

GM's R oad-to- L ab-to- M ath (RLM) initiative is a fundamental engineering strategy leading to higher quality design, reduced structural cost, and improved product development time. GM started the RLM initiative several years ago and the RLM initiative has already provided successful results. The purpose of this paper is to detail the specific RLM efforts at GM related to powertrain controls development and calibration. This paper will focus on the current state of the art but will also examine the history and the future of these related activities. This paper will present a controls development environment and methodology for providing powertrain controls developers with virtual (in the absence of ECU and vehicle hardware) calibration capabilities within their current desktop controls development environment.

Data-driven plant models are well established in engine base calibration to cope with the ever increasing complexity of today’s electronic control units (ECUs). The engine, drive train, or entire vehicle is replaced with a behavioral model learned from a provided training data set. The model is used for offline simulations and virtual calibration of ECU control parameters, but its application is often limited beyond these use cases. Depending on the underlying regression algorithm, limiting factors include computationally expensive calculations and a high memory demand. However, development and testing of new control strategies would benefit from the ability to execute such high fidelity plant models directly in real-time environments. For instance, map-based ECU functions could be replaced or enhanced by more accurate behavioral models, with the implementation of virtual sensors or online monitoring functions.

Real-time embedded automotive software has traditionally been tightly coupled with the hardware on which it runs. In the automotive ecosystem, OEMs and their suppliers are involved in various aspects of developing embedded software. In some instances, suppliers provide the embedded software together with the hardware. In other instances, suppliers provide hardware and infrastructure software, while the OEM takes charge of developing the Application Layer. OEMs typically have the responsibility of integrating the software. Either model can derive value from a non ECU-centric approach to designing the application software, as such an approach allows OEMs to add new software components to existing software packages whenever necessary. The AUTOSAR initiative was created on the value proposition portable application software provides.

The use of design of experiment (DoE) and data-driven simulation has become state-of-the-art in engine development and base calibration to cope with the drastically increased complexity of today's engine ECUs (electronic control units). Based on the representation of the engine behavior with a virtual plant model, offline optimizers can be used to find the optimal calibration settings for the engine controller, e.g. with respect to fuel consumption and exhaust gas emissions. This increases the efficiency of the calibration process and reduces the need for expensive test stand runs. The present paper describes the application of Gaussian process regression, a statistical modeling approach with practical benefits in terms of achievable model accuracy and usability. The implementation of the algorithm in a commercial tool framework enables a broad use in series engine calibration.

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.