Abstract
Engine brakes are more effective in braking a heavy-duty vehicle during deceleration compared to the traditional clutch-brake system. Therefore, commercial vehicle OEM’s along with regulations, demand the acclimatizing of engine brake (EB) system. To achieve this, it is equally important to adopt to variable valve actuation dynamic valvetrain (VT) system. To help develop these systems, Model Based Product Development approach is used primarily at Eaton.
In current work, the effect of valve lash sensitivity on EB performance and VT dynamics is studied using multi physics GT-SUITE models. This helps to understand the impact of lash on valve lift opening, lift loss and overall VT system compliance. In addition to above VT dynamics, its effect on EB power is also studied. This is done using a medium duty 6-cylinder GT-POWER engine model developed from Fast Response Model (FRM) database. Model is validated using Frictional Mean Effective Pressure (FMEP) motoring engine test (empirical) data. A type 3 VT dynamic model is developed to replicate the mechanism and validated using valve lift proxy data. The validated performance and mechanical models are then integrated so that the engine model imposes the cylinder and port pressures on the valve and the VT dynamic model actuates the engine valves. This integrated model captures the dynamic variation of the valve lift along with the lash sensitivity.
A study for EB performance loss due to the system lash, is important to develop the correct valve lift ramps. Also, lash sensitivity impacts performance of VT dynamics. So, the effect of lash sensitivity on other dynamic phenomenon such as contact forces, valve closing velocity and contact stresses are analyzed in this work. Therefore, the study helped to optimize the valve lift with ramp for lash sensitivity to meet all VT design limits along with maximum brake power within the required peak cylinder pressure constrain.
Keywords- Engine Brake, Engine Motoring, Kinematic, Dynamic, Valvetrain, Cam Lift, Valve Lift, Lash, FRM, Optimization