DEVICE FOR DETERMINING THE CAUSE OF AN ABORMALITY, VEHICLE CONTROL DEVICE AND VEHICLE CONTROL SYSTEM

Abstract

Device (40; 90) for determining the cause of an abnormality, which is used in a vehicle (VC; VC(1)) with an electromagnetic actuator (28), comprising: a storage device (46; 46, 96) configured to store characteristic map data, which are data for defining a characteristic map, wherein the characteristic map has a current variable (ΔI), which is a variable indicating a current actually flowing in the electromagnetic actuator (28), as an input variable, and a cause variable, which is a variable indicating the cause of an abnormality of an on-board unit having the electromagnetic actuator (28), as an output variable; and an implementing device (42, 44; 92, 94) which is configured to perform a detection process (S46, S48; S100) for detecting a value of the input variable based on a detection value from a sensor installed in the vehicle (VC; VC(1)) and a calculation process (S52) for calculating a value of the output variable by inputting the value of the input variable into the map, wherein the on-board unit is a gear-shifting device (26) that changes a gear ratio between a rotational speed of a drive shaft of a drive machine (VC; VC(1)) installed in the vehicle and a rotational speed of drive wheels; the electromagnetic actuator (28) comprises a solenoid valve (28a) of the gearshift device (26); and the current variable (ΔI), which is the input variable, contains a variable that indicates a current flowing in the solenoid valve (28a) during one switching period of the gear ratio in the gear shifting device (26).

Description

Technical field

[0001] The invention relates to a device for determining the cause of an abnormality, a vehicle control device and a vehicle control system. Background of the invention

[0002] For example, JP 2016-219569 A discloses a device that determines, based on a current flowing in the lifting magnet, whether an abnormality has occurred in the lifting magnet. Further prior art is known from WO 2020 / 031677 A1. Summary of the invention

[0003] If an abnormality has occurred in a lifting magnet, a lifting magnet drive device must be disassembled to identify details of the location where the abnormality occurred.

[0004] The invention provides a device for determining the cause of an abnormality, a vehicle control device, and a vehicle control system that can identify details of a location where an abnormality has occurred when an abnormality has occurred in an on-board unit that has an electromagnetic actuator. A device for determining the cause of an abnormality according to a first aspect of the invention is used in a vehicle that has an electromagnetic actuator, a storage device, and an execution device. The storage device is configured to store map data, which is data for defining a characteristic map.The characteristic map contains a current variable, which represents the actual current flowing in the electromagnetic actuator, as an input variable, and a cause variable, which represents the cause of an abnormality in a vehicle onboard unit containing the electromagnetic actuator, as an output variable. The implementing device is configured to perform a detection process to acquire a value for the input variable based on a reading from a sensor installed in the vehicle, and a calculation process to calculate a value for the output variable by inputting the value of the input variable into the characteristic map.

[0005] If an abnormality occurs in a component containing an electromagnetic actuator, the behavior of the current flowing in the actuator will likely be affected. This current behavior will probably vary depending on the specific cause of the abnormality in the component. The device for determining the cause of the abnormality, as described in the first section, calculates the value of the cause variable using a characteristic map that takes the current variable as an input and the cause variable as an output, taking into account the aforementioned points. This allows for the precise identification of the location where the abnormality has occurred.

[0006] In the device for determining the cause of an abnormality according to the first aspect, the onboard unit is a gearshift device that changes the gear ratio between the rotational speed of a drive shaft of a propulsion machine installed in the vehicle and the rotational speed of the drive wheels. The electromagnetic actuator is a solenoid valve of the gearshift device. The current variable, which is the input variable, contains a variable indicating the current flowing in the solenoid valve during one switching cycle of the gear ratio in the gearshift device.

[0007] The current flowing through the solenoid valve during the switching period of the gear ratio depends on the solenoid valve's function. Accordingly, with a device for determining the cause of an abnormality that has this configuration, it is possible to identify details of an abnormal position by including a variable indicating the current flowing through the solenoid valve as the input variable in the characteristic map.

[0008] In the device for determining the cause of an abnormality with the aforementioned configuration, the current variable can include a variable that indicates the difference between the measured value of the current flowing in the solenoid valve during the switching period and a current setpoint. If the setpoint for the current flowing in the solenoid valve changes, the behavior of the current flowing in the solenoid valve will differ from that before the change. Conversely, with the device for determining the cause of an abnormality that has the configuration mentioned above, it is possible to mitigate a change in the value of the current variable due to a change in the current setpoint by using a variable that indicates a difference as the current variable.

[0009] In the device for determining the cause of an abnormality, which has the configuration mentioned above, the storage device can be configured to store a multitude of map data segments that differ depending on the type of switching operation of the conversion ratio. The calculation process can include a selection process for choosing from the multitude of map data segments that correspond to the switching of the conversion ratio in a sampling period of the current variable, which is the input variable. These map segments are then used to define the map for calculating the value of the output variable.

[0010] Since a suitable gearshift control varies depending on the gear ratio, the behavior of the current quantity can also vary depending on the gear ratio. For the device used to determine the cause of an abnormality with the configuration described above, the requirements for the characteristic map are high if a single characteristic map is used, regardless of the type of gear ratio switching operation.Therefore, with the device for determining the cause of an abnormality, which has the above-mentioned configuration, by preparing characteristic map data that differ depending on the types of switching operation of the transmission ratio, and by calculating the value of the output variables using the corresponding characteristic map data, it is possible to perform accurate learning with a small number of training data used to learn the respective characteristic maps, to accurately calculate the value of the output variables with a small number of dimensions of the input variables, and to accurately calculate the value of the output variables with a simple structure.

[0011] In the device for determining the cause of an abnormality, which has the configuration described above, the storage device can be configured to store a plurality of map data segments that differ depending on a torque variable, which is a variable indicating the torque applied to the drive wheels. The acquisition process can include a process for acquiring a value of the torque variable. The calculation process can include a selection process for choosing the map data corresponding to the value of the torque variable acquired by the acquisition process from the plurality of map data segments, as the map data that define the map for calculating the value of the output variable.

[0012] Since the torque applied to the gearshift mechanism varies depending on the torque applied to the drive wheels, the corresponding gearshift control also varies. Consequently, the current behavior is likely to vary depending on the torque. Therefore, the requirements for the characteristic map are high if a single map is used regardless of the torque.Therefore, with the device for determining the cause of an abnormality using the configuration mentioned above, it is possible to perform accurate learning with the small number of training data used to learn the respective maps by preparing map data that differ depending on the torque and calculating the value of the output variables using the corresponding map data, to accurately calculate the value of the output variables with a small number of dimensions of the input variables, or to accurately calculate the value of the output variables with a simple structure.

[0013] In the device for determining the cause of an abnormality with the aforementioned configuration, the current variable, which is the input variable that is simultaneously entered into the characteristic map, can include a variable indicating a current that flowed in the solenoid valve when the same switching was performed in the past, in addition to the variable indicating the current flowing in the solenoid valve during a current switching period of the gear ratio in the gearshift device.

[0014] With the device for determining the cause of an abnormality that has this configuration, it is possible to calculate the value of the cause variable taking into account a history or trends in the behavior of the current by including a variable that indicates the current flowing in the solenoid valve when the same switching was performed in the past as the input variable.

[0015] In the device for determining the cause of an abnormality, which has the configuration mentioned above, the cause variable may include a decrease in the controllability of the solenoid valve due to bubbles contained in a hydraulic fluid of the gearshift device, a temporary sticking abnormality, which is an abnormality that occurs temporarily in an operation of the solenoid valve due to a temporary mixing of foreign substances in the solenoid valve, and a regular sticking abnormality, which is an abnormality that occurs regularly in the operation of the solenoid valve due to a mixing of foreign substances in the solenoid valve.

[0016] The behavior of the currents flowing in the solenoid valve during the three abnormalities likely differs significantly. Therefore, using the device for determining the cause of an abnormality, which has the configuration described above, it is possible to precisely determine, based on the value of the current variable, which of the three abnormalities corresponds to a given abnormality.

[0017] A vehicle control device according to a second aspect of the invention includes a device for determining the cause of an abnormality. The embodiment is configured to perform an abnormality detection process, an alarm process, and a detection process. The abnormality detection process includes a process for determining that an abnormality has occurred in the gearshift device if the difference between the rotational speed of an input shaft of the gearshift device during a transmission ratio shift and a reference rotational speed is equal to or greater than a predetermined value. The alarm process includes a process for issuing an alarm indicating that an abnormality has occurred.The acquisition process includes a process for acquiring the value of the input variable in a period in which the translation ratio is switched when the abnormality detection process determines that an abnormality has occurred.

[0018] In the case of the vehicle control device according to the second aspect, it is possible to specifically determine the cause of the abnormality detected in the abnormality detection process by calculating the value of the cause variable based on the value of the input variable, if an abnormality has been detected in the abnormality detection process.

[0019] In the vehicle control device according to the second aspect, the execution device can be configured to perform a storage process for saving a calculation result of the calculation process in the storage device. Since the calculation result is stored in the storage device, in the vehicle control device with the aforementioned configuration, a unit that determines which treatment is to be carried out on the vehicle can determine the treatment based on the calculation result stored in the storage device, for example, when a user who has been notified by an alarm drives the vehicle to a repair shop.

[0020] A vehicle control device according to a third aspect of the invention includes a device for determining the cause of an abnormality. The on-board unit is a gearshift device that changes a gear ratio between the rotational speed of a drive shaft of a drive motor installed in the vehicle and the rotational speed of drive wheels. The electromagnetic actuator includes a solenoid valve, the characteristic map is a first characteristic map, the characteristic map data are first characteristic map data, the detection process is a first detection process, and the calculation process is a first calculation process.The device is configured to perform a dither control process in which a current flows through the solenoid valve, causing the solenoid to oscillate in a range where the friction engagement element is disengaged (when the transmission ratio does not switch between disengage and engage). The storage device is configured to store secondary characteristic data for defining a second characteristic map. This second map includes the current variable as an input variable when the dither control process is performed and an abnormality variable, indicating whether an abnormality has occurred in the solenoid valve, as an output variable. The device is also configured to perform a second acquisition process and a second calculation process.The second acquisition process involves acquiring a value for the current variable when the dither control process is performed. The second calculation process includes a process for calculating a value for the output variable by inputting the value of the current variable, acquired in the second acquisition process, into the second characteristic map.

[0021] In the vehicle control device according to the third aspect, a value of the abnormality variable, which indicates whether an abnormality has occurred in the solenoid valve, is calculated on the basis of the current flowing in the solenoid valve that controls a friction engagement element into the disengaged state.

[0022] Accordingly, it is possible to determine whether an abnormality exists before an abnormality has occurred in the gearshift control.

[0023] In the vehicle control device according to the third aspect, the execution device can be configured to perform a notification process to inform an external component of the vehicle of the result of the second calculation process. With the vehicle control device having the aforementioned configuration, it is possible to inform the vehicle's exterior of information about a symptom of an abnormality or the like by notifying the vehicle's exterior of the result of the second calculation process, before an abnormality in the actual gearshift control within the vehicle is detected.

[0024] A vehicle control system according to a fourth aspect of the invention comprises the vehicle control device. The embodiment includes a first embodiment provided in the vehicle and a second embodiment not provided in the vehicle. The first embodiment may be configured to perform a data transmission process for transmitting data based on a detected value from the sensor, which is connected to a current actually flowing in the electromagnetic actuator. The second embodiment may be configured to perform a data reception process for receiving data transmitted during the data transmission and computation processes.

[0025] Since, according to the fourth aspect of the vehicle control system, the second execution unit performs the calculation process outside the vehicle, it is possible to further reduce the computational load on the first execution unit compared to a case where the first execution unit performs the calculation process. In the vehicle control system according to the fourth aspect, the second execution unit can be configured to perform the calculation process based on the values ​​of the current variables from a multitude of vehicles. The second execution unit can be configured to perform a feedback process and an update process. The feedback process can be a process for acquiring information indicating that the value of the output variable of the calculation process is invalid.The update process may involve updating the characteristic map data if the feedback process captures information indicating that the value of the output variable is invalid.

[0026] In this vehicle control system configuration, the map data is updated whenever the value of the output variable assigned to each of several vehicles is invalid. Consequently, compared to a case where only the value of a single vehicle's output variable is processed, the amount of data required for updates can be slightly increased, and the map data can be used to accurately calculate the value of the output variable during actual vehicle operation.

[0027] In the vehicle control system according to the fourth aspect, the second execution device can be configured to perform a result transmission process to transmit a calculation result of the calculation process. The first execution device can be configured to perform a result reception process to receive the calculation result transmitted in the result transmission process.

[0028] A vehicle control device according to a fifth aspect of the invention comprises the first embodiment in the vehicle control system. Brief description of the characters

[0029] Features, advantages, and technical and industrial significance of exemplary embodiments of the invention are described below with reference to the accompanying figures, in which the same symbols denote the same elements, and wherein: Fig. Figure 1 is a diagram showing a configuration of a drive system and a control device for a vehicle according to a first embodiment of the invention; Fig. Figure 2 is a block diagram showing processes performed by the control device according to the first embodiment; Fig. Figure 3 is a flowchart showing a process sequence that is executed by the control device according to the first embodiment; Fig. 4 is a time diagram showing deflection values ​​according to the first embodiment; Fig. Figure 5 is a flowchart showing a process sequence that is executed by the control device according to the first embodiment; Fig. Figure 6A is a time diagram showing a relationship between the behavior of a current and a rotational speed at the time of the switching operation and a cause of abnormality according to the first embodiment; Fig. Figure 6B is a time diagram showing a relationship between the behavior of a current and a rotational speed at the time of the switching operation and a cause of abnormality according to the first embodiment; Fig. Figure 6C is a time diagram showing a relationship between the behavior of a current and a rotational speed at the time of the switching operation and a cause of abnormality according to the first embodiment; Fig. Figure 6D is a time diagram showing a relationship between the behavior of a current and a rotational speed at the time of the switching operation and a cause of abnormality according to the first embodiment; Fig. 7 is a diagram showing the definition of output variables according to the first embodiment; Fig. 8 is a diagram showing a configuration of a system according to a second embodiment of the invention; Fig. 9A is a flowchart showing a process flow carried out by the system according to the second embodiment; Fig. 9B is a flowchart showing a process flow that is carried out by the system according to the second embodiment; Fig. 10 is a diagram showing the definition of output variables according to the second embodiment; Fig. Figure 11 is a diagram of a system showing a configuration according to a third embodiment of the invention; Fig. Figure 12A is a flowchart showing a process sequence carried out by the system according to the third embodiment; and Fig. Figure 12B is a flowchart showing a process flow that is carried out by the system according to the third embodiment. Description of the embodiments

[0030] A first embodiment is described below with reference to the accompanying figures. As shown in Fig. As shown in Figure 1, a power distribution device 20 is mechanically connected to a crankshaft 12 of an internal combustion engine 10. The power distribution device 20 divides the power of the internal combustion engine 10, a first motor-generator 22, and a second motor-generator 24. The power distribution device 20 has a planetary gear mechanism. The crankshaft 12 is mechanically connected to a carrier CR of the planetary gear mechanism. A shaft 22a of the first motor-generator 22 is mechanically connected to a sun gear S, and a shaft 24a of the second motor-generator 24 is mechanically connected to a ring gear R. An output voltage of a second inverter 25 is applied to the terminals of the second motor-generator 24.

[0031] In addition to the shaft 24a of the second motor-generator 24, drive gears 30 are mechanically connected to the ring gear R of the power distribution device 20 via a gearshift device 26. A drive shaft 32a of an oil pump 32 is mechanically connected to the support CR. The oil pump 32 is a pump that circulates oil in an oil pan 34 as a lubricant to the power distribution device 20 or delivers the oil as hydraulic fluid to the gearshift device 26. The pressure of the hydraulic fluid delivered by the oil pump 32 is set by a hydraulic pressure control circuit 28 in the gearshift device 26 and utilized in the hydraulic fluid. The hydraulic pressure control circuit 28 is a circuit that has a plurality of solenoid valves 28a and controls the flow state of the hydraulic fluid or the hydraulic pressure of the hydraulic fluid by activating the solenoid valves 28a.

[0032] A control device 40 controls the internal combustion engine 10 and operates various operating units of the internal combustion engine 10 to control torque, exhaust gas composition ratio, and the like as control values. The control device 40 controls the first motor-generator 22 and operates the first inverter 23 to control torque, speed, and the like as control values. The control device 40 controls the second motor-generator 24 and operates the second inverter 25 to control torque, speed, and the like as control values.

[0033] The control device 40 controls the control values ​​with reference to an output signal Scr of a crank angle sensor 50, an output signal Sm1 of a first rotation angle sensor 52, which detects a rotation angle of the shaft 22a of the first motor generator 22, or an output signal Sm2 of a second rotation angle sensor 54, which detects a rotation angle of the shaft 24a of the second motor generator 24. The control device 40 also refers to an oil temperature T-oil, which is an oil temperature detected by an oil temperature sensor 56, a vehicle speed SPD detected by a vehicle speed sensor 58, an accelerator pedal actuation amount ACCP, which is the amount of depressurization of an accelerator pedal 60 detected by an accelerator pedal sensor 62, and a current I flowing in the solenoid valves 28a, which is detected by a current sensor 64. The current sensor 64 actually has a large number of dedicated sensors that detect the currents of the large number of solenoid valves 28a.

[0034] The control device 40 comprises a CPU 42 and a ROM 44, a storage device 46, which is an electrically rewritable non-volatile memory, and a peripheral circuit 48, which can communicate with each other via a local network 49. The peripheral circuit 48 includes a circuit that generates a clock signal for setting internal processes, a power supply circuit, and a reset circuit. The control device 40 controls the control values ​​by causing the CPU 42 to execute a program stored in the ROM 44.

[0035] Fig. Figure 2 shows some processes that are carried out by the control device 40. The in Fig. The processes shown in the two examples are implemented by causing the CPU 42 to repeatedly execute a program stored in the ROM 44, e.g. at intervals of a predetermined duration.

[0036] A gear ratio setpoint adjustment process M10 is a process for setting a gear ratio setpoint Vsft*, which is a setpoint for a gear ratio based on the accelerator pedal actuation amount ACCP and the vehicle speed SPD during a gear ratio switching period. A hydraulic pressure setpoint adjustment process M12 is a process for setting a hydraulic pressure setpoint P0*, which is a base value for a hydraulic pressure set by a solenoid valve 28a. This valve is used for switching based on the accelerator pedal actuation amount ACCP, the oil temperature T-oil, the gear ratio setpoint Vsft*, and a switching variable ΔVsft at the time of the gear ratio switch. The switching variable ΔVsft indicates whether the gear ratio switch is an upshift or a downshift.If the gear ratio setpoint Vsft* indicates third gear and the shift variable ΔVsft is set to upshift, this means that one type of shift operation is a shift from third to fourth gear. The hydraulic pressure setpoint adjustment process M12 is implemented by instructing the CPU 42 to calculate the hydraulic pressure setpoint P0* in a state where map data with the accelerator pedal actuation amount ACCP, the type of shift operation, and the oil temperature T-oil as input variables and the hydraulic pressure setpoint P0* as the output variable are pre-stored in ROM 44. Map data are combinations of discrete values ​​of input variables and values ​​of output variables that correspond to the values ​​of the input variables. The map calculation can, for example,a process of outputting the value of the corresponding output variable of the map data as a result of the calculation if a value of an input variable matches one of the values ​​of the input variables of the map data, and outputting a value obtained by interpolating values ​​of a multitude of output variables contained in the map data as a result of the calculation if the value of an input variable does not match any value of the input variables.

[0037] Specifically, the hydraulic pressure setpoint P0* indicates the following: Fig. The diagram shows phases 1, 2, and 3. Here, phase 1 is the period from the point at which a switching command for the transmission ratio is issued until a predetermined time interval has elapsed. Phase 2 is the period until a torque phase ends, and phase 3 is the period until the switching of the transmission ratio ends. In phase 3, the value of the output variable of the characteristic map data is actually set to a rate of increase of the hydraulic pressure setpoint P0*.

[0038] A learning correction value calculation process M14 is a process for calculating a correction value ΔP to correct the hydraulic pressure setpoint P0* based on a deflection ΔNm2, which is a difference between a rotational speed Nm2 of the shaft 24a of the second motor generator 24 and a reference rotational speed Nm2*. Here, if the deflection ΔNm2 is a value within the switching period of the gear ratio in a range determined by the accelerator pedal actuation amount ACCP and the type of switching operation used to determine the hydraulic pressure setpoint P0*, then the correction value ΔP is a value within that range. The rotational speed Nm2 is calculated by the CPU 42 based on an output signal Sm2 from the second rotary angle sensor 54. The CPU 42 provides the gear ratio setpoint Vsft*, the switching variable ΔVsft, and the vehicle speed SPD as inputs for the reference rotational speed Nm2.This process can be realized by causing the CPU 42 to map the rotational speed Nm2* in a state in which map data with the gear ratio setpoint Vsft*, the switching variable ΔVsft and the vehicle speed SPD as input variables and with the reference rotational speed Nm2* as output variable are pre-stored in ROM 44.

[0039] A correction process M16 is a process for calculating a hydraulic pressure setpoint P* by adding the correction value ΔP to the hydraulic pressure setpoint P0*. A current conversion process M18 is a process for converting the hydraulic pressure setpoint P* into a setpoint of a current (a current setpoint I*) that flows in the solenoid valves 28a.

[0040] When the value of the translation ratio setpoint Vsft* changes, the control device 40 switches the friction engagement elements from a disengaged state to an engaged state by changing the current setpoint I* for each phase, as shown in Fig. 2 shown. The hydraulic pressure setpoint or the current setpoint corresponding to a friction engagement element switching from the engaged state to the disengaged state can also be calculated by characteristic map calculation based on the aforementioned characteristic map data.

[0041] Fig. Figure 3 shows a sequence of processes carried out by the control device 40. The in Fig. The process sequence shown in Figure 3 is achieved by instructing the CPU 42 to repeatedly execute a program stored in ROM 44, for example, at intervals of a predetermined duration. In the following description, a number prefixed with "S" denotes the step number of each process.

[0042] In the Fig. In the series of processes shown in Figure 3, the CPU 42 first determines whether it is time to control the switching of the gear ratio (S10). If it is determined that it is time to control the switching of the gear ratio (S10: YES), the CPU 42 acquires the accelerator pedal actuation amount ACCP, the gear ratio setpoint Vsft*, the switching variable ΔVsft, and the oil temperature T-oil (S12). The CPU 42 calculates a current difference ΔI, which is the difference between the current I flowing in the solenoid valves 28a to switch the friction engagement elements, which are switched from the disengaged state to the engaged state by this switching, and the current setpoint I*, and stores the calculated current difference ΔI in the storage device 46 (S14).

[0043] The CPU 42 then determines whether a predetermined time interval has elapsed since the shift command (S16). Here, the predetermined time interval is set based on a maximum value of the time required to complete the gearshift control. If it is determined that the predetermined time interval has not yet expired (S16: NO), the CPU 42 determines whether a state in which an absolute value of the difference between the rotational speed Nm2 of shaft 24a of the second motor-generator 24 and the reference rotational speed Nm2* is equal to or greater than a threshold value ΔNm2th should be continued for a predetermined time (S20). This process is used to determine whether an abnormality has occurred in the gearshift control.

[0044] This means that if an abnormality occurs in the gearshift control, a phenomenon occurs in which the input speed of the gearshift device 26 increases sharply, or something similar. Accordingly, as if by a single-point chain line in Fig. 4 indicated a phenomenon in which the rotational speed NE of the crankshaft 12 or the rotational speed Nm2 of the shaft 24a of the second motor generator 24 increases. Fig. Figure 4 shows the changes in hydraulic pressures Pc2 and Pc1 and their setpoints Pc2* and Pc1* together with the changes in rotational speeds NE, Nm1 and Nm2 and the torque setpoints Trqm1* and Trqm2*. Here, rotational speed NE is the rotational speed of crankshaft 12, and rotational speed Nm1 is the rotational speed of shaft 22a of the first motor-generator 22. The torque setpoint Trqm1* is a torque setpoint for the first motor-generator 22, and the torque setpoint Trqm2* is a torque setpoint for the second motor-generator 24. The in Fig. The hydraulic pressures Pc2 and Pc1 shown are a hydraulic pressure of an engagement element and a hydraulic pressure of a disengagement element of the friction engagement elements required for the switching operation.

[0045] The setpoint values ​​Pc2* and Pc1* are adjusted to dampen the occurrence of a phenomenon in which the input speed of the gearshift device 26 increases, or similar occurrences. The speed Nm2* used as a reference at the time of the shifting process is determined by this setting.

[0046] With reference to Fig. 3. If it is determined that the condition persists for the specified time or longer (S20: YES), CPU 42 temporarily detects that an abnormality has occurred (S22). CPU 42 returns the process flow to process S14 when process S22 is complete or if the detection result of process S20 is negative.

[0047] On the other hand, if it is determined that the specified time period has elapsed (S16: YES), CPU 42 determines whether the switching operation is complete (S18). Here, CPU 42 can determine that the switching operation is not complete if the actual gear ratio has not reached the target gear ratio value Vsft*. If it is determined that the switching operation has not been completed (S18: NO), CPU 42 determines that an abnormality has occurred (S24).

[0048] However, if it is determined that the switching operation has been completed (S18: YES), the CPU 42 determines whether a temporary abnormality detection has been performed (S25). If it is determined that a temporary abnormality detection has been performed (S25: YES), the CPU 42 increments a counter C by 1 (S26). The CPU 42 then determines whether the value of counter C is equal to or greater than a predetermined value Cth that is greater than 1 (S28). If it is determined that the value of counter C is equal to or greater than the predetermined value Cth (S28: YES), the CPU 42 determines that an abnormality has occurred (S24). The CPU 42 then performs a fail-safe process of setting the gear ratio to a predetermined gear ratio (S30).The predetermined transmission ratio is a transmission ratio in which a friction engagement element, which must be in the engaged state when an abnormality occurs, is switched to the disengaged state.

[0049] CPU 42 executes an alarm process in which a display 70 is caused to show visual information indicating that an abnormality has occurred, by the in Fig. 1. Display 70 shown is activated (S32). Then the CPU 42 stores data indicating that the detection of an abnormality has been carried out, and the accelerator pedal actuation amount ACCP, the gear ratio setpoint Vsft*, the switching variable ΔVsft and the oil temperature T-oil, when the abnormality occurred, in the storage device 46 (S34).

[0050] CPU 42 temporarily terminates a series of in Fig. 3 processes shown, if the process of S34 is completed or if the findings of S10, S25 and S28 are negative. Fig. Figure 5 shows a further sequence of processes executed by the control device 40. The in Fig. The processes shown in 5 are realized by causing the CPU 42 to repeatedly execute a program stored in ROM 44, e.g. at intervals of a predetermined duration.

[0051] In a series of processes that took place in Fig. As shown in 5, the CPU 42 first determines whether an abnormality has been detected by the in Fig. The sequence of processes shown in S40 was carried out. If it is determined that the detection of an abnormality has been carried out (S40: YES), the CPU 42 reads the accelerator pedal actuation amount ACCP, the gear ratio setpoint Vsft*, and the switching variable ΔVsft, which are stored in the memory device 46 in the process from S34 in Fig. 3 (S42) are stored. Then the CPU 42 selects and reads the corresponding characteristic map data DM from the data stored in the Fig. 1. Storage device 46 stored characteristic map data DM based on the accelerator pedal actuation amount ACCP and the type of shift operation when the abnormality occurred (S44). That is, the characteristic map data DM corresponding to areas A1, A2, ..., A7, B1, ... which are subject to a subdivision based on the accelerator pedal actuation amount ACCP and the type of shift operation, which is used to determine the hydraulic pressure setpoint P0* in the Fig. The hydraulic pressure setpoint adjustment process M12 shown in section 2 is stored in the storage device 46.

[0052] Then the CPU 42 reads the current differences ΔI(1), ΔI(2), ... and ΔI(n), which are time series data of the current difference ΔI that are in the process of S14 in Fig. 3 (S46). The current differences ΔI(1), ΔI(2), ..., and ΔI(n) are time series data of the current difference ΔI in a period in which the turns ratio is switched when an abnormality is detected. The time series data of the current difference ΔI are data that show a correlation with a cause of an abnormality.

[0053] The Fig. Figures 6A to 6D show changes in current I, hydraulic pressure Pc2, and a deflection ΔNm2 by which the rotational speed Nm2 is higher than the reference rotational speed Nm2* at the time of the switching operation. Six sampled values ​​of the deflection ΔNm2 are shown in the right-hand sections of Fig. 6A to 6D are shown. This shows Fig. 6A is an example of the changes in a normal state and Fig. Figures 6B to 6D show examples of the changes that occur in an abnormal condition.

[0054] In particular Fig. Figure 6B shows an example where the rotational speed Nm2 exhibits different behavior than under normal conditions because air is mixed into the solenoid valves 28a and an abnormality occurs in the control of the hydraulic pressure Pc2 by the feedback control. The actual behavior of the current at this time differs from that under normal conditions. Fig. Figure 6C shows an example where a foreign substance is mixed into the solenoid valves 28a, resulting in a temporary sticking, i.e., an abnormality where the valves temporarily do not operate. In this case, the deflection ΔNm2 temporarily exceeds a threshold value ΔNm2th due to a temporary bluntness of the rise in hydraulic pressure Pc2. The behavior of the current I at this time is different than in Fig. 6B shown. Fig. Figure 6D shows an example where a foreign object has penetrated the solenoid valves 28a, resulting in complete sticking, i.e., an abnormality where the valves do not operate normally. Since the hydraulic pressure Pc2 is low in this case, the friction engagement elements are not switched to the engaged state, and a state in which the deflection ΔNm2 is greater than the threshold ΔNm2th persists. The behavior of the current I in this case is also different than in Fig. 6B shown.

[0055] With reference to Fig. 5. The CPU 42 reads the current differences ΔI(-p+1), ΔI(-p+2), ... and ΔI(-p+n), which are the time series data of the current difference ΔI that are processed in the S14 process. Fig. 3 were stored in a period in which the same switching of a gear ratio is carried out as when an abnormality occurs, before it is determined that an abnormality has occurred (S48). Here, the “same switching of a gear ratio as when an abnormality occurs” means that the type of shift operation and the accelerator pedal actuation amount ACCP are in the same range as when an abnormality occurs from ranges A1, A2, ..., which are used to set the in Fig. The hydraulic pressure setpoint P0* shown in point 2 can be used. It is better to use a condition that, if an abnormality has occurred, the absolute value of the difference in oil temperature T-oil is equal to or less than a predetermined value.

[0056] Then, CPU 42 replaces the time series data acquired in processes S46 and S48 with input variables x(1) to x(2n) of a characteristic map defined by the characteristic map data DM selected in process S44 (S50). That is, with "i = 1 to n", the current difference ΔI(i) is inserted into the input variable x(i) and the current difference ΔI(-p+i) into the input variable x(n+i).

[0057] Then the CPU 42 calculates the values ​​of the output variables y(1), y(2), ..., y(q) by replacing the values ​​of the input variables x(1) to x(2n) in the characteristic map defined by the characteristic map data DM selected in the process of S44 (S52).

[0058] In the first embodiment, a function approximation operator is shown as a characteristic map, and, for example, a neural network of the type Total Binding Forward Propagation (TBP) with a single intermediate layer is demonstrated. Specifically, the value of an intermediate layer node is determined by replacing "m" values, obtained by converting the input variables x(1) to x(2n), into the values ​​in the process S50, and a bias parameter x(0) is replaced into an activation function f using a linear characteristic map defined by coefficients wFjk (where j = 1 to m, k = 0 to 2n). Additionally, values ​​of the output variables y(1), y(2), y(3), ... are determined by replacing values ​​obtained by converting the intermediate layer node's value using the linear characteristic map defined by the coefficients wSij into an activation function g.In the first embodiment, a hyperbolic tangent function is shown as an activation function f by way of example.

[0059] As in Fig. As shown in Figure 7, the output variables y(1), y(2), y(3), ... are causal variables used to identify the cause of an abnormality. Fig. In 7, the output variable y(1) indicates a probability that the in Fig. In the air mixing shown in 6B, the output variable y(2) indicates a probability that a Fig. 6C shows temporary adhesion occurring, and the output variable y(3) indicates a probability that a Fig. 6D shows complete adhesion occurring.

[0060] Referring back to Fig. 5. The CPU 42 selects a maximum value ymax from the output variables y(1) to y(q) (S54). Then, based on the same output variables as the maximum value ymax from the output variables y(1) to y(q), the CPU 42 identifies a cause of the abnormality and stores the result of the cause identification in the storage device 46 (S56). For example, if the value of the output variable y(1) is equal to the maximum value ymax, the CPU 42 stores data indicating that the cause of the detected abnormality is the mixing of air in the storage device 46.

[0061] CPU 42 temporarily terminates a series of in Fig. The processes shown in section 5 are triggered when process S56 is complete or when the result of process S40 is negative. The characteristic map data DM consists of a model trained using the current difference ΔI obtained by driving a prototype vehicle or similar, and data indicating whether an actual abnormality occurred as training data prior to the delivery of vehicle VC.

[0062] The operation and advantages of the first embodiment are described below. The CPU 42 detects whether an abnormality has occurred in the gearshift control because the absolute value of the difference between the rotational speed Nm² in a gear ratio shift cycle and the reference rotational speed Nm²* is equal to or greater than a threshold value ΔNm²th. If an abnormality is detected, the CPU 42 executes a fail-safe process and notifies a user that an abnormality has occurred. If an abnormality is detected, the CPU 42 identifies a cause of the abnormality based on the current I at that time. By referring to the behavior of the current I, it is thus possible to identify a cause of the abnormality.

[0063] The following processes and advantages are achieved according to the first embodiment described above. (1) The current difference ΔI, instead of the current I, is used as the input variable of the characteristic map. Since, in the first embodiment, the hydraulic pressure setpoint P0* is corrected based on the correction value ΔP, the current setpoint I* varies depending on the correction value ΔP, even within the same range defined by the accelerator pedal actuation amount ACCP and the type of shift operation. Even if the range defined by the accelerator pedal actuation amount ACCP and the type of shift operation is the same, and the oil temperature T-oil is the same, the current setpoint I* varies due to the characteristic map calculation. Accordingly, the behavior of the current I varies depending on the current setpoint I*, which is not directly related to an abnormality.Accordingly, by using the current difference ΔI instead of the current I, it is possible to limit the variation of the input variable x due to the variation of the current setpoint I*. In this way, by processing information that serves as the cause of an abnormality as a characteristic and inputting the processed information into a characteristic map, it is possible to calculate the value of the output variable more accurately.

[0064] (2) The characteristic map data DM used to calculate the output variable are selected according to the type of switching operation. Since the appropriate control varies depending on the type of switching operation, the behavior of the current I can vary depending on the turns ratio. If a single characteristic map is used regardless of the type of switching operation, the requirements for the characteristic map are high. On the other hand, according to the first embodiment, characteristic map data that differ depending on the type of switching operation are prepared, and the value of the output variable is calculated using the corresponding characteristic map data.Accordingly, it is possible to perform accurate learning with a small number of training data used to learn the respective characteristic curves, to accurately calculate the value of the output variable with a small number of dimensions of the input variable, or to accurately calculate the value of the output variable with a simple structure.

[0065] (3) The characteristic map data DM, which is used to calculate the output variables, is selected according to the accelerator pedal actuation amount ACCP. The accelerator pedal actuation amount ACCP has a positive correlation with the torque applied to the drive wheels 30. Since, on the other hand, the torque applied to the gearshift device 26 varies depending on the torque applied to the drive wheels 30, the corresponding control varies in the shift period of the gear ratio. Accordingly, the current behavior varies depending on the accelerator pedal actuation amount ACCP. Therefore, the requirements for the characteristic map are high if a single characteristic map is used regardless of the accelerator pedal actuation amount ACCP.On the other hand, according to the first embodiment, map data is prepared that differs depending on the accelerator pedal actuation amount (ACCP), and the value of the output variable is calculated using the corresponding map data. Accordingly, it is possible to perform accurate learning with the small amount of training data used for learning the respective maps, to accurately calculate the value of the output variable with a small number of input variable dimensions, or to accurately calculate the value of the output variable with a simple structure.

[0066] (4) In addition to the current differences ΔI(1) to ΔI(n) in the current switching period, in which the gear ratio in the gearshift device 26 is switched, the current differences ΔI(-p+1) to ΔI(-p+n), when the same switching has been carried out in the past, are included in the input variables that are simultaneously entered into the characteristic map. Accordingly, it is possible to calculate a value of a cause variable taking into account a history and trends in the behavior of the current.

[0067] (5) If the degree of deviation between the rotational speed Nm2 and the reference rotational speed Nm2* during the shifting period of the gear ratio is equal to or greater than a predetermined value, an abnormality in the gearshift device 26 is detected, and the values ​​of the output variables y(1) to y(q) are calculated based on the current difference ΔI when the abnormality is detected. Accordingly, it is possible to specifically determine the cause of the abnormality when an abnormality is detected.

[0068] (6) When the values ​​of the output variables y(1) to y(q) are calculated, the CPU 42 stores a cause identified based on a maximum value of these variables in the memory device 46. Accordingly, if a user who has been notified of an alarm drives the vehicle, for example, to a repair shop, a unit that determines what treatment is required for the vehicle can determine the treatment based on the calculation result stored in the memory device 46. That is, if, for example, air mixing has occurred, it can be checked whether an antifoaming agent or similar substance contained in the hydraulic fluid has deteriorated. If the hydraulic fluid has deteriorated, it is possible to suggest a change of the hydraulic fluid without disassembling the gearshift device 26. If, for example, temporary sticking has occurred, it is possible to check the operation.If the device is operating normally, it is possible to notify a user that an abnormality has temporarily occurred due to temporary sticking without disassembling the gearshift device 26.

[0069] A second embodiment of the invention is described below with reference to the figures, focusing on the differences from the first embodiment.

[0070] Fig. Figure 8 shows a configuration of a system according to the second embodiment. Fig. 8 will be elements that the in Fig. The elements shown in section 1 correspond to those shown, which for simplicity are designated with the same reference symbols, and their descriptions are not repeated. As in Fig. As shown in Figure 8, symptom detection data DPD is stored in the storage device 46 of a vehicle VC(1) in addition to the characteristic map data DM. The control device 40 has a communication device 47 and can communicate with a data analysis center 90 via an external network 80 using the communication device 47.

[0071] The data analysis center 90 collects and analyzes data transmitted as a Big Data database by a large number of vehicles VC(1), VC(2), ... The data analysis center 90 comprises a CPU 92, a ROM 94, a storage device 96, and a communication device 97, which can communicate with each other via a local network 99. The storage device 96 is a non-volatile, electrically rewritable memory that stores the Big Data database.

[0072] In the second embodiment, before an abnormality occurs in the gearshift control, a symptom of the abnormality of the gearshift device 26 is detected and a result of the symptom detection is transmitted to the data analysis center 90. Fig. 9A and Fig. Figure 9B shows a sequence of processes associated with the transmission and receipt of the symptom assessment result and from the in Fig. The system shown in section 8 can be executed. In particular, in Fig. 9A and Fig. Figure 9B shows a sequence of processes associated with the transmission and reception of the symptom detection result of an abnormality in a part connected to a first clutch. The processes associated with the transmission and reception of the symptom detection result of an abnormality in a part associated with another clutch or a brake are the same as those shown in Figure 9B. Fig. 9A and Fig. 9B are shown, and therefore their description is omitted.

[0073] In particular, the in Fig. The processes shown in Figure 9A are realized by causing the CPU 42 to repeatedly execute a program stored in ROM 44, e.g., at intervals of a predetermined duration. The processes shown in Figure 9A are implemented by causing the CPU 42 to repeatedly execute a program stored in ROM 44, e.g., at intervals of a predetermined duration. Fig. The processes shown in Figure 9B are implemented by causing the CPU 92 to repeatedly execute a program stored in ROM 94, e.g. at intervals of a predetermined duration.

[0074] In a series of processes that took place in Fig. As shown in Figure 9A, the CPU 42 first acquires a gear ratio setpoint Vsft* (S60). Then, based on the gear ratio setpoint Vsft* (S62), the CPU 42 determines whether the current gear ratio is sufficient to keep the first clutch in a disengaged state. If it is determined that the current gear ratio keeps the first clutch disengaged (S62: YES), the CPU 42 performs dither control, which is a control to finely increase or decrease the inrush current of the corresponding solenoid valve 28a of the hydraulic pressure control circuit 28 (S64). Dither control is a process in which an inrush current flows, causing the solenoid valve 28a to oscillate slightly, and the amplitude of the oscillation is within a range that does not disengage the first clutch.

[0075] The CPU 42 then acquires the current I of the corresponding solenoid valve 28a (S66). The CPU 42 then determines whether a continuous execution period of the dither control reaches a predetermined duration after the process of S70, described later, has been completed (S68). If it is determined that the continuous execution period reaches the predetermined duration (S68: YES), the CPU 42 replaces the currents I(1), I(2), ..., I(n), which are time series data acquired in the process of S66 over the predetermined duration, into the input variables x(1) to x(n) of a characteristic map defined by the symptom detection data DPD, which is described in the Fig. 8 shown storage device 46 are stored (S70).

[0076] The CPU then calculates 42 values ​​of output variables z(1), z(2), ..., z(p) by replacing the values ​​of the input variables x(1) to x(n) in the map defined by the symptom detection data DPD (S72).

[0077] In the second embodiment, a function approximation operator is shown as the characteristic map, and, for example, a neural network of the type Total Binding Forward Propagation (TBP) with a single intermediate layer is demonstrated. Specifically, the value of an intermediate layer node is determined by substituting "m" values, obtained by converting the input variables x(1) to x(n), into the values ​​in the S70 process, and by inserting a bias parameter x(0) into an activation function f using a linear characteristic map defined by coefficients wOjk (where j = 1 to m, k = 0 to n). Additionally, the values ​​of the output variables z(1), z(2), z(3)... are determined by substituting values ​​obtained by converting the intermediate layer node's value into an activation function u using a linear characteristic map defined by coefficients w Tij.In the second embodiment, a hyperbolic tangent function is shown as an activation function h and a softmax function as an activation function u.

[0078] As in Fig. As shown in Figure 10, the output variable z(1) is a causal variable that indicates a normal probability, the output variable z(2) is a variable that indicates a probability that a mixing of the air occurs, and the output variable z(3) is a variable that indicates a probability that a temporary adhesion occurs.

[0079] Referring back to Fig. 9A and Fig. 9B The CPU 42 extracts a maximum value zmax from the values ​​of the output variables z(1), z(2), z(3), ... (S74). The CPU 42 then determines whether the value of the output variable z(1) equals the maximum value zmax (S76). This process determines whether a component associated with the first clutch is functioning normally. If it is determined that a value other than the output variable z(1) equals the maximum value zmax (S76: NO), the CPU 42 sends a notification indicating that a symptom of an abnormality is present at the time of gearshift control, and information for identifying the type of abnormality by actuating the communication device 47 (S78).

[0080] CPU 42 temporarily terminates a series of in Fig. 9A shown processes, if the process of S78 is completed or if the finding result of the process of S76 is positive or if the finding results of the processes of S62 and S68 are negative.

[0081] On the other hand, CPU 92 of data analysis center 90 determines whether a notification indicating the symptom capture result has been received (S80) and stores the capture result as a Big Data DB in storage device 96 (S82) if it is determined that a notification has been received (S80: YES). CPU 92 temporarily terminates a number of processes that are in Fig. 9B are shown when the process of S82 is completed or when the determination result of the process of S80 is negative.

[0082] The symptom detection data DPD is a model that is trained using time series data prior to the shifting operation, which is normally carried out from time series data of current I collected at the time of driving a prototype vehicle or the like, and time series data immediately before the occurrence of an abnormality in the gear shift control as training data prior to the delivery of vehicles VC(1), VC(2), ....

[0083] As described above, according to the second embodiment, dither control is performed to increase or decrease the turn-on current of a corresponding solenoid valve in a state where a clutch or brake is disengaged, and it is determined whether there is a symptom of an abnormality in the gearshift control based on the behavior of the solenoid valve's turn-on current at that time. If a symptom is detected, the data analysis center 90 can collect the symptom detection results from a large number of vehicles VC(1), VC(2), ... by delivering the detection results to the data analysis center 90. Accordingly, by collecting detection results indicating that an abnormality has indeed occurred subsequently, it is possible to analyze which behavior causes a later abnormality based on a big data database.Consequently, if the symptom detection result is found to be inadequate, the symptom detection data DPD can be retrained. If the reliability of the symptom detection results based on the symptom detection data DPD is high, a user can be notified that an abnormality is likely to occur before the abnormality actually manifests in the gearshift control.

[0084] A third embodiment is described below with reference to the drawings, focusing on the differences from the first embodiment.

[0085] Fig. Figure 11 shows a configuration of a system according to the third embodiment. Fig. 11 elements will be included, which are in Fig. The 8 elements shown correspond to each other and, for the sake of simplicity, are designated with the same reference symbols. As in Fig. As shown in Figure 11, in the third embodiment, characteristic map data DM or symptom recognition data DPD are stored in the storage device 96 of the data analysis center 90.

[0086] Fig. 12A and Fig. Figure 12B shows a sequence of processes for determining a cause of abnormality based on the output variables y(1), y(2), ..., which is derived from the one in Fig. The system shown in section 11 is carried out. In particular, the processes described in section 11 are performed. Fig. The processes shown in Figure 12A are realized by causing the CPU 42 to repeatedly execute a program stored in ROM 44, e.g., at intervals of a predetermined duration. The Fig. The processes shown in Figure 12B are implemented by causing the CPU 92 to repeatedly execute a program stored in ROM 94, for example, at intervals of a predetermined duration. In the Fig. 12A and Fig. The processes shown in 12B are processes that correspond to the one described in Fig. The five processes shown correspond to each other, designated with the same step numbers for simplicity, and their descriptions are not repeated. A series of in Fig. 12A and Fig. The processes shown in 12B are described below along a time series of the processes for determining a cause of abnormality based on output variables y(1), y(2), ....

[0087] As in Fig. As shown in Figure 12A, the CPU 42 of the control device 40 performs the processes of S40, S42, S46 and S48 and then transmits the data read in the processes of S42, S46 and S48 together with an identifier of the vehicle VC(1) by actuating the communication device 47 (S90).

[0088] On the other hand, CPU 92 of the data analysis center receives 90, as in Fig. Figure 12B shows the data and identifier transmitted in process S90 (S100). CPU 92 then executes processes S44 and S50 through S54 using the received data. CPU 92 then transmits data related to the result of identifying an abnormality cause based on a variable with a maximum value ymax from the output variables y(1) to y(q) to a transmission source of the data received in process S100 by operating communication device 97, and stores the data in storage device 96 (S102).

[0089] On the other hand, the CPU receives 42, as in Fig. Figure 12A shows data related to the result of the determination, which was transmitted in the process from S102 (S92). Then the CPU 42 stores the result of the determination in the memory device 46 (S94).

[0090] CPU 42 temporarily terminates a process that is in Fig. The series of processes shown in 12A occurs when the process of S94 is completed or when the result of the process of S40 is negative. Accordingly, if a user disables the vehicle VC(1) through the alarm process of S32 in Fig. 3. When the vehicle is taken to a repair shop, the repair shop can understand the cause of the abnormality by accessing the storage device 46. For example, if a finding indicating that the abnormality is a temporary sticking is stored, and the abnormality in the gearshift control is not corrected, the repair shop can identify the cause of the abnormality. If it is determined that the finding is invalid, the repair shop reports data indicating this fact to the data analysis center 90.

[0091] On the other hand, as in Fig. As shown in Figure 12B, the CPU 92 of the data analysis center 90 determines whether the feedback has been performed (S104). If the feedback has been performed (S104: YES), the CPU 92 updates the map data DM so that the value of the output variable of the map, which is determined by the map data DM with the values ​​of the input variables x(1) to x(2n) that were entered there when the faulty detection was performed, shows a correct abnormality cause that has been reported back (S106).

[0092] CPU 92 temporarily terminates a number of processes that are running in Fig. 12B shows when the process of S106 has been completed or when the result of the process of S104 is negative. Furthermore, the process of calculating the values ​​of the output variables z(1), z(2), ... can be carried out in the same way as shown in Fig. 12A and Fig. The processes shown in 12B are carried out so that their description is not repeated.

[0093] In this way, according to the third embodiment, it is possible to reduce the computational load of CPU 42 by causing the exterior of vehicle VC(1) to execute process S52. If the result of the root cause analysis in processes S52 to S54 is faulty, the map data DM can be updated. In particular, a result of the root cause analysis can be verified using a map defined by the map data DM for an anomaly that has occurred in various driving situations by different users after vehicles VC(1), VC(2), ... have been delivered.

[0094] Correspondences in elements between the claims and the embodiments are described below. The correspondence is described below in the order of description in the "Summary of the Invention". An example of a device for determining the cause of an abnormality is the one described in Fig. 1 or Fig. 8 control device 40 shown or the one in Fig. 11. Data analysis center 90 shown. An example of an electromagnetic actuator is the solenoid valve 28a. An example of an implementing device is the one in Fig. 1 and Fig. 8 shown CPU 42 or the ROM 44 or the in Fig. Figure 11 shows the CPU 92 and the ROM 94. An example of a storage device is the one in Fig. 1 and Fig. Storage device 46 shown in 8 or the one in Fig. Storage device 96 shown in Figure 11. An example of a current quantity is the current difference ΔI. If an example of the device for determining the cause of the abnormality is the control device 40, then an example of a detection process is the processes of S46 and S48. If an example of the device for determining the cause of the abnormality is the data analysis center 90, then an example of a detection process is the process of S100. An example of a calculation process is the process of S52. An example of a selection process is the process of S44. An example of a "variable indicating a current flowing in the solenoid valve when the same switching was performed in the past" is the current differences ΔI(-p+1), ΔI(-p+2), ..., and ΔI(-p+n). An example of a vehicle control device is the one shown in Figure 90. Fig. 1 and Fig. 8 Control device shown 40. An example of an abnormality detection process is process S24. An example of an alarming process is process S32. An example of a storage process is process S56. An example of first characteristic map data is the characteristic map data DM. An example of second characteristic map data is the symptom detection data DPD. An example of a second acquisition process is process S66. An example of a second calculation process is process S72. An example of a notification process is process S78. An example of a first execution device is the one in Fig.Figure 11 shows CPU 42 and ROM 44, and an example of a second execution device is CPU 92 and ROM 94. An example of a data transmission process is the process of S90. An example of a data transmission process is the process of S100. An example of a feedback process is the process of S104. An example of an update process is the process of S106. An example of a result transmission process is the process of S102, and an example of a result reception process is the process of S92.

[0095] The embodiments can be modified into other forms as follows. The embodiments and the following modified examples can be combined with one another, provided there is no technical conflict.

[0096] A selection method is described below. In the aforementioned embodiment, the accelerator pedal actuation amount ACCP is used as a torque variable, which is a variable that indicates a torque applied to the drive wheels 30, but the invention is not limited thereto. For example, a setpoint for a drive torque, determined from the accelerator pedal actuation amount ACCP, can be calculated, and the calculated setpoint for the torque can be used as a torque variable.

[0097] In the aforementioned embodiment, methods for selecting one of a plurality of characteristic map data parts DM(A1), DM(A2), ..., which differ due to a torque variable and a type of switching operation, and for using the selected characteristic map as characteristic map data used to calculate the values ​​of the output variables y(1), y(2), ..., have been described above, but the invention is not limited thereto. For example, a plurality of characteristic map data parts can be provided which differ due to a torque variable, irrespective of a type of switching operation, one of which is selected based on the torque variable and used as characteristic map data used to calculate the values ​​of the output variables y(1), y(2), ....For example, a variety of parts of characteristic map data can be provided that differ independently of a torque variable due to the nature of a switching operation, one of which can be selected based on the nature of the switching operation and used as characteristic map data to calculate the values ​​of the output variables y(1), y(2), ...

[0098] The selection process is not limited to selecting one part from a multitude of map data parts that differ from each other due to at least one of two variables, including the torque variable and the type of shift operation. For example, the selection process can be a process of selecting one part from a multitude of map data parts that differ from each other due to the oil temperature (T-oil). This can be achieved by providing a multitude of map data parts that differ from each other based on the oil temperature (T-oil), regardless of the torque magnitude and the type of shift operation.A variety of map data can be provided, differing due to at least one of the two variables of the torque variable and the type of shifting operation and the oil temperature T-oil, and thus one can be selected from a variety of map data.

[0099] The characteristic map data for each range is not limited to data for each range where the hydraulic pressure setpoint is set to different values. For example, a range where the hydraulic pressure setpoint is the same can be divided into several sub-ranges, and different characteristic map data segments can be provided for these sub-ranges. Since, in this case, the learning process only needs to be performed so that a characteristic map outputs a suitable value for an output variable, it is possible, for example, to accurately calculate the value of the output variable in a situation where the number of intermediate layers is small, or to accurately calculate the value of the output variable in a situation where the number of dimensions of the input variable is small.

[0100] It is not strictly necessary to construct the characteristic map data DM using a multitude of data parts for the ranges. In other words, it is not strictly necessary to perform the selection process. The setpoints are described below. In the aforementioned embodiments, the hydraulic pressure setpoint is determined based on the accelerator pedal actuation amount ACCP, the type of shift operation, and the oil temperature T-oil, but the invention is not limited thereto. For example, the hydraulic pressure setpoint for each partitioned sub-range can be determined based on only two of the three variables. For example, the hydraulic pressure setpoint for each partitioned sub-range can be determined based on only one of the three variables.

[0101] It is not strictly necessary to correct the hydraulic pressure setpoint through a learning process. The input variables for a characteristic map are described below. The current difference ΔI is shown as an example of a current variable that is an input variable to a characteristic map, which in the aforementioned embodiments is defined by the characteristic map data DM, but the invention is not limited thereto. For example, the current I can also be used. In this case, if, for example, the hydraulic pressure setpoint is not corrected in the learning process, as described above for the setpoint, and a multitude of parts of characteristic map data are provided that differ depending on the accelerator pedal actuation amount ACCP, the type of shift operation, and the oil temperature T-oil, as described above for the selection process, it is possible to accurately calculate the value of the output variable using the current I.When the current I is used, it is not necessarily required that the amount of change in the hydraulic pressure setpoint be small in a range where arbitrary map data is used.

[0102] In the aforementioned embodiments, the current variable, which is an input variable for a characteristic map defined by the characteristic map data DM, includes, in addition to the time series data in a switching period immediately before the occurrence of an abnormality, also time series data when the abnormality has occurred, but the invention is not limited thereto. For example, the current variable can contain time series data in a past switching period before the occurrence of an abnormality, in addition to the time series data at the time of the abnormality's occurrence. For example, the behavior variable can contain time series data in a plurality of switching periods before the occurrence of an abnormality, in addition to the time series data when the abnormality has occurred.

[0103] It is not strictly necessary for the current variable, which is an input variable for the characteristic map defined by the characteristic map data DM, to contain time series data in a switching period prior to the occurrence of an abnormality. The input variable to the characteristic map defined by the characteristic map data DM can contain the correction value ΔP.

[0104] The input variable of the characteristic map, defined by the characteristic map data DM, can include the deflection value ΔNm². The input variable of the characteristic map, defined by the characteristic map data DM, can include the oil temperature T-oil.

[0105] The input variable of the characteristic map, defined by the symptom detection data DPD, can include the oil temperature T-oil. The characteristic map data DM is described below. The neural network defined by the characteristic map data DM is not limited to a total-binding forward propagation neural network, but can, for example, be a recurrent neural network. The invention is not limited to a neural network, and, for example, a linear recurrent model can be used.

[0106] The symptom recognition data DPD are described below. The neural network defined by the symptom recognition data DPD is not limited to a total-binding forward propagation neural network and can, for example, be a recurrent neural network. The invention is not limited to a neural network and can, for example, use a linear recurrent model.

[0107] The alarm procedure is described below. In the aforementioned embodiment, the process of displaying visual information indicating that an abnormality has occurred, using the display 70 as an alarm device, has been described above, but the invention is not limited thereto. For example, a method for outputting acoustic information indicating that an abnormality has occurred, using a loudspeaker as an alarm device, can be employed.

[0108] The storage process is described below. In the aforementioned embodiment, the storage device that stores the result of the calculation of the output variables is set to the same device as the storage device that stores the characteristic map data DM, but the invention is not limited thereto.

[0109] Even if the values ​​of the output variables y(1), y(2), ... are calculated in the vehicle VC, it is not absolutely necessary to perform the storage operation. For example, instead of performing the storage process, a process to transmit the calculation result to a manufacturer of the vehicle VC, the data analysis center 90, or similar entity can be carried out.

[0110] The notification process is described below. In the aforementioned embodiments, when a symptom of an abnormality is detected, the data analysis center 90, which contains a big data database with stored data from a large number of vehicles VC(1), VC(2), ..., is notified, but the invention is not limited to this. For example, if the data analysis center 90 is different from a manufacturer of the vehicles VC(1), VC(2), ..., the manufacturer can be notified. For example, a dealer of vehicle VC(1) can be notified.

[0111] The applications of the output variables are described below. (a) Applications of the output variables y(1), y(2), ... are described. In the aforementioned embodiments, the values ​​of the output variables y(1), y(2), ... are used to determine whether a component should be replaced when a vehicle VC is taken to a repair shop, but the invention is not limited thereto. For example, the manufacturer of the vehicle VC can use the values ​​of the output variables as feedback information for product improvement. (b) Applications of the output variables based on actual waveforms at the time of dither control are described below. In the aforementioned embodiments, the output variables z(1), z(2), ... are used to detect a symptom of an abnormality, but the invention is not limited thereto. For example, by including full adhesion in the output variables z(1), z(2), ..., the process of S30 or the process of S32 can be carried out when the maximum value zmax is equal to the value of the output variable corresponding to full adhesion. The invention is not limited to these methods, and, for example, the method of S34 can be carried out.

[0112] The following describes a vehicle control system. In the third embodiment, the values ​​of the output variables y(1), y(2), ... and the values ​​of the output variables z(1), z(2), ... are calculated by the data analysis center 90, but the invention is not limited to this. For example, the values ​​of the output variables y(1), y(2), ... can be calculated by the data analysis center 90 and the values ​​of the output variables z(1), z(2), ... can be calculated by the vehicle.

[0113] In the aforementioned embodiment, the values ​​of the output variables y(1), y(2), ... and z(1), z(2), ... are calculated by the data analysis center 90 for the purpose of updating the characteristic map data DM, but the invention is not limited thereto. For example, it is also possible to reduce the computational load of the CPU 42 even when the characteristic map data DM is not updated, by having the exterior of the vehicle VC calculate the values ​​of the output variables y(1), y(2), ... and z(1), z(2), ....

[0114] The data based on a measured value, which is sent to the Data Analysis Center 90, is not limited to data that serve as the input variables x(1), x(2), ..., such as the current difference ΔI. For example, the current I can be used. In this case, the Data Analysis Center 90 can calculate the current difference ΔI by sending the values ​​of the variables required for calculating the current setpoint I*, such as the accelerator pedal actuation amount ACCP, the type of shift operation, and the oil temperature T-oil, to the Data Analysis Center 90.

[0115] The destination to which the vehicle VC sends data based on sensor readings required to calculate the values ​​of the output variables y(1), y(2), ... is not limited to the unit performing the calculation process. For example, a data center storing a big data database and an analytics center calculating the values ​​of the output variables y(1), y(2), ... could be separate entities, and data based on sensor readings could be sent from the vehicle VC to the data center. In this case, the data center could then forward the received data and other information to the analytics center.

[0116] The destination to which the vehicle VC sends the data based on sensor readings required to calculate the values ​​of the output variables y(1), y(2), ... is not limited to the unit that processes data from a multitude of vehicles VC(1), VC(2), .... The destination could, for example, be a mobile device belonging to a user of the vehicle VC.

[0117] The destination to which the vehicle VC sends the data based on sensor readings required to calculate the value of the output variables z(1), z(2), ... is not limited to the unit that processes data from a multitude of vehicles VC(1), VC(2), .... The destination could, for example, be a mobile device belonging to a user of the vehicle VC.

[0118] An execution device is described below. The execution device is not limited to an execution device comprising the CPU 42 (92) and the ROM 44 (94) that executes software processes. For example, a dedicated hardware circuit, such as an application-specific integrated circuit (ASIC), which executes at least some of the software processes executed in the aforementioned embodiments, may be provided in the hardware. That is to say, the execution device may have at least one of the following configurations (a) to (c). (a) A processor that executes all processes according to a program and a program storage device, such as a ROM, that stores the program are provided. (b) A processor that executes some of the processes according to a program, a program storage device, and a dedicated hardware circuit that executes the other processes are provided.(c) A dedicated hardware circuit is provided to execute all processes. Here, the number of software processing circuits, including a processor and a program storage device, or the number of dedicated hardware circuits may be two or more.

[0119] An electromagnetic actuator is described below. The electromagnetic actuator 28 is not limited to the electromagnetic actuator 28 of the gearshift device 26. An onboard electromagnetic actuator, other than the gearshift device 26, can effectively use the characteristic map to identify details of the location of an abnormality.

[0120] The following describes a vehicle. The vehicle is not limited to a series / parallel hybrid vehicle. For example, the vehicle can be a series hybrid vehicle or a parallel hybrid vehicle. The onboard propulsion system is not limited to a propulsion system with an internal combustion engine and a motor-generator. For example, a vehicle with an internal combustion engine but no motor-generator can be used, or a vehicle with a motor-generator but no internal combustion engine can be used.

Claims

[1] Device (40; 90) for determining the cause of an abnormality applied to a vehicle (VC; VC(1)) with an electromagnetic actuator (28), comprising: a storage device (46; 46, 96) configured to store characteristic map data, which are data for defining a characteristic map, wherein the characteristic map has a current variable (ΔI), which is a variable indicating a current actually flowing in the electromagnetic actuator (28), as an input variable, and a cause variable, which is a variable indicating the cause of an abnormality of an on-board unit having the electromagnetic actuator (28), as an output variable; and an implementing device (42, 44; 92, 94) which is configured to perform a detection process (S46, S48; S100) for detecting a value of the input variable based on a detection value from a sensor installed in the vehicle (VC; VC(1)) and a calculation process (S52) for calculating a value of the output variable by inputting the value of the input variable into the map, wherein the on-board unit is a gear-shifting device (26) that changes a gear ratio between a rotational speed of a drive shaft of a drive machine (VC; VC(1)) installed in the vehicle and a rotational speed of drive wheels; the electromagnetic actuator (28) comprises a solenoid valve (28a) of the gearshift device (26); and the current variable (ΔI), which is the input variable, contains a variable that indicates a current flowing in the solenoid valve (28a) during one switching period of the gear ratio in the gear shifting device (26). [2] Device for determining the cause of an abnormality (40; 90) according to claim 1, wherein the current variable (ΔI) contains a variable that specifies a difference between the detection value of the current flowing in the solenoid valve (28a) during the switching period and a current setpoint. [3] Device (40; 90) for determining the cause of an abnormality according to claim 1 or 2, wherein: the storage device (46; 46, 96) is configured to store a plurality of parts of characteristic map data that differ depending on a type of switching of the transmission ratio; and The calculation process includes a selection process (S44) for selecting the characteristic map data corresponding to the switching of the transformation ratio in a sampling period of the current variable (ΔI), which is the input variable, from the multitude of parts of the characteristic map data as the characteristic map data that define the characteristic map for calculating the value of the output variable. [4] Device (40; 90) for determining the cause of an abnormality according to any one of claims 1 to 3, wherein: the storage device (46; 46, 96) is configured to store a plurality of parts of characteristic map data which differ depending on a torque variable, which is a variable that indicates a torque applied to the drive wheels; the acquisition process includes a process for acquiring a value of the torque variable; and The calculation process includes a selection process (S44) for selecting the map data corresponding to the value of the torque variable acquired by the acquisition process from the multitude of map data parts as the map data that define the map for calculating the value of the output variable. [5] Device for determining the cause of an abnormality (40; 90) according to any one of claims 1 to 4, wherein the current variable (ΔI), which is the input variable that is simultaneously entered into the characteristic map, contains a variable (ΔI(-p+1), ΔI(-p+2), ..., ΔI(-p+n)) indicating a current that flowed in the solenoid valve (28a) when the same switching was performed in the past, in addition to the variable indicating the current flowing in the solenoid valve (28a) in a present switching period of the gear ratio in the gear-shifting device (26). [6] Device for determining the cause of an abnormality (40; 90) according to one of claims 1 to 5, wherein the cause variable comprises a decrease in the controllability of the solenoid valve (28a) due to bubbles contained in a hydraulic fluid of the gearshift device (26), a temporary sticking abnormality, which is an abnormality that occurs temporarily in an operation of the solenoid valve (28a) due to a temporary mixing of foreign substances in the solenoid valve (28a), and a regular sticking abnormality, which is an abnormality that occurs regularly in the operation of the solenoid valve (28a) due to a mixing of foreign substances in the solenoid valve (28a). [7] Vehicle control device (40) comprising the device for determining the cause of an abnormality according to any one of claims 1 to 6, wherein: the execution device (42, 44; 92, 94) is set up to carry out: an abnormality detection process to determine that an abnormality has occurred in the gearshift device (26) when the extent of the difference between a rotational speed of an input shaft of the gearshift device (26) during a period in which the gear ratio is switched and a reference rotational speed is equal to or greater than a predetermined value, and an alerting process to issue an alarm indicating that an abnormality has occurred; and The acquisition process includes a process for capturing the value of the input variables during the period in which the translation ratio is switched, if the abnormality detection process determines that an abnormality has occurred. [8] Vehicle control device (40) according to claim 7, wherein the implementing device (42, 44; 92, 94) is configured to perform a storage process for storing a calculation result of the calculation process in the storage device (46; 46, 96). [9] Vehicle control device (40) comprising the device for determining the cause of an abnormality according to any one of claims 1 to 6, wherein: the on-board unit is a gear-shifting device (26) that changes a gear ratio between a rotational speed of a drive shaft of a drive machine (VC; VC(1)) installed in the vehicle and a rotational speed of drive wheels; the electromagnetic actuator (28) has a solenoid valve (28a); the map is a first map; the characteristic map data are the first characteristic map data; the data collection process is a first data collection process; the calculation process is a first calculation process; the implementing device (42, 44; 92, 94) is configured to perform a dither control process which causes a current to flow in the solenoid valve (28a) so that the solenoid valve (28a) vibrates in a range in which the friction engagement element is not engaged, in order to switch a friction engagement element that is disengaged when the transmission ratio does not switch between disengage and engage; the storage device (46; 46, 96) is configured to store second characteristic map data for defining a second characteristic map which includes the current variable (ΔI) as an input variable when the dither control process is performed and an abnormality variable, which is a variable indicating whether an abnormality has occurred in the solenoid valve (28a), as an output variable; and the execution device (42, 44; 92, 94) is set up to carry out: a second acquisition process (S66) to acquire a value of the current variable (ΔI) when the dither control process is performed, and a second calculation process (S72) which calculates a value of the output variable by entering the value of the current variable (ΔI) acquired in the second acquisition process (S66) into the second characteristic field. [10] Vehicle control device (40) according to claim 9, wherein the implementing device (42, 44; 92, 94) is configured to perform a notification process (S78) to notify an exterior of the vehicle (VC; VC(1)) of a calculation result of the second calculation process (S72). [11] Vehicle control system with the vehicle control device (40) according to any one of claims 7 to 10, wherein: the implementing device (42, 44; 92, 94) comprises a first implementing device (42, 44) which is provided in the vehicle (VC; VC(1)) and a second implementing device (92, 94) which is not provided in the vehicle (VC; VC(1)); the first implementing device (42, 44) is configured to perform a data transmission process (S90) for transmitting data based on a detection value from the sensor, which is connected to a current actually flowing in the electromagnetic actuator (28); and the second execution device (92, 94) is configured to perform a data reception process (S100) to receive data that was transmitted in the data transmission process (S90) and the computation process. [12] Vehicle control system according to claim 11, wherein: the second implementing device (92, 94) is configured to carry out the calculation process based on values ​​of the current variables (ΔI) of a plurality of vehicles (VD; VC(1)); and the second execution device (92, 94) is configured to perform a feedback process (S104) and an update process (S106), wherein the feedback process is a process for acquiring information indicating that the value of the output variable of the calculation process is not valid, and the update process (S106) is a process for updating the characteristic map data when information indicating that the value of the output variable is not valid is acquired in the feedback process. [13] Vehicle control system according to claim 11 or 12, wherein: the second execution device (92, 94) is configured to perform a result transmission process (S102) for transmitting a calculation result of the calculation process; and the first execution device (42, 44) is set up to perform a result reception process (S92) to receive the calculation result transmitted in the result transmission process (S102). [14] Vehicle control device (40) with the first embodiment device (42, 44) in the vehicle control system according to one of claims 11 to 13.

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