Vehicle deterioration prediction system and vehicle deterioration prediction method

Abstract

A deterioration prediction device for a vehicle includes: a grouping unit that performs grouping using data relating to traveling conditions of a plurality of vehicles as an input; a determination unit that determines which group the vehicle to be diagnosed belongs to; a deterioration prediction information generation unit that generates deterioration prediction information using data of the plurality of vehicles belonging to the group determined by the determination unit; and a diagnosis unit that determines and diagnoses whether or not the vehicle is malfunctioning by inputting the vehicle condition of the subject to be diagnosed into the deterioration prediction information.

Description

Technical Field

[0001] The present invention relates to a vehicle degradation prediction system and a vehicle degradation prediction method. Background Art

[0002] The reliability of in-vehicle devices installed in vehicles such as automobiles is guaranteed by passing the specified durability tests set by each vehicle manufacturer. However, in general, in order to fully ensure the durability of the vehicle, the reliability of in-vehicle devices is improved, and when observing each device, there is sometimes excess quality.

[0003] Excessive mass in on-board devices increases manufacturing costs, leading to higher vehicle prices, which is undesirable. However, even a single failure in an on-board device can cause a vehicle malfunction and hinder driving, so reducing the durability and reliability of the on-board devices is undesirable. Therefore, even if individual components have excessive mass, rationalization of mass is not pursued.

[0004] Typically, because vehicle usage varies from one user to another, the distance traveled before a failure also varies from vehicle to vehicle, making it difficult to predict the likelihood of failure for each vehicle. For example, while fragile parts like tires and batteries are recommended to be replaced after a certain driving distance, in-vehicle devices like electronic components are typically replaced after a failure occurs, making it difficult to predict failure in advance.

[0005] Patent Document 1 describes a technique for collecting and grouping data related to vehicle driving states, and when a vehicle failure occurs, extracting vehicles belonging to the group to which the failed vehicle belongs as vehicles indicating a possible failure. Prior art literature Patent Literature

[0006] Patent Document 1: Japanese Patent Application Laid-Open No. 2022-145169 Summary of the Invention Problems to be solved by the invention

[0007] According to the technology described in Patent Document 1, vehicles are grouped based on data related to their driving conditions. When a failure occurs in one vehicle in a specific group, it can be assumed that other vehicles in the same group are also showing signs of failure. However, in the technology described in Patent Document 1, if any vehicle in a group fails, all vehicles in the same group are classified as vehicles showing a sign of failure. Therefore, it cannot be said that vehicle life prediction is performed appropriately.

[0008] If the lifespan of each onboard device is taken into consideration and the signs of failure in each vehicle can be appropriately estimated, the corresponding components can be replaced before a failure occurs, thus preventing excess mass in onboard devices. However, such high-precision failure sign detection has been difficult in the past.

[0009] An object of the present invention is to provide a vehicle degradation prediction system and a vehicle degradation prediction method capable of performing highly accurate degradation prediction. Technical means to solve the problem

[0010] In order to solve the above-mentioned problems, for example, the configuration described in the claims is adopted. The present application includes multiple units for solving the above-mentioned problems. For example, a degradation prediction system for a vehicle includes: a grouping unit, which performs grouping by taking data related to the driving conditions of multiple vehicles as input; a belonging group determination unit, which determines to which group the vehicle of the diagnosis object belongs; a degradation prediction information generation unit, which uses data of multiple vehicles belonging to the group determined by the belonging group determination unit to generate degradation prediction information; and a fault judgment unit, which determines whether the diagnosed vehicle has a fault by inputting the vehicle condition of the diagnosis object into the degradation prediction information. Effects of the Invention

[0011] According to the present invention, since degradation prediction is performed using data of a plurality of vehicles belonging to a determination group, it is possible to improve the accuracy of diagnosing whether a vehicle has a fault. Other problems, structures, and effects than those described above will become clear from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a diagram showing an example of the overall configuration of a vehicle degradation prediction system according to an embodiment of the present invention. Figure 2 This is a diagram showing the configuration of an example of a vehicle to which the diagnosis of the degradation prediction system according to one embodiment of the present invention is applied. Figure 3 This is a block diagram showing an example of a detailed configuration of a vehicle degradation prediction system according to an embodiment of the present invention. Figure 4 This is a flowchart showing an example of the flow of degradation prediction in a data center of a vehicle degradation prediction system according to an embodiment of the present invention. Figure 5 This is a flowchart showing an example of processing in a target vehicle when a degradation prediction system for a vehicle according to an embodiment of the present invention performs degradation prediction. Figure 6 This is a flowchart showing an example of notification processing of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 7 This is a diagram showing an example of grouping in a vehicle degradation prediction system according to an embodiment of the present invention. Figure 8 This is a diagram showing an example of processing performed by a degradation prediction unit of a vehicle degradation prediction system according to an embodiment of the present invention. Figure 9 This is a diagram showing an example of specific processing data of a degradation prediction unit of a vehicle degradation prediction system according to an embodiment of the present invention. Figure 10 This is a diagram showing an example of the relationship between a degradation prediction value and a travel distance in a vehicle degradation prediction system according to an embodiment of the present invention. Figure 11 This is a diagram showing an example of the relationship between a degradation prediction value and time in a vehicle degradation prediction system according to an embodiment of the present invention. Figure 12 This is a diagram showing an example of a histogram of the history of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 13 This is a diagram showing an example of properties of fuel according to an embodiment of the present invention. Figure 14 This is a block diagram showing a modified example (an example of selecting a minimum value of a device) of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 15 This is a block diagram showing a modified example (an example of selecting the minimum value of a component) of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 16 This is a diagram showing a modified example (an example using information on vehicles that have undergone reliability tests) of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 17 This is a diagram showing a modified example (an example using information on a faulty vehicle) of the vehicle degradation prediction system according to one embodiment of the present invention. Figure 18 This is a diagram showing a modified example of the vehicle degradation prediction system according to one embodiment of the present invention (an example using information on vehicles that have undergone reliability tests and information on vehicles with faults). DETAILED DESCRIPTION

[0013] Hereinafter, a vehicle degradation prediction system and a vehicle degradation prediction method according to an embodiment example of the present invention (hereinafter referred to as “this embodiment”) will be described with reference to the drawings.

[0014] [Overall Structure of Vehicle Deterioration Prediction System] Figure 1 An example of the overall configuration of the vehicle degradation prediction system of this example is shown. The vehicle degradation prediction system of this example includes a data center 100. Data center 100 collects information about multiple vehicles 11, 12, 13, 14, etc. from a real environment 10 via a network NW1. Furthermore, data center 100 collects information from an energy station 21, a base station 22, and a manufacturer 23 (vehicle manufacturer, device manufacturer) via network NW1. In addition, Figure 1 In the example, one vehicle 11 among the plurality of vehicles 11 to 14 is regarded as a target vehicle expected to be deteriorated.

[0015] The data center 100 collects detailed information on components indicating what type of vehicle-mounted devices are installed, sensor information of the components, and travel information related to travel conditions, etc., from each of the vehicles 11 to 14 . Furthermore, the data center 100 collects, from the energy station 21 , information related to energy, such as the properties of gasoline or light oil, the properties of fuel hydrogen, and the power supply method. In addition, the data center 100 collects surrounding environment information from the base station 22 and device production information from the manufacturer 23 .

[0016] The data center 100 includes an information collecting unit 101 , a vehicle information matching unit 102 , a grouping unit 103 , a belonging group determining unit 104 , a degradation prediction information generating unit 105 , and a transmitting unit 106 . The information collection unit 101 collects information on components (onboard devices), sensor information, and driving information of each vehicle 11 , 12 , 13 , 14 , etc. from the actual environment 10 . The information collection unit 101 also collects information from the energy station 21 , base station 22 , and manufacturer 23 .

[0017] The vehicle information matching unit 102 matches the vehicle information based on the individual information of each vehicle 11 , 12 , . . . , the manufacturing number of the vehicle-mounted device, the surrounding environment, the travel distance, the energy acquisition status, and the like. The grouping unit 103 groups the vehicles 11 , 12 , . . . based on the matching information in the vehicle information matching unit 102 . The belonging group determination unit 104 performs a process of determining the group to which the determination target vehicle (here, the vehicle 11 ) belongs.

[0018] The degradation prediction information generating unit 105 generates degradation prediction information based on information about vehicles belonging to the group to which the target vehicle 11 belongs. The degradation prediction information generating unit 105 may also acquire a failure determination value based on the generated degradation prediction information. The transmission unit 106 transmits the degradation prediction information of the target vehicle 11 generated by the degradation prediction information generation unit 105 to the target vehicle 11. When the transmission unit 106 obtains a failure determination value, it transmits the failure determination value to the target vehicle 11.

[0019] The target vehicle 11 includes a receiving unit 201 , a degradation prediction calculation unit 202 , and a failure determination unit 203 . The receiving unit 201 receives the degradation prediction information from the data center 100. When a failure determination value is transmitted, the receiving unit 201 receives the transmitted failure determination value. The degradation prediction calculation unit 202 substitutes the information of the vehicle 11 into the received degradation prediction information to calculate a degradation prediction value.

[0020] The fault diagnosis unit 203 compares the degradation prediction value calculated by the degradation prediction calculation unit 202 with a predetermined threshold value and determines the possibility of a fault based on the comparison result. The fault diagnosis unit 203 notifies the user of the result of the determination through a display unit (not shown) within the target vehicle 11. Alternatively, the fault diagnosis result may be transmitted to the data center 100 or the manufacturer. Furthermore, the target vehicle 11 is configured to transmit information required for degradation prediction and information required for grouping to the data center 100 .

[0021] [Example of vehicle configuration] Figure 2 A configuration example of the vehicle 11 is shown. The vehicle 11 supplies power from a battery 301 to an inverter 303 via a power cable 302 , and supplies the power converted by the inverter 303 to a motor 304 . The motor 304 is driven by the power supply, and rotates a drive shaft 306 via a transmission 305 . Furthermore, the vehicle 11 is provided with an engine 309 , and the drive shaft 306 can be rotated via the transmission 305 by driving the engine 309 . Furthermore, the vehicle 11 is provided with a steering device 307 and a brake 308 . In addition, each part of the vehicle 11 is controlled by the vehicle-mounted device 310 .

[0022] Figure 3 1 shows an example of the configuration of a vehicle-mounted device mounted on the vehicle 11. In particular, Figure 3 In the example of FIG, the configuration of the control device of the vehicle 11 is shown. The vehicle 11 includes a plurality of controllers. Specifically, the vehicle 11 includes a vehicle integrated control controller 310 and controllers 321 to 326 for each device that are communicably connected to the vehicle integrated control controller 310 via an in-vehicle network.

[0023] The vehicle integrated control controller 310 is configured as a computer including, for example, a CPU (Central Processing Unit) 311 , a memory 312 , and an interface 313 . The CPU 311 is a processor that executes a program stored in the memory 312 and constitutes a processing function unit in a work area of ​​the memory 312 to perform vehicle control processing. Figure 1 The receiving unit 201 , the degradation prediction calculation unit 202 , and the failure determination unit 203 described above are also configured in the working area of ​​the memory 312 under the control of the CPU 311 . The interface 313 exchanges information with other controllers 321 to 326 in the vehicle. Figure 1 The network NW2 shown exchanges information with the data center 100 .

[0024] Controllers 321 to 326 of each device will be described. Controller 321 controls engine 306. Controller 322 controls transmission 305. Controller 323 controls battery 301. Controller 324 controls inverter 303 and motor 304. Controller 325 controls steering device 307. Controller 326 controls brake 308.

[0025] Similar to vehicle integrated control controller 310, these controllers 321-326 are configured as computers equipped with a CPU, memory, and interfaces. Each controller 321-326 controls the devices connected to it based on instructions from vehicle integrated control controller 310. Furthermore, each controller 321-326 transmits information regarding the status of each device to vehicle integrated control controller 310. The vehicle integrated control controller 310 calculates the drivable distance of the vehicle-mounted device. Alternatively, the controllers 321 to 326 that control each device calculate the drivable distance of the connected device.

[0026] The configuration in which the vehicle integrated control controller 310 and each controller 321 to 326 perform calculations based on installed programs is merely an example, and other configurations are also possible. For example, a portion or all of the vehicle integrated control controller 310 may be implemented using dedicated hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). In addition, for Figure 1The data center 100 shown is also configured by a computer similar to the vehicle integrated control controller 310 or other dedicated hardware.

[0027] [Processing in Data Centers] Figure 4 This is a flowchart showing an example of the flow of degradation prediction processing in the data center 100 constituting the degradation prediction system of this example. First, the information collection unit 101 of the data center 100 performs a process of collecting information related to the vehicle (step S11 ). Next, the vehicle matching unit 102 performs a matching process for the vehicle information (step S12). Here, the vehicle matching unit 102 performs a one-to-one matching process between the vehicle and the device mounted on the vehicle. For example, the vehicle matching unit 102 uses the vehicle manufacturing number or the device manufacturing number to retrieve the factory inspection results obtained from the vehicle manufacturer or the device manufacturer when the device was manufactured, and then matches the vehicle with the device.

[0028] Furthermore, the vehicle matching unit 102 uses the position information (including history) of the individual vehicle to determine the environment (external air, humidity, and air pressure) in which the individual vehicle is used. This surrounding environment information is obtained from the base station 22, for example.

[0029] Next, the grouping unit 103 performs a process of grouping vehicles based on the matching information matched by the vehicle information matching unit 102 (step S13 ). Then, the belonging group determination unit 104 determines the target vehicle (here Figure 1 The group to which the vehicle 11 shown in FIG. 1 belongs (step S14 ). In step S14, when the group determination unit 104 determines the target vehicle's group, the degradation prediction information generation unit 105 generates degradation prediction information based on information about vehicles belonging to the group to which the target vehicle 11 belongs (step S15). The degradation prediction information is information necessary for calculating the degradation prediction of the target vehicle 11. The degradation prediction information generation unit 105 may also obtain a failure determination value through calculations based on the generated degradation prediction information.

[0030] Then, the transmitter 106 transmits the degradation prediction information generated by the degradation prediction information generator 105 to the target vehicle 11 (step S16 ). If the degradation prediction information generator 105 obtains a failure determination value, the transmitter 106 also transmits the failure determination value to the target vehicle 11 .

[0031] [Handling in Vehicle] Figure 5 This is a flowchart showing an example of the flow of processing in the target vehicle 11 constituting the degradation prediction system of this example. First, the target vehicle 11 transmits vehicle information required for grouping vehicles to the data center 100 (step S21 ). Next, the target vehicle 11 determines whether or not the degradation prediction information of the subject vehicle needs to be updated (step S22 ).

[0032] If the degradation prediction information of the host vehicle needs to be updated in step S22 (YES in step S22 ), the receiving unit 201 receives the degradation prediction information of the group to which the host vehicle belongs from the data center 100 (step S23 ). Then, after receiving the degradation prediction information in step S23, or when the degradation prediction information of the vehicle does not need to be updated in step S22 ("No" in step S22), the degradation prediction calculation unit 202 inputs the vehicle information of the vehicle into the degradation prediction information and calculates the degradation prediction value (step S24).

[0033] When the calculation of the degradation prediction value in step S24 is completed, the degradation prediction calculation unit 202 determines whether the obtained degradation prediction value is greater than a predetermined threshold value (step S25). Here, the degradation threshold value is set in advance based on design information in the development stage. If the predicted degradation value is equal to or greater than the threshold value in step S25 (YES in step S25 ), the failure determination unit 203 determines that a device mounted on the vehicle has failed, and notifies the user of the failure of the device (step S27 ).

[0034] If the predicted degradation value is not equal to or greater than the threshold value in step S25 (No in step S25 ), the failure determination unit 203 calculates the drivable distance until the vehicle fails and notifies the user (step S26 ). The notification in steps S26 and S27 is performed, for example, by displaying or outputting sound on a display unit mounted on the vehicle. Alternatively, the notification may be performed by sending a message to a mobile terminal held by the user of the vehicle 11. Alternatively, the calculation result may be sent to the data center 100.

[0035] In addition, Figure 5 In the process described in the flowchart of FIG, in step S26, the failure determination unit 203 calculates the drivable distance until the vehicle fails and notifies the user. In contrast, Figure 6As shown in the flowchart, after calculating the drivable distance until the vehicle breaks down in step S26, the drivable time until the vehicle breaks down may be calculated by the fault diagnosis unit 203 in step S28, and the drivable time (number of drivable days) may be notified. The drivable time may be calculated, for example, by converting the drivable distance into drivable time using a moving average of the drivable distances of the target vehicle 11 at regular intervals (e.g., 30 days). Figure 6 The other steps are Figure 5 The flowchart is the same as By providing notification in the form of time in this manner, it is possible to provide the user with an easily understandable notification.

[0036] [Grouping example] Figure 7 1 and 2 are diagrams showing an example of grouping performed by the grouping unit 103 . Figure 7 The vehicle information x1 to x2 acquired by the data center 100 is obtained. n x i and x j The distribution diagram of the cross section. Figure 7 The distribution diagram on the left is an example before grouping. Figure 7 The right side of is the grouped example. Figure 7 In the figure, the characteristics indicated by the circle mark represent the characteristics of the target vehicle 11, and the distribution characteristics indicated by the solid mark represent the distribution characteristics of vehicles equipped with the same on-board device as the target vehicle 11. Furthermore, the distribution characteristics indicated by the triangle mark represent the distribution characteristics of vehicles that have experienced a failure within the distribution characteristics of vehicles equipped with the same on-board device as the target vehicle 11. The vehicles indicated by the distribution characteristics here may also include scrapped vehicles or vehicles whose on-board devices have been replaced.

[0037] Here is the vehicle information x1~x n Use the following information as variables. Sensor information (voltage, current, etc.) from onboard devices installed in the vehicle Driving history (driving location, driving distance, etc.) Surrounding environment information (temperature, humidity, air pressure, road conditions, elevation, etc.) Energy station information (charger type, power supply, gasoline properties, hydrogen fuel properties, etc.)

[0038] The grouping unit 103 is based on the vehicle information x1 to x n The values ​​of . Figure 7 In the example, the three groups are group Gr.01 to group Gr.03. Figure 7In the figure, the grouping is performed from the perspective of two pieces of information xi and xj in the plurality of vehicle information x1 to xn. However, in reality, the grouping unit 103 considers all pieces of information x1 to xn. n to group. When the grouping unit 103 performs grouping, for example, classification is performed based on a rule. Alternatively, the grouping unit 103 may perform grouping using unsupervised machine learning or the like.

[0039] [Example of degradation prediction information] Figure 8 An example of the generation process of the degradation prediction information in the degradation prediction information generation unit 105 is shown. The degradation prediction information generating unit 105 obtains Figure 8 The distribution shown on the left side of Figure 7 When the same distribution is used, among the vehicles in the same group as the target vehicle 11, the devices or components mounted on the vehicle that experienced the failure are investigated, and the information x1 to x2 of the vehicle that experienced the failure is used. n Obtain degradation prediction information d p However, using the information x1 to x2 of the vehicle that experienced the failure n This is an example, and information x1 to x2 of other vehicles in the same group as the target vehicle 11 may also be used. n . Information about the vehicle x1~x n For example, in the following Figure 9 is described in .

[0040] In addition, if Figure 8 As shown in the table on the right side of , the information x1 to x2 of the vehicles that have experienced vehicle failure in the group are n and degradation prediction information d p In addition, information on vehicles and on-board devices that have undergone reliability tests performed during the vehicle development phase may be combined. Details of using information on vehicles and on-board devices that have undergone reliability tests performed during the vehicle development phase will be described later.

[0041] Figure 9 Degradation prediction information d p and each variable x1~x n Examples of relationships. Figure 9 The information shown in the table is variables x1 to x n For example, variables x1 to x n This includes voltage, current, temperature, number of charges, and number of fast charges as device (component) sensor information. In addition, variables x1 to x nThe vehicle information includes the product inspection results (device deviation: upper, middle, and lower performance), vehicle type, year, driving distance, and location.

[0042] In addition, variables x1 to x n It contains the temperature, humidity, air pressure, elevation, and road conditions as the surrounding environment information. In addition, variables x1 to x n The energy station information includes the type of charger, power supply method, gasoline properties, and fuel hydrogen properties. In addition, although Figure 9 Although not shown in the figure, it also includes design information and durability test information. In addition, not only faulty vehicles but also the use history information of scrapped vehicles can be used.

[0043] In addition, the information obtained here is such as Figure 12 As shown in , the information of the time history can also be used as a histogram, and the time and number of times included in a certain range can be used as variables. Figure 12 In the example, the voltage history (horizontal axis) and frequency (vertical axis) up to now are shown. Figure 12 As shown, the voltage history can also be divided into levels ranging from 1 to 10, and the frequency (time) of entering each range 1 to 10 and the information x1 to x2 can be set. n .

[0044] [Calculation Process of Drivable Distance and Drivable Time] Figure 10 and Figure 11 An example is shown in which the degradation prediction calculation unit 202 of the vehicle 11 calculates the drivable distance and the drivable time. Figure 10 The vertical axis shows the travel distance Dd of the vehicle 11 and the degradation prediction information d p Here, the degradation prediction calculation unit 202 is the degradation prediction information d p In the case of an increase in proportion to the driving distance Dd, the degradation prediction information d p At the threshold dp -limit If the above is true, it is judged as a fault.

[0045] exist Figure 10 In the example, when the travel distance Dd is the distance value Dd p When , it is the degradation prediction value dp -p , the degradation prediction calculation unit 202 determines that it is normal. On the other hand, -limit The corresponding driving distance Dd -limit In the above case, the fault determination unit 203 determines that the target vehicle 11 is faulty. In addition, when it is determined to be normal, the degradation prediction calculation unit 202 calculates the current distance Dd by performing p -Threshold driving distance Dd -limit ] calculation, the drivable distance until the failure can be calculated.

[0046] Figure 11 The time Td (vertical axis) and the degradation prediction information d of the vehicle 11 are shown. p Here, the fault judgment unit 203 is a case where the degradation prediction information dp increases in proportion to the time Td. At the threshold dp of the degradation prediction information dp, -limit If the above is true, it is judged as a fault.

[0047] exist Figure 11 In the example, when time Tp is value T p When , it is the degradation prediction value dp -p , the degradation prediction calculation unit 202 determines that it is normal. On the other hand, -limit The corresponding time T limit In the above case, the fault determination unit 203 determines that the target vehicle 11 is faulty. In addition, when it is determined to be normal, the degradation prediction calculation unit 202 calculates [current time Tp-threshold time T limit ] calculation, the drivable distance and time until the failure can be calculated.

[0048] The drivable time T can be calculated by dividing the drivable distance Dd of the target vehicle 11 by the drivable distance within a certain period of time. In addition, Figure 10 and Figure 11 In the example of FIG, the degradation prediction value dp is shown as increasing linearly, but this linear increase is an example and is not limited to this. However, the degradation prediction value dp can be represented by a monotonic increase or a monotonic phenomenon.

[0049] [Examples of gasoline properties] Figure 13 Represents variables x1 to x n Examples of gasoline properties used. Gasoline properties include color, density, distillation properties, octane number, vapor pressure, sulfur content, benzene content, etc. The deterioration prediction information generation unit 105 obtains information on these gasoline properties based on the gasoline manufacturer, the time (season) when the gasoline is used, etc., and generates them as variables x1 to x2. n use. Depending on the fuel components such as gasoline properties, the corrosion state of resin parts or metal parts of hoses may vary, and the predicted degradation value may vary.

[0050] According to the degradation prediction system of this embodiment described above, since degradation prediction is performed using data of a plurality of vehicles belonging to a determination group, it is possible to diagnose whether the target vehicle has a fault. In particular, when grouping, the grouping unit 103 uses variables x1 to x2 as n By collecting various information about the vehicle, the degradation prediction information generating unit 105 can significantly improve the accuracy of the diagnosis. In addition, the degradation prediction information generating unit 105 can also diagnose the extent of the driving distance or time at which the failure occurs. Furthermore, when obtaining the degradation prediction information dp, the degradation prediction information generating unit 105 can perform a more accurate diagnosis by using only information on the failed vehicle.

[0051] [Example of drivable distance using multiple in-vehicle devices] Furthermore, when the travelable distances are calculated for each of a plurality of devices mounted on one vehicle, the calculated travelable distances may be used as the respective travelable distances. Figure 14 This is a block diagram showing a modified example of the vehicle degradation prediction system (an example of selecting the minimum value of the device). The target vehicle 11 is equipped with a first vehicle-mounted device 351 , a second vehicle-mounted device 352 , . . . , and an N-th vehicle-mounted device 359 (N is an integer equal to or greater than 2), and is provided with controllers 361 to 369 , respectively.

[0052] At this time, the minimum value selection unit 310a of the vehicle integrated control unit 310 selects the minimum value of the drivable distances calculated by each controller 361-369 (here, 50,000 km for the first onboard device 351). The drivable distance presentation unit 310b then presents the minimum drivable distance value. Furthermore, when presenting the minimum drivable distance value, the drivable distance presentation unit 310b may also present information about which device has the minimum value.

[0053] This can avoid a situation where the target vehicle experiences driving problems while the target vehicle still has a remaining travelable distance. In addition, the drivable distance prompt unit 310b calculates the drivable distance of each device (inverter, motor, battery, etc.) and selects the device with the minimum value. This allows the corresponding device to be repaired, replaced, or the vehicle to be exchanged (new for old) before the vehicle becomes unusable. In this case, it is preferable to calculate the drivable distance in units of replaceable devices (parts) mounted on the vehicle and notify which device has failed or needs to be replaced. By calculating the drivable distance for each replaceable device (component) mounted on the vehicle and notifying the user, it is possible to appropriately replace the device or component.

[0054] In addition, by Figure 14 The drivable distances calculated by the controllers 361 to 369 are reset to their initial values ​​when the corresponding devices are replaced. This allows the drivable distances to be accurate even when components of the devices in the vehicle are replaced.

[0055] [Example of drivable distance using multiple vehicle-mounted components] exist Figure 14 In the example of FIG, the drivable distance is calculated for each of the plurality of devices mounted on a vehicle. However, the drivable distance may be calculated for each of the plurality of components instead of the plurality of devices mounted on a vehicle. Figure 15 This is a block diagram showing a modified example of the vehicle degradation prediction system (an example of selecting the minimum value of a component). The first device 351 of the target vehicle 11 is equipped with a first component 351a, a second component 351b, ..., and an Nth component 351n (N is an integer greater than or equal to 2). Controllers 361 to 369 of the first device 351 manage the drivable distances of the components 351a to 351n.

[0056] At this time, the minimum value selection unit 310a of the vehicle integrated control unit 310 selects the minimum value of the drivable distances of each component calculated by each controller 361 (here, 50,000 km for the first component 351a). The drivable distance presentation unit 310b then presents the minimum drivable distance value. Furthermore, when presenting the minimum drivable distance value, the drivable distance presentation unit 310b may also present information about which component has the minimum value.

[0057] Therefore, with Figure 14 Similarly, it is possible to avoid the poor running of the target vehicle when the drivable distance of the target vehicle is still remaining. In addition, it is possible to appropriately inform the replacement of the target parts, etc.

[0058] [Example using information of vehicles that have undergone reliability testing] When the degradation prediction information generating unit 105 obtains the degradation prediction information, it can add not only the information of the actually driven vehicle but also the information of the reliability test of the vehicle that has been conducted in advance. Figure 16 As shown, the degradation prediction information generating unit 105 adds a plurality of reliability test result information, namely, test result 1, test result 2, ..., and test result W, to the information used to obtain the degradation prediction information. Furthermore, the degradation prediction information generating unit 105 obtains information on the driving distance at which a vehicle or an onboard device fails during the reliability test conducted during the development phase.

[0059] Generally, when a certain type of vehicle is first put into circulation, the number of vehicles is small and there is little information on failures, so it is sometimes difficult to perform appropriate degradation prediction. Figure 16 As shown, by adding the reliability test result information to the degradation prediction information generating unit 105, the correlation between the causal parameter and the degradation progress until the failure can be obtained even in a situation where there are few failures. Figure 8 The degradation prediction information is obtained by combining the information on the group Gp. 3 to which the target vehicle 11 described above belongs and the reliability test result information. As a result, the approximation accuracy of the degradation prediction information is improved, and the prediction accuracy of the degradation prediction value dp is improved.

[0060] [Example of using information about a broken-down vehicle] In addition, when the degradation prediction information generating unit 105 obtains the degradation prediction information, it is also possible to extract only the information of the vehicle that has failed, and obtain the degradation prediction information only from the information of the vehicle that has failed. Figure 17 As shown, the degradation prediction information generating unit 105 extracts information on the faulty vehicle or other device in group 3 as information for obtaining degradation prediction information. Figure 17 In the example of , the degradation prediction information generating unit 105 extracts the information of the vehicle 0010 and the information of the vehicle 0012 to obtain the degradation prediction information.

[0061] This allows the degradation prediction information to be obtained only for the vehicle with the fault, resulting in a strong correlation between the causal parameter and the degradation progression leading up to the fault. Furthermore, the approximation accuracy of the degradation prediction information is improved, thereby improving the prediction accuracy of the degradation prediction value. In addition, regarding the information of the broken-down vehicle, not only the broken-down vehicle information but also the scrapped vehicle information, repair information, and information after parts replacement can be added.

[0062] [Example of using information on vehicles that have undergone reliability tests and information on vehicles that have failed] Furthermore, the degradation prediction information generating unit 105 may also use Figure 16 Information on vehicles that underwent reliability testing as described in Figure 17 The degradation prediction information is obtained by using the information of the vehicle having the fault described in . That is, Figure 18 As shown, the degradation prediction information generating unit 105 obtains degradation prediction information using information on the vehicles 0010 and 0012 that have failed in the same group as the target vehicle 11 and information on the test results 1 to W.

[0063] This allows degradation prediction information to be derived using information from the vehicle that failed and test results from the development phase. This allows for a stronger correlation between the causal parameters and the progression of degradation leading up to the failure, improving the accuracy of the degradation prediction value. Furthermore, the use of test results allows for appropriate degradation prediction immediately after a vehicle is released to the market. In addition, in this Figure 18 In the case of the example, regarding the information of the broken-down vehicle, not only the broken-down vehicle information but also the scrapped vehicle, repaired vehicle, and information after parts replacement can be added.

[0064] [Other Modifications] The embodiments described so far have been described in detail to facilitate understanding of the present invention, and are not necessarily limited to having all the described configurations. For example, in the above-described embodiments, the degradation prediction value calculation unit 202 is provided on the vehicle 11 side.

[0065] Alternatively, the data center 100 may acquire information from the vehicle 11 and calculate the degradation prediction value. Alternatively, an application for calculating the degradation prediction value may be installed on a terminal device such as a smartphone owned by a user of the vehicle 11, and the degradation prediction value may be calculated on the terminal device.

[0066] Furthermore, the data center 100 may include a processing unit corresponding to the degradation prediction value calculation unit 202 , and the vehicle 11 may acquire and notify the degradation prediction value obtained by calculation in the data center 100 .

[0067] In addition, if Figure 1 As shown, the calculation of the drivable distance within the vehicle is performed by controllers connected to various devices or by the vehicle integrated control unit 310 within the vehicle. Alternatively, information can be transmitted from the controller of the device containing the component performing degradation prediction to the controllers of other devices connected to the in-vehicle network for calculation. This allows for efficient utilization of the in-vehicle controller.

[0068] In addition, Figure 1 and Figure 3 In the configuration diagrams shown in FIG, control lines and information lines are shown only as necessary for explanation, and not all control lines and information lines are shown in the product. In reality, it can be assumed that almost all components are connected to each other.

[0069] Furthermore, the vehicle integrated control controller 310 and other controllers 321 to 326 are examples of each processing unit being constituted on a computer by executing a program, but the program in this case can also be placed in an external memory, IC card, SD card, optical disk or other recording medium and transferred to the computer. Explanation of symbols

[0070] 10… Actual Environment, 11–14… Vehicle, 21… Energy Station, 22… Base Station, 23… Manufacturer, 100… Data Center, 101… Information Collection Unit, 102… Vehicle Information Matching Unit, 103… Grouping Unit, 104… Group Belonging Determination Unit, 105… Degradation Prediction Information Generation Unit, 106… Transmitter, 201… Receiving Unit, 202… Degradation Prediction Calculation Unit, 203… Fault Determination Unit, 301… Battery, 302… Power Cable, 303… …inverter, 304…motor, 305…transmission, 306…drive shaft, 307…steering, 308…brake, 309…engine, 310…vehicle integrated control unit, 310a…minimum value selection unit, 310b…drivable distance indication unit, 311…CPU, 312…memory, 313…interface, 321–326, 361…controller, 351–359…on-board devices, 351a–351n…components.

Claims

1. A vehicle degradation prediction system, characterized in that: have: a grouping unit that performs grouping by receiving data related to the driving conditions of a plurality of vehicles as input; a belonging group determination unit for determining to which group the vehicle to be diagnosed belongs; a degradation prediction information generating unit that generates degradation prediction information using data of a plurality of vehicles belonging to the group determined by the belonging group determining unit; as well as A failure determination unit determines whether the vehicle to be diagnosed has a failure by inputting a condition of the vehicle to be diagnosed into the degradation prediction information.

2. The vehicle degradation prediction system according to claim 1, wherein: The drivable distances of a plurality of devices mounted on a vehicle are predicted, and the drivable distance of the device with the shortest drivable distance is used as the drivable distance of the vehicle, and the fault determination unit makes a determination.

3. The vehicle degradation prediction system according to claim 1, wherein: The drivable distances of a plurality of components in a device mounted on a vehicle are predicted, and the drivable distance of the component with the shortest drivable distance is used as the drivable distance of the device, and the fault determination unit makes a determination.

4. The vehicle degradation prediction system according to claim 1, wherein: The failure determination unit notifies the driver of the drivable distance in units of replaceable devices mounted on the vehicle.

5. The vehicle degradation prediction system according to claim 1, wherein: The drivable distance associated with the replaced device mounted on the vehicle is reset to an initial state, and the failure determination unit performs the determination.

6. The vehicle degradation prediction system according to claim 1, wherein: The degradation prediction information generating unit generates degradation prediction information based on information on a plurality of vehicles in a group including a target vehicle and information on vehicles and devices that have undergone reliability testing.

7. The vehicle degradation prediction system according to claim 1, wherein: The degradation prediction information is generated by extracting information on a faulty vehicle in the group to which the target vehicle belongs.

8. The vehicle degradation prediction system according to claim 1, wherein: The degradation prediction information is generated based on information on failed vehicles in the group to which the target vehicle belongs and information on vehicles and devices that have undergone reliability tests.

9. The vehicle degradation prediction system according to claim 1, wherein: The degradation prediction information is transmitted from a controller of a device including a component that performs degradation prediction to a controller of another device connected to a network within the vehicle and is calculated.

10. The vehicle degradation prediction system according to claim 1, wherein: The fault determination unit indicates the available travel time.

11. A vehicle degradation prediction method, characterized in that: include: a grouping process that takes data related to the driving conditions of a plurality of vehicles as input and performs grouping; Judgment processing, determining to which group the vehicle to be diagnosed belongs; a degradation prediction information generation process for generating degradation prediction information using data of a plurality of vehicles belonging to the group determined by the determination process; as well as The diagnostic process determines whether the vehicle to be diagnosed is faulty by inputting a condition of the vehicle to be diagnosed into the degradation prediction information.

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