R&H Optimization by development of an automation toolchain

The car manufacturers (OEMs) invest time and resources on vehicle dynamics development to meet different customer expectations: Ride Comfort & Handling performance, functional requirements (i.e. road safety) and NVH (noise vibration and harshness) quality. In this scenario, reducing development costs and delivering higher value to the customer represent the two major challenges to fulfill. The high level of most OEMs was reached by combining subjective evaluations on road testing and objective virtual methods, providing data and key performance indicators. Vehicle dynamics simulation allows finding the best solutions in the concept and development phases, investigating a large number of vehicle variants that in the real world are only possible for some selected variants of a given vehicle due to limited availability and timing. Vehicle Ride & Handling (R&H) driving characteristics are one of the key areas of virtual development. New methodologies and tools are continuously implemented to represent relevant properties at the whole vehicle, subsystem, and component level (like suspension hardware, tire, steering and chassis control systems). The number of relevant model parameters increases depending on the complexity of the model and the use-case. Due to increasing vehicle complexity and topology, it is challenging to achieve key performance indexes (KPI) via a traditional trial & error approach. Optimization tools are required to gain a better understanding of the key parameters affecting the performance and efficiently achieve targets using a structured process. Recently, Hyundai Motor Europe Technical Center (HMETC) chassis department has decided to design an automated environment focused on full vehicle optimization that runs all required simulation models and tools, using remote computational resources for heavy simulation runs. For this reason, a toolchain for selected Ride & Handling use-cases has been designed during a Master of Science Thesis project in collaboration between Hyundai Motor Group, Politecnico di Torino and KTH Royal Institute of Technology. The thesis describes how the toolchain has been implemented in Matlab/Simulink and integrated with vehicle dynamics CAE software generally used in HMETC (based on both multi-body and functional models) and with HEEDS MDO, which provides the optimization algorithm. Finally, different R&H use-cases show the system's reached level of robustness and flexibility.