Uncertainty Quantification of Wet Clutch Actuator Behaviors in P2 Hybrid Engine Start Process 2022-01-0652

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions. In this paper, a predictive hydro-mechanical clutch actuator model is first presented. The equations of motion of the actuator piston include a parametric representation of squeeze film and friction material compression for damping and stiffness effects. The hydraulic line dynamics is modeled with a series of lumped volumes connected through orifices. Clutch torque is computed based on the Coulomb friction assumption. The model behaviors are qualitatively validated with experimental vehicle data. Monte Carlo simulations are conducted to investigate the effects of uncertainties in the input signal, bulk modulus of the fluid, piston seal friction, and piston damping. The mean behavior of piston pressure changes considerably when the presence of uncertainty is accounted for, which significantly affects the piston motion and clutch torque predictions. The results demonstrate that uncertainty quantification offers valuable insights into system behaviors that are not obtainable through conventional deterministic analyses. This knowledge of the uncertainty propagation can in turn help improve system performance through uncertainty-aware control and hardware design.