Simulation Model for Drivability Assessment and Optimization of Hybrid Drive Trains

The growing electrification of vehicle drive trains is increasing their complexity significantly. The interactions between the different drive train components should not be noticed negatively by the occupants, which is considered as good drivability and thus contributes to increasing customer acceptance. Today’s development processes of hybrid- and electric driven cars consider energy management in earlier development phases as drivability optimization. In these early development phases, fuel- and energy consumption are optimized on the basis of standardized driving cycles. Drivability aspects and influences of real driving operation are not integrated until the prototype phase. In this way, modifications of drivability-relevant aspects phase are limited, which restricts the potential to find optimal solutions. In this context, the submitted paper presents an approach for assessment and optimization of the drivability of hybrid drive trains in the virtual development process. The created simulation model is exemplarily based on the P2-hybrid drive train of a VW Passat GTE. For the validation of the drive train model and the assessment of drivability, defined maneuvers were carried out on a test track and compared with the results of maneuver simulations. By simulating different driving maneuvers, the resulting acceleration oscillations, which affect the passenger, are calculated and evaluated from the aspect of drivability. The assessment method is derived from a VDI directive dealing with the effects of vibrations on the wellbeing and human health. In order to identify the influencing factors of different maneuvers and parameters of the drive train components, both were varied in the study. It turned out that change of gears and closing of the clutch had the greatest influence on the drivability and thus has the greatest potential for optimizing design and control strategy of hybrid drive trains. In this way, the presented approach enables the assessment and optimization of drivability of hybrid drive trains in the early development phase and thus reduces the gap between virtual development and prototype phase.  

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