REAL TIME SUPERVISED MACHINE LEARNING TORQUE CONVERTER MODEL

A vehicle, control system for operating a torque converter of a vehicle and a method of operating a torque converter. The control system includes a machine learning model and a model-based controller. The machine learning model is configured to receive a first set of measurements of operational parameters of the torque converter, and determine fit parameters for a model of the torque converter using the first set of measurements. The model-based controller is configured to receive a second set of measurements of operational parameters of the torque converter, determine a clutch pressure for the torque converter from the second set of measurements and the fit parameters, and apply the determined clutch pressure to the torque converter.

INTRODUCTION

The subject disclosure relates to operating a torque converter of a vehicle and, in particular, to forming a model of operation of the torque converter and controlling a pressure applied to a clutch of the torque converter based on operational parameters of the torque converter and the model.

A torque converter is used to transfer torque from an engine of a vehicle to a transmission of the vehicle through hydraulic transmission methods. Current torque converter models are not able to capture variations in both operational parameters of the torque converter and pressure at a clutch of the torque converter. These models also require time in order to be calibrated. Accordingly, it is desirable to provide a torque converter model that can learn from operational parameters and clutch pressures in real-time in order to determine and apply suitable pressures at the clutch of the torque converter.

SUMMARY

In one exemplary embodiment, a method of operating a torque converter is disclosed. A first set of measurements of operational parameters of the torque converter is obtained. Fit parameters are determined for a model of the torque converter using the first set of measurements. A second set of measurements of operational parameters of the torque converter is obtained. A clutch pressure is determined for the torque converter from the second set of measurements and the fit parameters. The determined clutch pressure is applied to the torque converter.

In addition to one or more of the features described herein, determining the fit parameters further includes applying a recursive least squares fitting to the first set of measurements. The method further includes receiving the first set of measurements at a machine learning system that determines the fit parameters, and receiving the second set of measurements at a model-based controller that determines and applies the clutch pressure. The method further includes modeling the clutch pressure as a linear combination of the operational parameters. The method further includes determining a controlling sub-region of operation of the torque converter and selecting at least the first set of measurements from the controlling sub-region. The operational parameters include an operational parameter of the engine and an operational parameter of the turbine. The operational parameters can include at least one of a turbine speed, an engine speed, a clutch torque gain, a clutch friction compensation term, and a clutch pressure offset.

In another exemplary embodiment, a control system for operating a torque converter of a vehicle is disclosed. The control system includes a machine learning model and a model-based controller. The machine learning model is configured to receive a first set of measurements of operational parameters of the torque converter, and determine fit parameters for a model of the torque converter using the first set of measurements. The model-based controller is configured to receive a second set of measurements of operational parameters of the torque converter, determine a clutch pressure for the torque converter from the second set of measurements and the fit parameters, and apply the determined clutch pressure to the torque converter.

In addition to one or more of the features described herein, the machine learning model is configured to apply a recursive least squares fitting to the first set of measurements to determine the fit parameters. The machine learning model is further configured to model the clutch pressure as a linear combination of the operational parameters. The control system further includes a supervisor configured to determine a controlling sub-region of operation of the torque converter and select at least the first set of measurements from the controlling sub-region. The operational parameters include an operational parameter of the engine and an operational parameter of the turbine. The operational parameters can include at least one of a turbine speed, an engine speed, a clutch torque gain, a clutch friction compensation term, and a clutch pressure offset.

In yet another exemplary embodiment, a vehicle is disclosed. The vehicle includes a torque converter and a control system. The control system is configured to obtain a first set of measurements of operational parameters of the torque converter, determine fit parameters for a model of the torque converter using the first set of measurements, obtain a second set of measurements of operational parameters of the torque converter, determine a clutch pressure for the torque converter from the second set of measurements and the fit parameters, and apply the determined clutch pressure to the torque converter.

In addition to one or more of the features described herein, the control system includes a machine learning model configured to receive the first set of measurements and determine the fit parameters, and a model-based controller configured to receive the second set of measurements, determine the clutch pressure and apply the determined clutch pressure to the torque converter. The machine learning model is configured to apply a recursive least squares fitting to the first set of measurements to determine the fit parameters. The machine learning model is further configured to model the clutch pressure as a linear combination of the operational parameters. The control system further includes a supervisor configured to determine a controlling sub-region of operation of the torque converter and select at least the first set of measurements from the controlling sub-region. The operational parameters include an operational parameter of the engine and an operational parameter of the turbine. The operational parameters can include at least one of a turbine speed, an engine speed, a clutch torque gain, a clutch friction compensation term, and a clutch pressure offset.

DETAILED DESCRIPTION

In accordance with an exemplary embodiment,FIG. 1schematically depicts a vehicle100operable using a torque converter control system. The vehicle includes an engine102, a torque converter104and a transmission106. The torque converter104converts torque provided by the engine at an engine speed to a torque usable at the transmission106in order to operate wheels110of the vehicle. Such torque conversion controls the transfer of rotary motion from the engine102to the transmission106. A control system108for the torque converter104includes a processor112that obtains measurements from various sensors at the torque converter and controls the operation of the torque converter based on these measurements, as discussed below.

FIG. 2shows a cross-sectional side view of an illustrative torque converter104ofFIG. 1. The torque converter104includes various components that are coupled to the engine102,FIG. 1and various components coupled to the transmission106,FIG. 1. Components coupled to the engine102include a drive shaft202, pump204and cover206. Components coupled to the transmission106,FIG. 1include turbine208, damper assembly210, clutch assembly212and turbine shaft214. A stator216is a grounded component of the torque converter104that is not coupled to either the engine102or the transmission106. The stator216can be useful to facilitate fluid flow between the turbine208and pump204in order to control torque transfer between pump204and turbine208.

The pump204and turbine208are separate components that rotate within a fluid-filled cavity formed by cover206. The pump204rotates to cause a circulation of the fluid in the cavity. The circulating fluid causes the turbine208to rotate, thereby transferring rotary motion from the pump204to the turbine208. The drive shaft202is coupled to the pump204and transfers a rotation of the engine to a rotation of the pump204. Similarly, the turbine shaft214is coupled to turbine208and transfers the rotation of the turbine208to a rotation of the transmission106,FIG. 1. Thus, in order to transfer power from the engine to the transmission, the engine rotates the drive shaft202to rotate pump204in order to cause circulation of the fluid in the cavity, with the circulation of the fluid causing the rotation of turbine208and turbine shaft214. Clutch assembly212and damper assembly210control a relative axial proximity of the pump204to the turbine shaft214, thereby controlling the torque coupling between pump204and turbine shaft214. This proximity can be controlled by applying a torque converter clutch pressure or “clutch pressure,” PTCC.

FIG. 3shows a schematic diagram of a torque converter system300that includes the torque converter104and control system108. The control system includes a machine learning system302for forming a model of operation of the torque converter104based on various measurements from the torque converter104and a model-based controller304that operates the torque converter104based on the model. The control system108operates the machine learning system302and the model-based controller304. Various sensors (not shown) of the torque converter104provide measurements to the machine learning system302. Exemplary measurements include a turbine rotational speed ωTurb, an engine rotational speed ωEng, clutch torque gain τEng, a clutch friction compensation term ωTCCand the clutch pressure PTCC. The machine learning system302forms a model of the torque converter104from these measurements and provides the model to the model-based controller304. The model-based controller304uses the model in order to determine a torque converter clutch pressure PTCCthat achieves a selected torque conversion and applies the determined clutch pressure PTCCto the clutch of the torque converter104.

In various embodiments, the machine learning system302forms a model of the torque converter104that relates clutch pressure PTCCto the operational parameters of the torque converter, as shown below in Eq. (1):

where

represents measured operational parameters of the torque converter104. The first three values of the β vector of Eq. (2) (i.e., ωTurb2, ωTurbωEng, and ωEng2) are hydraulic parameters of the torque converter104. Engine torque τEngsignifies a generalized clutch gain and ωTCCindicates a friction curve compensation at the clutch212. The vector x of Eq. (1) include fit parameters or fit coefficients associated with the operational parameters of the torque converter104, as shown in Eq. (3):

The machine learning system302determines these fit parameters of the x vector and provides the determined fit parameters from the machine learning system302to the model-based controller304. The model-based controller304then determines a clutch pressure PTCCby measuring the operational parameters of rotational speed ωTurb, engine rotational speed ωEng, clutch torque gain τEng, and clutch friction compensation term ωTCC, and suppling these operational parameters to the model as established by the determined fit parameters. The model-based controller304then applies this clutch press PTCCto the clutch212of the torque converter104.

The discussion below with respect to Eqs. (4)-(15) describes determining the fit parameters of the torque converter model using recursive least squares operation. At the machine learning system302, N measurements are made of the operational parameters of the torque converter104. Given N measurements, Eq. (1) can be written as an N-dimensional model:

The model of Eq. (4) can be solved in order to determine the fit parameters x by treating the model as a linear system: The linear system of Eq. (4) is in the form:

where A represents the matrix βNand b represent the vector PTCC(N). The solution of this matrix equation can be determined by minimizing the equation:

which can be minimized by evaluating:

In terms of determining the fit parameters for the torque converter104, N measurements of the operational parameters for the torque converter104can be used to determine initialized values of the operational parameters:

and of the corresponding clutch pressures:

where A0and b0are initial variables of the linear equation of Eq. (5):

are the kth values of the operational parameters of Eq. (5). Once the N measurements have been obtained, it is possible to form the initial matrix A0and initial vector b0. An initial value x0for the fit parameters can be determined from the calculations of Eqs. (12) and (13):

An iteration process is used to update the fit parameters with each iteration or new set of operational parameter data. At the kthiteration, the fit parameters can be updated based on initial values (of Eqs. (8) and (9)) and the kthmeasurements (of Eqs. (10) and (11)), as shown below in Eqs. (14) and (15):

As the number of measurements increases, and thus the number of iterations, the fit parameters converge to given values.

FIG. 4shows convergence plots400for the various fit parameters of the torque converter model, illustrating the convergence of the fit parameters over several iterations. Convergence for the fit parameters (a1, a2, a3, a4, a5, a6) for the five operational parameters (ωTurb2, ωTurbωEng, ωEng2, τEngωTCC) and clutch offset pressure are displayed. Suitable convergence can be determined in less than 1000 iteration for all of the fit parameters, with some of the fit parameters converging fewer iterations.

FIG. 5shows a plot500comparing predicted pressure values vs. actual pressure values. These values are plotted against engine torque and turbine speed. The predicted pressure values show good agreement with actual pressure values.

FIG. 6shows a diagram600illustrating various operating regions of the torque converter104. The diagram600shows a two-dimensional map of an operating region defined along the x-axis by turbine rotational speed ωTurband along the y-axis by engine torque τEng. Four sub-regions are shown, labelled I, II, III and IV. Data points are shown accumulated within each of the four sub-regions. Sub-region I shows an accumulation of four counts, sub-region II shows an accumulation of three counts, sub-region III shows an accumulation of three counts and sub-region IV shows an accumulation of 1 count.

In various embodiments the torque converter104tends to operate within one of these operating sub-regions more than in the others. The sub-region having most accumulations can be a controlling sub-region of operation. Thus, the machine learning system302is facilitated by determining the controlling sub-region of operation of the torque converter104and determining PTCC for the controlling sub-region of operation based on the measurements corresponding to the controlling sub-region.

In various embodiments, a supervisor310of the machine learning system302maintains a count of the accumulations to determine a controlling sub-region of operation of the torque converter104. The count can be a running count of the N most recent data points, for example. The supervisor310provides the data points from the controlling sub-region of operation in order to determine the PTCC. The supervisor310can prevent the machine learning system302from receiving singularity values. The supervisor310can also obtain a suitable distribution of data points for use at the machine learning system302.

FIG. 7shows a flowchart illustrating a method700for operating a torque converter using the model disclosed herein. In box702, a first set of measurements of operational parameters of the torque converter are obtained at the machine learning system302. In box704, the machine learning system302creates a model for operation of the torque converter, determining a set of fit parameters for the operational parameters. In box706, a second set of measurements of operational parameters are obtained at model-based controller304. In box708, the model based controller304determines a clutch pressure PTCC based on the second set of measurements and the model or the fit parameters of the model. In box710, the model-based controller304applies the determined clutch pressure to the torque converter.