Automotive Hydraulic Valve Fluid Field Estimator Based on Non-Dimensional Artificial Neural Network (NDANN)

A conventional automatic transmission (AT) hydraulic control system includes many spool-type valves that have highly asymmetric flow geometry. An accurate analysis of their flow fields typically requires a time-consuming computational fluid dynamics (CFD) technique. A simplified flow field model that is based on a lumped geometry is computationally efficient. However, it often fails to account for asymmetric flow characteristics, leading to an inaccurate analysis. In this work, a new hydraulic valve fluid field model is developed based on a non-dimensional neural network (NDANN) to provide an accurate and numerically efficient tool in AT control system design applications. A “grow-and-trim” procedure is proposed to identify critical non-dimensional inputs and optimize the network architecture. A hydraulic valve testing bench is designed and built to provide data for neural network model development. NDANN-based fluid force and flow rate estimator are established based on the experimental data. The NDANN models provide more accurate predictions of flow force and flow rates under broad operating conditions compared with conventional lumped flow field models. The NDANN fluid field estimator also exhibits input-output scalability. This capability allows the NDANN model to estimate the fluid force and flow rate even when the design geometry parameters are outside the range of the training data.