Simulation Model for Drivability Assessment and Optimization of Hybrid Drive Trains

Domijanic, M.; Hirz, M. · 2021 · DOAJ

DOI: 10.21122/2227-1031-2021-20-1-37-44

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Summary

This paper presents a simulation-based approach for assessing and optimizing the drivability of hybrid drive trains, specifically focusing on a P2-hybrid reference vehicle. The research addresses the need for virtual development tools that can evaluate driving comfort and performance in early project phases, reducing reliance on physical testing. The study aims to identify key factors influencing drivability oscillations and determine how specific drivetrain parameters can be adjusted to improve comfort without significantly compromising performance. The methodology involved modeling the P2-hybrid drive train using data acquired from comprehensive measurements on public roads and test tracks. The simulation model was validated against measured vehicle speed and acceleration data for maneuvers such as normal starts, creeping, and rolling stops. Minor discrepancies in braking maneuvers were attributed to uncertainties in brake system modeling, which were deemed negligible for propulsion-focused analysis. Virtual drivability assessments were conducted using the VDI 2057-1 evaluation method. The study analyzed various driving maneuvers, including Tip-In at different initial velocities (30–130 km/h), full-throttle acceleration, and gear shifts, while varying throttle pedal positions, velocity, and battery State of Charge. The results indicated that drivability is significantly influenced by gear changes at high gear ratios and the transition between hybrid and electric driving modes, both of which trigger peaks in acceleration signals at the driver’s seat rail. In Tip-In maneuvers, driving comfort increased with higher initial velocities due to lower gear ratios, although oscillations remained noticeable. Parameter variation analysis revealed that increasing the stiffness and damping coefficient of the clutch’s torsional damper improved drivability, particularly during Tip-In maneuvers. Similarly, longer gearbox shifting times enhanced comfort but reduced acceleration performance. Conversely, increasing drive shaft stiffness increased comfort values but required careful design to ensure torque transmission. Wheel suspension damping also showed an overall improvement in drivability with stronger damping. The study concludes that gear changes and clutch engagement have the greatest impact on drivability, offering the highest potential for optimization in hybrid drive trains. The proposed simulation approach enables effective assessment and optimization of drivability in virtual environments, supporting early-stage development. While optimizing for comfort can affect performance metrics like acceleration time, the model provides a robust tool for balancing these factors. The research highlights the importance of detailed component data, such as engine characteristics and clutch torque limits, for precise drive train representation. This framework supports the integration of comfort considerations into the technical development of future hybrid vehicles.

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