XIL simulation enables vast testing, requiring small time and prior to the availability of the hardware. The objective of this work is to describe the implementation of a modular X-In-the-Loop (XIL) for Electronic Control Unit (ECU) testing for Electric Vehicles (EV). The proposed showcase allows to significantly reduce the time and cost of the feature developments, allowing to test not only the code of the ECU, but also its hardware and communications.

Layout design is the process in which industrial robots and other manufacturing components are positioned within a manufacturing system so that the intended operations can be handled appropriately. The traditional layout design process presents several challenges. It involves numerous iterations of testing different manually generated manufacturing layouts, requiring extensive trial and error to achieve an optimal solution. This process is highly time-consuming and demands significant expertise and cognitive effort from the designer. Within this publication, a flexible, scalable, and efficient function-block-based solution is presented for the optimization of manufacturing system layouts, especially in the field of multi-robot cells in two different use cases: one in additive manufacturing and one in jig-less welding. The findings showcase that the methodology followed enabled the efficient allocation of industrial robots in a workspace, minimizing the cognitive effort required in comparison to the traditional manual trial-and-error layout design procedure.

Electric vehicles (EVs) allow the implementation of multi-motor powertrains. These topologies allow to control the torque exerted in each active wheel of the vehicle, and thus, can naturally implement yaw moment control approaches such as Torque Vectoring, which improve vehicle stability and cornering response. Among the different control techniques proposed for this purpose, due to the recent improvement in computation power of embedded devices, optimal control based solutions have become one of the most promising alternatives. Hence, in this paper the performance of three different optimal based torque vectoring algorithms (Linear Quadratic Regulator, Linear Time-Variant Model-based Predictive Control and Nonlinear Model-based Predictive Control) are proposed, studied and comprehensively compared.