Gregory Michael Pietron

Hydraulic clutch modeling for automotive control

A low-order dynamic model of a clutch for hydraulic control in an automatic transmission is developed by separating dynamics of the shift into four regions based on clutch piston position. The first three regions of the shift are captured by a physics-based model and the fourth region is represented by a system identification model. These models are determined using nominal values and validated against nominal and off-nominal experimental data. The model provides two lumped flow parameters to be used for tuning to the desired hydraulic clutch system.

Experimental Characterization and Gray-Box Modeling of Spool-Type Automotive Variable-Force-Solenoid Valves With Circular Flow Ports and Notches

In automatic transmission design, electronic control techniques have been adopted through proportional variable-force-solenoid valves, which typically consist of spool-type valves (Christenson, W. A., 2000, SAE Technical Paper Series, 2000-01-0116). This paper presents an experimental investigation and neural network modeling of the fluid force and flow rate for a spool-type hydraulic valve with symmetrically distributed circular ports. Through extensive data analysis, general trends of fluid force and flow rate are derived as functions of pressure drop and valve opening. To further reveal the insights of the spool valve fluid field, equivalent jet angle and discharge coefficient are calculated from the measurements, based on the lumped parameter models. By incorporating physical knowledge with nondimensional artificial neural networks (NDANN), gray-box NDANN-based hydraulic valve system models are also developed through the use of equivalent jet angle and discharge coefficient. The gray-box NDANN models calculate fluid force and flow rate as well as the intermediate variables with useful design implications. The network training and testing demonstrate that the gray-box NDANN fluid field estimators can accurately capture the relationship between the key geometry parameters and discharge coefficient/jet angle. The gray-box NDANN maintains the nondimensional network configuration, and thus possesses good scalability with respect to the geometry parameters and key operating conditions. All of these features make the gray-box NDANN fluid field estimator a valuable tool for hydraulic system design.

Torque Phase Shift Control Based on Clutch Torque Estimation

An automatic transmission shift method is presented, in which the torque transfer phase is controlled in closed loop. This is made possible by real-time estimation of the torque transmitted by the off-going and on-coming clutches participating in the shift. Each clutch torque is determined based on measured or estimated input and output shaft torques and accelerations. To illustrate an application of the method, traditional friction elements are used to emulate one-way-clutch function during a power-on upshift.Copyright

Iterative model and trajectory refinement for launch optimization of automotive powertrains

Abstract A recently proposed iterative model and trajectory refinement (IMTR) approach is applied to launch control of an automotive powertrain with a turbocharged gasoline engine and a dual-clutch transmission. The optimal clutch torque trajectory is determined by a piecewise linear in time function that minimizes a cost function while meeting multiple constraints on vehicle acceleration and engine speed. A high-fidelity model of the powertrain and a simplified physics-based model are used iteratively to refine solutions. The iterative method converges on a solution that meets target performance metrics with efficient execution time.

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.Copyright

Adaptive Shift Control for Automatic Transmissions

Using feedback information of estimates from a model of the hydraulic clutch actuation and measurements from transmission mechanicals, a closed-loop adaptive controller is designed. The controller is structured to update at three different rates: every time instance, every shift, and every n-th number of shifts. Part of the controller is designed to operate in open-loop for the first two regions of the shift until feedback information is available. The open-loop controller adapts within the shift, thus allowing for corrections to the control design to be made during the current shift and in subsequent shifts. The model tuning parameters as well as the return spring pre-load force become the adaptive parameters, which are being adjusted so that the plant matches the model in real-time operation. The control design is validated against a high fidelity simulation model of the transmission hydraulics and mechanicals, as well as experimental data.Copyright

On Testing and Modeling Automotive Spool-Type Hydraulic Valve With Circular Flow Ports

This paper describes empirical investigations of the fluid field for a spool-type hydraulic valve with symmetrically distributed circular ports that is often found in an automotive VFS (Variable Force Solenoid) valve system. Through extensive data analysis, a general trend of fluid force and flow rate is derived as a function of pressure drop and valve opening. Aiming at further revealing the insights of the steady state spool valve fluid field, the equivalent jet angle and discharge coefficient are calculated from the measurements based on the lumped parameter models. New Non-Dimensional Artificial Neural Network (NDANN)-based hydraulic valve system models are also developed in this paper through the use of equivalent jet angle and discharge coefficient. By introducing the outputs of the new NDANN models into the lumped parameter model, fluid force and flow rate can be easily calculated. Therefore, the new approach calculates fluid force and flow rate as well as the intermediate variables (equivalent jet angle and discharge coefficient) with useful design implications. The network training and testing demonstrate that the NDANN fluid field estimators can accurately capture the relationship between the key geometry parameters and discharge coefficient/jet angle. The new approach also maintains the non-dimensional network configuration and possesses scalability with respect to the geometry parameters and key operating conditions. All these features make the new NDANN fluid field estimator a valuable tool for automotive hydraulic system design.Copyright