Vehicle deterioration prediction system and vehicle deterioration prediction method

Abstract

A vehicle deterioration prediction device according to the present invention comprises: a grouping unit that executes grouping by using, as input, data pertaining to the travel state of a plurality of vehicles; a determination unit that determines in which group a vehicle to be diagnosed belongs; a deterioration prediction information creation unit that creates deterioration prediction information by using data of a plurality of vehicles belonging to a group determined by the determination unit; and a diagnosis unit that determines whether a failure exists in a vehicle to be diagnosed by inputting the state of the vehicle to be diagnosed into the deterioration prediction information.

Description

Vehicle deterioration prediction system and vehicle deterioration prediction method

[0001] The present invention relates to a vehicle deterioration prediction system and a vehicle deterioration prediction method.

[0002] The reliability of on-board devices installed in vehicles such as automobiles is guaranteed by passing durability tests specified by each vehicle manufacturer. However, because the reliability of on-board devices is generally high to ensure sufficient vehicle durability, some individual devices may be of excessive quality.

[0003] If the on-board equipment is of excessive quality, it will increase the manufacturing cost of the on-board equipment, which is undesirable as it will lead to an increase in the price of the vehicle. However, since a failure of even one on-board device will cause a vehicle malfunction and hinder driving, it is not appropriate to reduce the durability and reliability of the on-board equipment. For this reason, even if the individual parts are of excessive quality, quality optimization is not carried out.

[0004] Typically, because the way a vehicle is used varies from user to user, the distance traveled before a breakdown also varies from vehicle to vehicle, making it difficult to know the likelihood of a breakdown for each individual vehicle. For example, while it is recommended that consumable parts such as tires and batteries be replaced after a certain distance has been traveled, in the case of on-board devices such as electronic components, replacement is generally only performed after they have broken down, making it difficult to predict breakdowns in advance.

[0005] Patent Document 1 describes a technology that collects data on the driving conditions of vehicles, groups them, and when a vehicle malfunction occurs, extracts vehicles belonging to the group to which the malfunctioning vehicle belongs as vehicles that are showing signs of a malfunction.

[0006] Japanese Patent Application Laid-Open No. 2022-145169

[0007] According to the technology described in Patent Document 1, vehicles are grouped based on data related to the vehicle's driving conditions, and when a failure occurs in one vehicle in a specific group, the other vehicles in the same group can also be considered as vehicles that are showing signs of failure. However, with the technology described in Patent Document 1, when a failure occurs in any one vehicle in a group, all vehicles in the same group are classified as vehicles that are showing signs of failure, so it cannot be said that the technology properly predicts the lifespan of vehicles.

[0008] If it were possible to accurately predict the occurrence of a failure in each vehicle, taking into account the lifespan of each on-board device, it would be possible to replace the relevant part before a failure occurs, thereby preventing excessive quality of on-board devices. However, such accurate detection of failure signs has traditionally been difficult.

[0009] An object of the present invention is to provide a vehicle deterioration prediction system and a vehicle deterioration prediction method that are capable of highly accurate deterioration prediction.

[0010] In order to solve the above problems, for example, the configuration described in the claims is adopted. The present application includes a plurality of means for solving the above problems, and one example thereof is a vehicle deterioration prediction system including a grouping unit that performs grouping using data related to the driving conditions of a plurality of vehicles as input, a group determination unit that determines which group a vehicle to be diagnosed belongs to, a deterioration prediction information creation unit that creates deterioration prediction information using data on the plurality of vehicles that belong to the group determined by the group determination unit, and a failure determination unit that determines whether the vehicle to be diagnosed has a failure by inputting the vehicle conditions of the vehicle to be diagnosed into the deterioration prediction information.

[0011] According to the present invention, deterioration prediction is performed using data from multiple vehicles belonging to a determined group, thereby improving the accuracy of diagnosing whether a vehicle being diagnosed has a malfunction. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments.

[0012] FIG. 1 is a diagram showing an example of the overall configuration of a vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 1 is a configuration diagram showing an example of a vehicle to which a diagnosis by the deterioration prediction system according to an embodiment of the present invention is applied. FIG. 2 is a block diagram showing an example of a detailed configuration of a vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 3 is a flowchart showing an example of the flow of deterioration prediction at a data center by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 4 is a flowchart showing an example of processing in a target vehicle when deterioration is predicted by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 5 is a flowchart showing an example of notification processing by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 6 is a diagram showing an example of grouping by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 7 is a diagram showing an example of processing performed by a deterioration prediction unit of the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 8 is a diagram showing an example of specific processed data by the deterioration prediction unit of the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 9 is a diagram showing an example of the relationship between a deterioration predicted value and mileage by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 10 is a diagram showing an example of the relationship between a deterioration predicted value and time by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 11 is a diagram showing an example of a histogram of history by the vehicle deterioration prediction system according to an embodiment of the present invention. FIG. 12 is a diagram showing an example of fuel properties according to an embodiment of the present invention. FIG. 1 is a block diagram showing a modified example of a vehicle deterioration prediction system according to an embodiment of the present invention (an example of selecting the minimum value of a device); FIG. 2 is a block diagram showing a modified example of a vehicle deterioration prediction system according to an embodiment of the present invention (an example of selecting the minimum value of a part); FIG. 3 is a diagram showing a modified example of a vehicle deterioration prediction system according to an embodiment of the present invention (an example of using information from vehicles that have undergone reliability testing); FIG. 4 is a diagram showing a modified example of a vehicle deterioration prediction system according to an embodiment of the present invention (an example of using information from vehicles that have undergone reliability testing and information from vehicles that have failed);

[0013] A vehicle deterioration prediction system and a vehicle deterioration prediction method according to one embodiment of the present invention (hereinafter referred to as "this example") will be described below with reference to the accompanying drawings.

[0014] [Overall Configuration of Vehicle Deterioration Prediction System] Fig. 1 shows an example of the overall configuration of a vehicle deterioration prediction system of this example. The vehicle deterioration prediction system of this example includes a data center 100. The data center 100 collects information on multiple vehicles 11, 12, 13, 14, ... from a real environment 10 via a network NW1. The data center 100 also collects information from an energy station 21, information from a base station 22, and information from a manufacturer 23 (vehicle manufacturer, equipment manufacturer) via the network NW1. In Fig. 1, one vehicle 11 of the multiple vehicles 11 to 14 is set as the target vehicle for which deterioration is predicted.

[0015] The data center 100 collects detailed information about components indicating what on-board devices are installed in each of the vehicles 11 to 14, sensor information about those components, and driving information, which is information about the driving conditions, from the respective vehicles 11 to 14. The data center 100 also collects energy-related information, such as the properties of gasoline and diesel, the properties of fuel hydrogen, and the electricity supply method, from the energy station 21. The data center 100 also collects ambient environment information from the base station 22 and equipment production information from the manufacturer 23.

[0016] The data center 100 includes an information collection unit 101, a vehicle information matching unit 102, a grouping unit 103, a group judging unit 104, a deterioration prediction information creating unit 105, and a transmitting unit 106. The information collection unit 101 collects information on components (on-board devices) of each vehicle 11, 12, 13, 14, ..., sensor information, driving information, etc. from the real environment 10. The information collection unit 101 also collects information from energy stations 21, base stations 22, and manufacturers 23.

[0017] The vehicle information matching unit 102 matches vehicle information based on individual information of each vehicle 11, 12, ..., the serial number of the on-board device, the surrounding environment, mileage, energy acquisition status, etc. The grouping unit 103 groups each of the vehicles 11, 12, ... based on the matching information in the vehicle information matching unit 102. The belonging group determination unit 104 performs a determination process to determine the group to which the target vehicle (here, vehicle 11) belongs.

[0018] The deterioration prediction information creation unit 105 creates deterioration prediction information based on information related to vehicles belonging to the group to which the target vehicle 11 belongs. The deterioration prediction information creation unit 105 may acquire a failure determination value based on the created deterioration prediction information. The transmission unit 106 transmits the deterioration prediction information of the target vehicle 11 created by the deterioration prediction information creation unit 105 to the target vehicle 11. Furthermore, 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 deterioration prediction calculation unit 202, and a failure determination unit 203. The receiving unit 201 receives deterioration prediction information from the data center 100. Furthermore, when a failure determination value is transmitted, the receiving unit 201 receives the transmitted failure determination value. The deterioration prediction calculation unit 202 calculates a deterioration prediction value by substituting information about the target vehicle 11 into the received deterioration prediction information.

[0020] The failure determination 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 failure based on the comparison result. The result of the determination by the failure determination unit 203 is notified by display on a display unit (not shown) in the target vehicle 11. Alternatively, the result of the failure determination may be transmitted to the data center 100 or the manufacturer. The target vehicle 11 also has a configuration for transmitting information necessary for degradation prediction and information necessary for grouping to the data center 100.

[0021] 2 shows an example of the configuration of a vehicle 11. 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 this power supply, and rotates a drive shaft 306 via a transmission 305. The vehicle 11 is also equipped with an engine 309, and the drive of the engine 309 can rotate the drive shaft 306 via the transmission 305. The vehicle 11 is also equipped with a steering wheel 307 and a brake 308. Each of these parts of the vehicle 11 is controlled by an on-board device 310.

[0022] Fig. 3 shows an example of the configuration of an on-board device mounted on the vehicle 11. In particular, the example of Fig. 3 shows the configuration of a control device of the vehicle 11. The vehicle 11 has a plurality of controllers. That is, the vehicle 11 has a vehicle integrated control controller 310, and controllers 321 to 326 for each device that are connected to the vehicle integrated control controller 310 via an internal vehicle network so that they can communicate with each other.

[0023] The vehicle integrated 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 programs stored in the memory 312 to configure a processing function unit in the work area of ​​the memory 312 and execute vehicle control processing. The receiving unit 201, deterioration prediction calculation unit 202, and failure determination unit 203 described in FIG. 1 are also configured in the work 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. The interface 313 also exchanges information with the data center 100 via the network NW2 shown in FIG. 1.

[0024] The controllers 321 to 326 for each device will be explained below. The controller 321 controls the engine 306. The controller 322 controls the transmission 305. The controller 323 controls the battery 301. The controller 324 controls the inverter 303 and the motor 304. The controller 325 controls the steering 307. The controller 326 controls the brake 308.

[0025] Similar to the vehicle integrated control controller 310, these controllers 321 to 326 are configured as computers equipped with a CPU, memory, and interface. Each of the controllers 321 to 326 controls the devices connected to it based on commands from the vehicle integrated control controller 310. Each of the controllers 321 to 326 also transmits information about the status of each device to the vehicle integrated control controller 310. The vehicle integrated control controller 310 performs calculation processing of the remaining driving distance of the in-vehicle device. Alternatively, the controllers 321 to 326 that control each device perform calculation processing of the remaining driving distance of the connected device.

[0026] Note that the vehicle integrated control controller 310 and each of the controllers 321 to 326 are configured to perform arithmetic processing based on an installed program, but other configurations are also possible. For example, part or all of the vehicle integrated control controller 310 may be realized by dedicated hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). Furthermore, the data center 100 shown in FIG. 1 is also configured by a computer similar to the vehicle integrated control controller 310 or other dedicated hardware.

[0027] [Processing at Data Center] Figure 4 is a flowchart showing an example of the flow of deterioration prediction processing in the data center 100 that constitutes the deterioration prediction system of this example. First, the information collection unit 101 of the data center 100 performs a process of collecting information related to vehicles (step S11). Next, the vehicle matching unit 102 performs a process of matching vehicle information (step S12). Here, the vehicle matching unit 102 performs a one-to-one matching process between individual vehicles and devices installed in the vehicles. For example, the vehicle matching unit 102 uses the vehicle serial number or the device serial number to search for shipping inspection results, etc., obtained from the vehicle manufacturer or device manufacturer when the device was manufactured, and associates them with the vehicle.

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

[0029] Next, the grouping unit 103 performs a vehicle grouping process based on the matching information obtained by the matching process in the vehicle information matching unit 102 (step S13). Thereafter, the belonging group determination unit 104 determines the group to which the target vehicle (here, vehicle 11 shown in FIG. 1 ) belongs (step S14). Once the belonging group determination unit 104 determines the group of the target vehicle in step S14, the deterioration prediction information creation unit 105 performs a deterioration prediction information creation process based on information related to vehicles belonging to the group to which the target vehicle 11 belongs (step S15). The deterioration prediction information is information necessary for calculating a deterioration prediction for the target vehicle 11. Here, the deterioration prediction information creation unit 105 may acquire a failure determination value by calculation based on the created deterioration prediction information.

[0030] Then, the transmission unit 106 transmits the deterioration prediction information created by the deterioration prediction information creation unit 105 to the target vehicle 11 (step S16). If the deterioration prediction information creation unit 105 obtains a malfunction determination value, the transmission unit 106 also transmits the malfunction determination value to the target vehicle 11.

[0031] 5 is a flowchart showing an example of the processing flow in the target vehicle 11 that constitutes the deterioration prediction system of this example. First, the target vehicle 11 transmits vehicle information necessary for grouping the vehicles to the data center 100 (step S21). Next, the target vehicle 11 determines whether or not it is necessary to update the deterioration prediction information of its own vehicle (step S22).

[0032] If it is determined in step S22 that the deterioration prediction information of the host vehicle needs to be updated (Yes in step S22), the receiving unit 201 receives the deterioration prediction information of the group to which the host vehicle belongs from the data center 100 (step S23). After receiving the deterioration prediction information in step S23, or if it is determined in step S22 that the deterioration prediction information of the host vehicle does not need to be updated (No in step S22), the deterioration prediction calculation unit 202 inputs the vehicle information of the host vehicle into the deterioration prediction information and calculates a deterioration prediction value (step S24).

[0033] When the calculation of the deterioration prediction value in step S24 is completed, the deterioration prediction calculation unit 202 determines whether the obtained deterioration prediction value is equal to or greater than a predetermined threshold value (step S25). Here, the deterioration threshold value is set in advance based on design information at the development stage. If the deterioration prediction 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 malfunctioned and notifies the user of the device malfunction (step S27).

[0034] If the predicted deterioration 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 distance the vehicle can travel before breaking down and notifies the user (step S26). The notification in steps S26 and S27 is performed, for example, by display on a display unit mounted on the vehicle or by audio output. Alternatively, the notification may be performed by sending a message to a mobile terminal carried by the user of the vehicle 11. The calculation result may also be transmitted to the data center 100.

[0035] In the process described in the flowchart of FIG. 5 , in step S26, the failure determination unit 203 calculates the distance the vehicle can travel before breaking down and notifies the user. Alternatively, as shown in the flowchart of FIG. 6 , after calculating the distance the vehicle can travel before breaking down in step S26, the failure determination unit 203 may calculate the time the vehicle can travel until breaking down in step S28 and notify the user of the time (number of days the vehicle can travel). The calculation of the time can be performed by converting the distance the vehicle can travel into time using, for example, a moving average value of the distance traveled by the target vehicle 11 for each fixed period (e.g., 30 days). The other steps in FIG. 6 are the same as those in the flowchart of FIG. 5 . By notifying the user in terms of time in this manner, it is possible to provide a notification that is easy for the user to understand.

[0036] [Example of Grouping] Fig. 7 is a diagram showing an example of grouping by the grouping unit 103. Fig. 7 shows the vehicle information x1 to x n x i and x j 7 is a distribution diagram obtained by taking a cross section of the vehicle. The distribution diagram on the left side of FIG. 7 is an example before grouping, and the distribution diagram on the right side of FIG. 7 is an example after grouping. In FIG. 7, the characteristics indicated by the circle marks indicate the characteristics of the target vehicle 11, and the distribution characteristics indicated by the black marks indicate the distribution characteristics of vehicles equipped with the same on-board equipment as the target vehicle 11. Furthermore, the distribution characteristics indicated by the triangle marks indicate the distribution characteristics of vehicles that have experienced a failure among the distribution characteristics of vehicles equipped with the same on-board equipment as the target vehicle 11. Note that the vehicles indicated by the distribution characteristics here may include vehicles that have already been scrapped and vehicles whose on-board equipment has been replaced.

[0037] Vehicle information here x1 to x n The following information is used as variables: Sensor information from on-board devices installed in the vehicle (voltage, current, etc.) Driving history (driving position, distance traveled, etc.) Surrounding environment information (temperature, humidity, air pressure, road surface conditions, altitude, etc.) Energy station information (charger type, supplied power, gasoline properties, hydrogen fuel properties, etc.)

[0038] The grouping unit 103 divides such vehicle information x1 to xn 7 shows an example of grouping into three groups, group Gr. 01 to group Gr. 03. Note that in FIG. 7, a large number of vehicle information x1 to x n Two pieces of information x i and x j However, in reality, the grouping unit 103 divides all the information x1 to x n When the grouping unit 103 performs grouping, the grouping unit 103 performs classification based on rules, for example. Alternatively, the grouping unit 103 may perform grouping using unsupervised machine learning or the like.

[0039] [Example of Deterioration Prediction Information] Fig. 8 shows an example of the process of creating deterioration prediction information in the deterioration prediction information creation unit 105. When the distribution shown on the left side of Fig. 8 (the same distribution as Fig. 7) is acquired, the deterioration prediction information creation unit 105 investigates the devices and parts installed in vehicles that have experienced a breakdown among the vehicles in the same group as the target vehicle 11, and creates information x1 to x2 of the vehicles that have experienced a breakdown. n Using the deterioration prediction information d ｐ However, the information of the vehicle that experienced the breakdown, x1 to x n The use of the information x1 to x2 of other vehicles in the same group as the target vehicle 11 is an example. n Vehicle information x1 to x n A specific example of this will be explained in the next FIG.

[0040] As shown in the table on the right side of Figure 8, the information x1 to x2 of the vehicles that have experienced a breakdown in the group is n and deterioration prediction information d ｐ In addition, information on the vehicle and the on-board device that underwent reliability testing during the development stage of the vehicle may be combined. Details of using information on the vehicle and the on-board device that underwent reliability testing during the development stage of the vehicle will be described later.

[0041] FIG. 9 shows the deterioration prediction information d ｐ and each variable x1 to x n The following shows an example of the relationship between the variables x1 to x n For example, variables x1 to x ncontains the device (component) sensor information, such as voltage, current, temperature, number of charge attempts, and number of quick charge attempts. n The data includes vehicle information such as product inspection results (device variability: high, medium, or low performance), vehicle type, model year, mileage, and location.

[0042] Also, variables x1 to x n contains ambient environment information such as temperature, humidity, air pressure, altitude, and road surface condition. n The information includes energy station information such as the type of charger, the power supply method, the gasoline properties, and the properties of the fuel hydrogen. Furthermore, although not shown in FIG. 9, design information and durability test information may also be included. Furthermore, usage history information of scrapped vehicles may also be used in addition to broken-down vehicles.

[0043] The information obtained here can be used as a histogram of time history information, for example, as shown in Figure 12, and the time and number of times within a certain range can be used as variables. That is, the example in Figure 12 shows the voltage history (horizontal axis) and frequency (vertical axis). As shown in Figure 12, the voltage history is divided into levels 1 to 10, and the frequency (time) values ​​falling within each range 1 to 10 and the information x1 to x2 are used. n It may be configured as follows.

[0044] 10 and 11 show an example of calculation of the remaining driving distance and remaining driving time by the deterioration prediction calculation unit 202 of the vehicle 11. ｐ Here, the deterioration prediction calculation unit 202 calculates the deterioration prediction information dp when the deterioration prediction information dp increases in proportion to the mileage Dd, and the deterioration prediction information dp is calculated based on the threshold dp －limit In the above cases, it is determined that a malfunction has occurred.

[0045] In the example of FIG. 10, the traveled distance Dd is the distance value Dd ｐ When this is the case, the deterioration prediction value dp －ｐ Therefore, the deterioration prediction calculation unit 202 determines that the device is healthy. －limit The corresponding travel distance Dd －limitIf the current distance Dd is greater than or equal to the current distance Dd, the failure determination unit 203 determines that the target vehicle 11 is in a failure state. ｐ - Threshold travel distance Dd －limit ], the distance that can be traveled before a breakdown can be calculated.

[0046] FIG. 11 shows the relationship between the time Td (vertical axis) of the vehicle 11 and the deterioration prediction information d ｐ Here, the failure determination unit 203 determines whether the deterioration prediction information dp increases in proportion to the time Td, and whether the threshold value dp of the deterioration prediction information dp －limit In the above cases, it is determined that a malfunction has occurred.

[0047] In the example of FIG. 11, the time Tp is equal to the value T ｐ When this is the case, the deterioration prediction value dp －ｐ Therefore, the deterioration prediction calculation unit 202 determines that the device is healthy. －limit The time T limit If the above is true, the failure determination unit 203 determines that the target vehicle 11 is in a failure state. If the target vehicle 11 is determined to be in a healthy state, the deterioration prediction calculation unit 202 calculates the time Tp of the target vehicle 11 by subtracting the time Tp from the threshold value. limit ], the remaining driving distance and time until failure can be calculated.

[0048] The remaining driving time T can be calculated by dividing the remaining driving distance Dd of the target vehicle 11 by the driving distance within a certain time. In the examples of Figures 10 and 11, the deterioration prediction value dp increases linearly, but this linear increase is merely an example and is not limiting. However, the deterioration prediction value dp can also be expressed as a monotonic increase or a monotonic phenomenon.

[0049] [Example of gasoline properties] Figure 13 shows the variables x1 to x n The gasoline properties indicate characteristics such as color, density, distillation properties, octane number, vapor pressure, sulfur content, and benzene content. The deterioration prediction information creation unit 105 acquires information on these gasoline properties based on the gasoline manufacturer, the time (season) when the gasoline was used, and the like, and calculates the variables x1 to x nDepending on the fuel components, such as the properties of gasoline, the degree of corrosion of the plastic and metal parts of the hoses may differ, which may result in different predicted deterioration values.

[0050] According to the deterioration prediction system of this example described above, deterioration prediction is performed using data on multiple vehicles that belong to the determined group, so it is possible to diagnose whether or not the target vehicle is malfunctioning. In particular, when dividing into groups, the grouping unit 103 divides the variables x1 to x n Since the deterioration prediction information creation unit 105 collects various information about the vehicle as the deterioration prediction information dp, the deterioration prediction information creation unit 105 can significantly improve the accuracy of the diagnosis. The deterioration prediction information creation unit 105 can also diagnose the mileage and time required for a breakdown. Furthermore, by using only information about the broken-down vehicle when obtaining the deterioration prediction information dp, the deterioration prediction information creation unit 105 can perform a diagnosis with even higher accuracy.

[0051] [Example of Using the Driving Distance of Multiple On-Board Devices] If the driving distance is calculated individually for each of multiple devices mounted on a single vehicle, the calculated driving distance can be used as the driving distance for each of them. Figure 14 is a block diagram showing a modified example of a vehicle deterioration prediction system (an example of selecting the minimum value of the devices). The target vehicle 11 is equipped with a first on-board device 351, a second on-board device 352, ..., an Nth on-board device 359 (N is an integer of 2 or more), each of which is equipped with controllers 361 to 369.

[0052] At this time, the minimum value selection unit 310a of the vehicle integrated controller 310 selects the minimum value of the remaining driving distance calculated by each of the controllers 361 to 369 (here, 50,000 km for the first in-vehicle device 351). Then, the remaining driving distance presentation unit 310b presents the minimum value of the remaining driving distance. When presenting the minimum value of the remaining driving distance, the remaining driving distance presentation unit 310b may also present information about which device has the minimum value.

[0053] This makes it possible to prevent the target vehicle from becoming unrunnable when the target vehicle still has remaining drivable distance. Furthermore, the drivable distance notification unit 310b calculates the drivable distance of each device (inverter, motor, battery, etc.) and displays the device selected as the minimum value, allowing the corresponding device to be repaired or replaced or the vehicle to be switched (replaced) before the vehicle becomes unrunnable. In this case, it is preferable to calculate the drivable distance for each replaceable device (component) installed in the vehicle and notify the user of which device has failed or needs to be replaced. Calculating the drivable distance for each replaceable device (component) installed in the vehicle and notifying the user of the failure or need to replace the device or component allows the device or component to be replaced appropriately.

[0054] Furthermore, the remaining driving distance calculated by each of the controllers 361 to 369 shown in Fig. 14 is reset to its initial state when the device corresponding to each of the controllers 361 to 369 is replaced. This ensures that the remaining driving distance will be an accurate value even if a part of the device inside the vehicle is replaced.

[0055] [Example of Using the Driving Distance of Multiple On-Vehicle Components] In the example of Figure 14, the driving distance is calculated individually for each of multiple devices mounted on a single vehicle. However, the driving distance may be calculated individually for each of multiple components, rather than for multiple devices mounted on a single vehicle. Figure 15 is a block diagram showing a modified example of a vehicle deterioration prediction system (an example of selecting the minimum value of components). A first device 351 of a target vehicle 11 is equipped with a first component 351a, a second component 351b, ..., an Nth component 351n (N is an integer greater than or equal to 2), and controllers 361 to 369 of the first device 351 manage the driving distance of each of the components 351a to 351n.

[0056] At this time, the minimum value selection unit 310a of the vehicle integrated controller 310 selects the minimum value of the remaining driving distance of each part calculated by each controller 361 (here, 50,000 km for the first part 351a). Then, the remaining driving distance presentation unit 310b presents the minimum value of the remaining driving distance. When presenting the minimum value of the remaining driving distance, the remaining driving distance presentation unit 310b may also present information about which part has the minimum value.

[0057] This makes it possible to prevent the target vehicle from becoming unsuitable for driving when the target vehicle still has a remaining driving distance, as in the example of Fig. 14. Also, it becomes possible to appropriately notify the user of the need to replace the target part.

[0058] [Example of using information on vehicles that have undergone reliability testing] When the deterioration prediction information creation unit 105 obtains deterioration prediction information, it may add information on vehicles that are actually running, as well as information on reliability tests that have been conducted in advance on the vehicles. That is, as shown in Fig. 16 , the deterioration prediction information creation unit 105 adds a plurality of pieces of reliability test result information, such as test result 1, test result 2, ..., test result W, to the information for obtaining the deterioration prediction information. Furthermore, in reliability tests conducted in the development stage, the deterioration prediction information creation unit 105 obtains information on the mileage at which the vehicle or on-board device will break down.

[0059] Typically, immediately after a particular type of vehicle begins to be distributed on the market, there are only a few vehicles and there is little information on failures, making it difficult to properly predict deterioration. Here, as shown in FIG. 16 , the deterioration prediction information creation unit 105 adds reliability test result information, thereby making it possible to obtain a correlation between the causative parameters and the progression of deterioration leading up to a failure even in a situation where there are few failures. For example, the deterioration prediction information creation unit 105 obtains deterioration prediction information from information on group Gp.3 to which the target vehicle 11 described in FIG. 8 belongs and reliability test result information. This improves the approximation accuracy of the deterioration prediction information and improves the prediction accuracy of the deterioration prediction value dp.

[0060] [Example of Using Information on Broken-Down Vehicles] Furthermore, when the deterioration prediction information creation unit 105 obtains deterioration prediction information, it may extract only information on vehicles in which a breakdown has occurred, and obtain deterioration prediction information only from information on vehicles in which a breakdown has occurred. That is, as shown in Fig. 17 , the deterioration prediction information creation unit 105 extracts information on devices such as broken-down vehicles in group 3 as information for obtaining deterioration prediction information. For example, in the example of Fig. 17 , the deterioration prediction information creation unit 105 extracts information on vehicles 0010 and 0012 to obtain deterioration prediction information.

[0061] This limits the information obtained when obtaining deterioration prediction information to vehicles that have broken down, and a strong correlation is obtained between the causative parameters and the progression of deterioration leading up to the breakdown. This improves the approximation accuracy of the deterioration prediction information and the prediction accuracy of the deterioration prediction value. Note that the information on broken down vehicles may include not only information on the broken down vehicles but also information on scrapped vehicles, repairs, and part replacements.

[0062] [Example of using information on vehicles that have undergone reliability testing and information on vehicles that have broken down] Furthermore, the deterioration prediction information creation unit 105 may obtain deterioration prediction information using information on vehicles that have undergone reliability testing as described in Fig. 16 and information on vehicles that have experienced a failure as described in Fig. 17. That is, as shown in Fig. 18, the deterioration prediction information creation unit 105 obtains deterioration prediction information using information on vehicles 0010 and 0012, which are vehicles that have experienced a failure in the same group as the target vehicle 11, and information on test results 1 to W.

[0063] As a result, the information used to obtain deterioration prediction information is information based on the broken-down vehicle and test results from the development stage, and a stronger correlation can be obtained between the causative parameters and the progress of deterioration leading up to the failure, improving the prediction accuracy of the deterioration prediction value. Furthermore, because test results are used, appropriate deterioration prediction becomes possible immediately after the vehicle is released on the market. Note that, in the example of Figure 18, the information on the broken-down vehicle may also include information on scrapping, repairs, and part replacement in addition to the information on the broken-down vehicle.

[0064] [Other Modifications] The embodiment described above has been described in detail to clearly explain the present invention, and is not necessarily limited to one that includes all of the described configurations. For example, in the embodiment described above, the deterioration 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 perform the calculation process of the deterioration prediction value. Alternatively, an application program that performs the calculation process of the deterioration prediction value may be installed in a terminal device such as a smartphone carried by a user of the vehicle 11, and the deterioration prediction value may be calculated on the terminal device.

[0066] Furthermore, the data center 100 may be provided with a processing unit equivalent to the deterioration prediction value calculation unit 202, and the vehicle 11 may acquire and notify the deterioration prediction value obtained by calculation in the data center 100.

[0067] 1, the calculation of the remaining driving distance within the vehicle is performed by a controller connected to each device or by the vehicle integrated controller 310 within the vehicle. Alternatively, information may be transferred from the controller of the device containing the part for which deterioration prediction is performed to a controller of another device connected to the network within the vehicle for calculation. This allows the controller within the vehicle to be used more effectively.

[0068] 1 and 3, only the control lines and information lines that are considered necessary for explanation are shown, and not all control lines and information lines in the product are necessarily shown. In reality, it can be assumed that almost all components are interconnected.

[0069] Furthermore, the vehicle integrated general controller 310 and the other controllers 321 to 326 are examples in which each processing unit is configured on a computer by executing a program, but in this case the program may be stored on a recording medium such as an external memory, an IC card, an SD card, or an optical disk and transferred to the computer.

[0070] 10... Actual environment, 11 to 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... Belonging group determination unit, 105... Deterioration prediction information creation unit, 106... Transmitter, 201... Receiver, 202... Deterioration 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 controller, 310a...minimum value selection unit, 310b...travelable distance presentation unit, 311...CPU, 312...memory, 313...interface, 321 to 326, 361...controller, 351 to 359...vehicle-mounted device, 351a to 351n...parts

Claims

1. A vehicle deterioration prediction system comprising: a grouping unit that performs grouping using data regarding the driving conditions of multiple vehicles as input; a group belonging determination unit that determines which group a vehicle to be diagnosed belongs to; a deterioration prediction information creation unit that creates deterioration prediction information using data on multiple vehicles that belong to the group determined by the group belonging determination unit; and a failure determination unit that determines whether the vehicle to be diagnosed has a failure by inputting the vehicle conditions of the vehicle to be diagnosed into the deterioration prediction information.

2. A vehicle deterioration prediction system as described in claim 1, in which the failure judgment unit predicts the driving distance for multiple devices installed in a single vehicle, and determines the vehicle's driving distance using the driving distance of the device with the shortest driving distance.

3. A vehicle deterioration prediction system as described in claim 1, in which the failure judgment unit predicts the remaining driving distance for multiple parts in a device installed in the vehicle, and uses the remaining driving distance of the part with the shortest driving distance as the remaining driving distance of the device.

4. The vehicle deterioration prediction system according to claim 1, wherein the failure determination unit notifies the vehicle's remaining driving distance in units of replaceable devices mounted on the vehicle.

5. The vehicle deterioration prediction system according to claim 1, wherein the mileage of a replaced device mounted on the vehicle is reset to an initial state and judged by the failure judgment unit.

6. A vehicle deterioration prediction system as described in claim 1, wherein the deterioration prediction information creation unit creates deterioration prediction information based on information on a plurality of vehicles in a group including the target vehicle, and information on vehicles and equipment that have undergone reliability testing.

7. The vehicle deterioration prediction system according to claim 1, wherein the deterioration prediction information is created by extracting information on broken-down vehicles within a group to which the target vehicle belongs.

8. The vehicle deterioration prediction system according to claim 1, wherein the deterioration prediction information is created based on information about broken-down vehicles in the group to which the target vehicle belongs and information about vehicles and equipment that have undergone reliability testing.

9. The vehicle deterioration prediction system according to claim 1, wherein the deterioration prediction information is transferred from a controller of a device including a part for which deterioration prediction is performed to a controller of another device connected to a network within the vehicle and calculated.

10. The vehicle deterioration prediction system according to claim 1, wherein the failure determination unit presents a remaining driving time.

11. A method for predicting deterioration of a vehicle, comprising: a grouping process that uses data regarding the driving conditions of a plurality of vehicles as input and performs grouping; a judgment process that determines which group a vehicle to be diagnosed belongs to; a deterioration prediction information creation process that creates deterioration prediction information using data on a plurality of vehicles that belong to the group determined by the judgment process; and a diagnosis process that determines whether or not the vehicle to be diagnosed has a malfunction by inputting the condition of the vehicle to be diagnosed into the deterioration prediction information.