Automatic Transmission Method

A method can be used for modeling an automatic transmission using an artificial neural network. The method includes generating the artificial neural network (ANN) by combining a plurality of fully connection neural networks (FCNNs) with a multi-layer recurrent neural network (RNN) and training the artificial neural network using input data and output data of the automatic transmission. The input data might include a preset gear stage, a target gear stage, a current signal of a clutch hydraulic actuator, and an engine torque and the output data might include an engine revolution per minute (RPM), a turbine RPM, a transmission output RPM, and a vehicle acceleration. The trained artificial neural network can be determined as a model of the automatic transmission.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Korean Patent Application No. 10-2019-0146139, filed in the Korean Intellectual Property Office on Nov. 14, 2019, which application is hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to an automatic transmission method.

BACKGROUND

In general, deep learning (deep neural network) is one type of machine learning and includes an artificial neural network (ANN) having multiple layers between an input and an output. The ANN may include a convolution neural network (CNN) or a recurrent neural network (RNN) depending on an architecture, a problem to be solved, and an object.

Data input into the CNN is classified into a training set and a test set. The CNN learns a weight of the neural network based on the training set and verifies the learning result based on the test set.

In such a CNN, when data is input, operations are gradually performed from an input layer to a hidden layer and the results of the operations are output. In this procedure, the input data passes through all nodes only once. The passing of the data through the all nodes only once refers to that the CNN has an architecture which is not based on a data sequence, that is, in a time aspect. Accordingly, the CNN performs learning regardless of the time sequence of input data.

Meanwhile, the CNN has an architecture in which a result of a hidden layer at a previous node is used as an input of a hidden layer at a next node. This refers to that such an architecture is based on a time sequence of the input data.

Such a CNN, which is a deep learning model for learning data changing in a time flow such as time-series data, is an artificial neural network configured through network connection at a reference time point (t) and at a next time point (t+1).

The CNN, in which the connection between units constituting the artificial neural network forms a directed cycle, representatively includes a fully recurrent network (FRN), an echo state network (SEN), a long short term memory network (LSTM), and a continuous-time RNN (CTRNN).

The CNN may include a plurality of cyclic neural network blocks depending on the number of time-series data. CNNs are may be stacked at multiple layers. In this case, a full connection neural network (FCNN) may be used to connect between the CNNs.

According to a conventional method for modeling an automatic transmission, after generating a motion equation for the automatic transmission, considerable know-how is required and time is significantly taken in the process of modifying the motion equation to match multiple test data to the motion equation.

The matter described in the Background art may be made for the convenience of explanation, and may include matters other than a prior art well known to those skilled in the art.

SUMMARY

The present disclosure relates to a technology of generating a model representing the relationship between an input signal and an output signal of an automatic transmission using an artificial neural network.

An aspect of the present disclosure provides a method for modeling an automatic transmission using an artificial neural network capable of inputting a result, which is estimated using an initial value and an output of a recurrent neural network (RNN) block, and the output of the RNN block into an RNN block at a next layer, in the artificial neural network generated by combining a plurality of fully connection neural networks (FCNN) and a multi-layer RNN, thereby estimating a final output value having a higher accuracy even if the number of RNN blocks at each layer and the number of the layers are increased.

According to an aspect of the present disclosure, a method for modeling an automatic transmission using an artificial neural network may include generating an artificial neural network (ANN) by combining a plurality of fully connection neural networks (FCNNs) with a multi-layer recurrent neural network, training the artificial neural network using input data and output data of the automatic transmission, and determining the trained artificial neural network as a model of the automatic transmission.

According to an embodiment of the present disclosure, the artificial neural network may have an architecture to input a result, which is estimated using an initial value and an output of a recurrent neural network (RNN) block, and the output of the RNN block into an RNN block at a next layer.

According to an embodiment of the present disclosure, the artificial neural network may have an architecture, in which an RNN including a plurality of RNN blocks has multiple layers, and the RNN blocks at the multiple layers are connected with each other through the FCNN.

According to an embodiment of the present disclosure, the training of the artificial neural network may include inputting an initial value and an output of a first RNN block at each layer into a first FCNN at the layer, and inputting a result estimated by the first FCNN at the layer into a second FCNN at the layer.

According to an embodiment of the present disclosure, the inputting of the result into the second FCNN may include inputting a result estimated by a first FCNN into a second FCNN, at a first layer, inputting a result estimated by a first FCNN into a second FCNN, at a second layer, and inputting a result estimated by a first FCNN into a second FCNN, at a third layer.

According to an embodiment of the present disclosure, the inputting of the result into the second FCNN may further include inputting the result estimated by the first FCNN at the first layer into a first RNN block at the second layer, inputting the result estimated by the first FCNN at the second layer into a first RNN block at the third layer, and inputting the result estimated by the first FCNN at the third layer as an output value for an input value.

According to an embodiment of the present disclosure, the input data may include at least one of a preset gear stage, a target gear stage, a current signal of a clutch hydraulic actuator, and an engine torque.

According to an embodiment of the present disclosure, the output data may include at least one of an engine revolution per minute (RPM), a turbine RPM, a transmission output RPM, or a vehicle acceleration.

According to another aspect of the present disclosure, a method for modeling an automatic transmission using an artificial neural network may include generating an architecture to input a result, which is estimated using an initial value and an output of an RNN block, and the output of the RNN block into an RNN block at a next layer, and modeling the automatic transmission using the generated artificial neural network.

According to an embodiment of the present disclosure, the artificial neural network may have an architecture, in which an RNN including a plurality of RNN blocks has multiple layers, and the RNN blocks at the multiple layers are connected with each other through the FCNN.

According to an embodiment of the present disclosure, the training of the artificial neural network may include inputting an initial value and an output of a first RNN block at each layer into a first FCNN at the layer, and inputting a result estimated by the first FCNN at the layer into a second FCNN at the layer.

According to an embodiment of the present disclosure, the inputting of the result into the second FCNN may further include inputting a result estimated by a first FCNN into a second FCNN, at a first layer, inputting a result estimated by a first FCNN into a second FCNN, at a second layer, and inputting a result estimated by a first FCNN into a second FCNN, at a third layer.

According to an embodiment of the present disclosure, the inputting of the result into the second FCNN may further include inputting the result estimated by the first FCNN at the first layer into a first RNN block at the second layer, inputting the result estimated by the first FCNN at the second layer into a first RNN block at the third layer, and inputting the result estimated by the first FCNN at the third layer as an output value for an input value.

According to an embodiment of the present disclosure, the input data may include at least one of a preset gear stage, a target gear stage, a current signal of a clutch hydraulic actuator, and an engine torque.

According to an embodiment of the present disclosure, the output data may include at least one of an engine RPM, a turbine RPM, a transmission output RPM, or a vehicle acceleration.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

In the present invention, an automatic transmission means all transmissions except a manual transmission. For example, the automatic transmission may include DCT (Dual Clutch Transmission), CVT (Continuously Variable Transmission), fusion transmission, hybrid transmission, and the like.

FIG. 1is a block diagram illustrating a computing system to execute a method for modeling an automatic transmission using an artificial neural network, according to an embodiment of the present disclosure.

Referring toFIG. 1, according to an embodiment of the present disclosure, the method for modeling the automatic transmission using the artificial neural network may be implemented through a computing system. A computing system1000may include at least one processor1100, a memory1300, a user interface input device1400, a user interface output device1500, a storage1600, and a network interface1700, which are connected with each other via a system bus1200

Thus, the operations of the methods or algorithms described in connection with the embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor1100. The software module may reside on a storage medium (i.e., the memory1300and/or the storage1600), such as a RAM memory, a flash memory, a ROM, memory an erasable and programmable ROM (EPROM), an electrically EPROM (EEPROM), a register, a hard disc, a solid state drive (SSD), a removable disc, or a compact disc-ROM (CD-ROM). The exemplary storage medium may be coupled to the processor1100. The processor1100may read out information from the storage medium and may write information in the storage medium. Alternatively, the storage medium may be integrated with the processor1100. The processor and storage medium may reside in an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. Alternatively, the processor and storage medium may reside as separate components of the user terminal.

FIG. 2is a flowchart illustrating a method for modeling an automatic transmission using an artificial neural network, according to an embodiment of the present disclosure, and illustrates a procedure performed by the processor1100.

First, the processor1100generates an artificial neural network (ANN) by combining a plurality of FCNNs and a multi-layer RNN (201). In this case, the ANN may have an architecture to input a result, which is estimated using an initial value and an output of a RNN block, and the output of the RNN block into an RNN block at a next layer. For example, the processor1100may generate an ANN as illustrated inFIG. 3.

Thereafter, the processor1100trains the generated ANN using test data (202). For example, the processor1100may train the ANN to input, as the input value, a preset gear stage, a target gear stage, a current signal of a clutch hydraulic actuator, and an engine torque, and output, as an output value, an engine revolution per minute (RPM), a turbine RPM, a transmission output RPM, and a vehicle acceleration.

Thereafter, the processor1100determines the trained ANN as a model of the automatic transmission (203).

The automatic transmission is modeled in such a manner, so modeling is possible more efficiently with a higher accuracy within a shorter time of period as compared to a conventional method for modeling an automatic transmission based on a motion equation.

Meanwhile, the model of the automatic transmission may be expressed as a function (f) of a relationship of M transmission output signals to N transmission input signals (control signals) for a reference time (T=n), and expressed as following Equation 1. In this case, the automatic transmission may be regarded as a function (f) to map xito yi.

On the assumption that time-series data of the input signal (xi) are X=(x1, x2, . . . , yn), and the time-series data of the output signal (yi) are Y=(y1, y2, . . . , yn), k test data (X, Y) may be expressed in the form of a set (D) as illustrated in following Equation 2.

Accordingly, the modeling for the automatic transmission may be defined to find a function (h) approximating to a function (f). This may be a procedure of generating an ANN, and training the generated ANN using test data related to the input/output of the automatic transmission, as illustrated inFIG. 3.

FIG. 3is a view illustrating an artificial neural network to model an automatic transmission, according to an embodiment of the present disclosure.

As illustrated inFIG. 3, according to an embodiment of the present disclosure, the ANN may include three layers and n ‘RNN’ blocks at each layer. The number of layers and the number of RNN blocks at each layer may be varied depending on the intension of a designer.

At the first layer, a first RNN block111receives a first input value (x1) and inputs an output value of the first RNN block111into a first FCNN121and a first RNN block211at a second layer. In this case, the output value of the first RNN block111is input into a second RNN block112. In addition, the first FCNN121receives an initial value (y1) and the output value of the first RNN block111and inputs an output value of the first FCNN121into the first RNN block211at a second layer and a second FCNN122at the first layer.

At the first layer, the second RNN block112receives a second input value (x2) and the output value of the first RNN block111and inputs an output value of the second RNN block112into a second RNN block212at the second layer. In this case, the output value of the second RNN block112is input into a (n−1)thRNN block113. In addition, the second FCNN122receives the output value of the first FCNN121and inputs an output value of the second FCNN122into the second RNN block212at the second layer and a (k−1)thFCNN123at the first layer.

This procedure may be performed until the final output value (ŷn) for the final input value (xn) is estimated.

At the second layer, the first RNN block211receives the output value of the first FCNN121at the first layer and the output value of the first RNN block111at the first layer and inputs the output value of the first RNN block211into a first RNN block311at a third layer. In this case, the first RNN block211inputs the output value of the first RNN block211into the second RNN block212. In addition, a first FCNN221receives the initial value (y0) and an output value of the first RNN block211and inputs an output value of the first FCNN221into the first RNN block311at the third layer and a second FCNN222at the second layer.

At the second layer, the second RNN block212receives the output value of the second FCNN122at the first layer and the output value of the second RNN block112at the first layer and inputs the output value of the second RNN block212into a second RNN block312at the third layer. In this case, the second RNN block212inputs the output value of the second RNN block212into an (n−1)thRNN block213. In addition, the second FCNN222receives the output value of the first FCNN221and inputs an output value of the second FCNN222into the second RNN block312at the third layer and a (k−1)thFCNN223at the second layer.

This procedure may be performed until the final output value (ŷn)) for the final input value (xn) is estimated.

At the third layer, the first RNN block311receives the output value of the first FCNN221at the second layer and the output value of the first RNN block211at the second layer and inputs the output value of the first RNN block311into a first FCNN321. In this case, the first RNN block311inputs the output value of the first RNN block311into the second RNN block312. In addition, the first FCNN321receives the initial value (y0) and the output value of the first RNN block311, inputs an output value of the first FCNN321into a second FCNN322, and outputs the output value of the first FCNN321as a final output value (ŷ1) for the first input value (x1).

At the third layer, the second RNN block312receives the output value of the second FCNN222at the second layer and the output value of the second RNN block212at the second layer and inputs the output value of the second RNN block312to the second FCNN322. In this case, the second RNN block312inputs the output value of the second RNN block312into an (n−1)thRNN block313. In addition, the second FCNN322receives an output value of the first FCNN321, and inputs an output value of the second FCNN322into a (k−1)thFCNN323. In this case, the second FCNN322outputs the output value of the second FCNN322as a final output value (ŷ2) for the second input value (x2).

This procedure may be performed until the final output value (ŷn)) for the final input value (xn) is estimated.

According to an embodiment of the present disclosure, the best performance is expressed when the ANN includes three layers and 36 RNN blocks at each layer.

FIG. 4is a view illustrating a horizontal flow of an initial value in an artificial neural network to model an automatic transmission, according to an embodiment of the present disclosure.

At the first layer, the initial value (y0) is input into the first FCNN121, and output of the first FCNN121is input into the second FCNN122. The output of the second FCNN122is input into the (k−1)thFCNN123, and the output of the (k−1)thFCNN123is input into the kth FCNN124.

At the second layer, the initial value (y0) is input into the first FCNN221, and the output of the first FCNN221is input into the second FCNN222. The output from the second FCNN222is input into the (k−1)thFCNN223, and the output of the (k−1)thFCNN223is input into the kth FCNN224.

At the third layer, the initial value (y0) is input into the first FCNN321, and the output of the first FCNN321is input into the second FCNN322. The output from the second FCNN322is input into the (k−1)thFCNN323, and the output of the (k−1)thFCNN323is input into the kth FCNN324.

As described above, the initial value is influenced in a horizontal direction at each layer. Accordingly, even if the number of RNN blocks at each layer and the number of the layers are increased, the final output values may be estimated with higher accuracy.

FIG. 5is a flowchart illustrating a method for modeling an automatic transmission using an artificial neural network, according to an embodiment of the present disclosure.

As illustrated inFIG. 5, according to a conventional method for modeling an automatic transmission, data loss is increased in each epoch. Accordingly, the estimation performance may be degraded.

Meanwhile, according to the suggested invention, less data loss in each epoch is represented, so the higher estimation performance may be represented.

According to an embodiment of the present disclosure, in the method for modeling the automatic transmission using the artificial neural network, the result, which is estimated using the initial value and the output of a recurrent neural network (RNN) block, and the output of the RNN block may be input into an RNN block at a next layer, in the artificial neural network generated by combining the plurality of FCNNs and the multi-layer RNN, thereby estimating the final output value having the higher accuracy even if the number of RNN blocks at each layer and the number of the layers are increased.