Development of an optimised side crash concept for the battery-electric vehicle concept Urban Modular Vehicle

Kurzfassung

A methodological approach to the development of an optimised side crash concept for the battery-electric vehicle concept Urban Modular Vehicle was presented in order to meet the requirements of a fully functional concept. In addition to the load path analyses shown, and the subsequent energy absorption process, validation tests at a coupon and component level, as well as the optimisation models validated alongside these, have been demonstrated. To optimise the sandwich crash concept, the ‘morphing’ method was used to modify the geometry. This allows the main influential factors for energy absorption to be identified due to the buckling of the sandwich core, as well as allowing the determination of an optimal combination of the parameters being tested. One sole optimisation of the door sill structure was shown to be impractical, which led to the development of an analogical modelling, which yielded acceptable levels of accuracy (2.5% deviation for intrusion when compared to the full-vehicle simulation) and the best possible reduction in time (98% reduction when compared to the full-vehicle simulation ) to be achieved when optimising the internal structures of the door sill. The analogical model shows, above all else and with sufficient accuracy, the important area of deformation at the point of impact of the pole. The model also shows weaknesses in the free ends of the edge areas, which is of little importance here. Given that the variation in the door sill structure is equal to that of a discrete optimisation problem, a multi-level optimisation was used. The advantage of this is that each variant is optimised and then later compared with the discrete variants. One final check is made in the full-vehicle crash model in order to verify both the optimisation results and the analogical model. Furthermore, optimisation steps 7 and 8 improve the specific energy absorption by ~24%, and the intrusion by ~30%.