SYSTEMATIC OPTIMISATION PROCESS FOR AN EBIKE DRIVE UNIT IN A HIGHLY VARIABLE ENVIRONMENT

Drive units of eBikes are used in every type of bicycle and for different riding scenarios and riders. Due to the different riders and bike types, an enormous variety of influencing parameters and load spectra must be considered during the design process. Therefore, in this paper, a systematic approach for the optimization of the drive unit is presented, which adopts and combines several approaches from design theory. The focus is on efficient modeling and simulation of the relevant parameters and load spectra to minimize uncertainties in the design process. Based on a system analysis, dimension-reduced parameter spaces are formed for the simulation of the system, meta-models are integrated into the simulation model and the results of the simulation are transferred into a data-based surrogate model to cover the parameter space in an efficient way with a minimum number of time consuming FE simulations. Furthermore, a coordinate-based evaluation method is presented for the FE model in order to form the input for the surrogate model, reduces the amount of data, and to allows a geometry- and mesh-independent evaluation to compare different models.