Method for diagnosing deterioration of vehicle and vehicle-mounted component

Abstract

The present disclosure relates to a deterioration diagnosis method for a vehicle and a vehicle-mounted component. The vehicle is provided with a storage device configured to store a presumption algorithm configured to output a degree of deterioration of a component if a value of a parameter related to the component mounted on the vehicle is input; a sensor configured to detect the value of the parameter; and a control device. The control device is configured to perform a performance test based on automatic driving of the vehicle, acquire data indicating a performance of the component in the performance test, and update the presumption algorithm using the data acquired in the performance test.

Description

Technical Field

[0001] This disclosure relates to methods for diagnosing the deterioration of vehicles and onboard components. Background Technology

[0002] For example, Japanese Patent Application Publication No. 2006-096060 discloses a system that sends vehicle status information, which is required for at least the maintenance and repair of a customer's vehicle equipped with an in-vehicle terminal, from an in-vehicle terminal to a management center.

[0003] In recent years, from the perspective of reducing environmental impact, there has been a demand for longer vehicle lifespans. To achieve this, there is a desire to diagnose the deterioration of onboard components within vehicles in use and replace them at appropriate intervals. Furthermore, to meet evolving needs, there is a desire to retrofit commercially available vehicles with additional functions. Examples of such retrofitted functions (systems) include automatic braking systems and autonomous driving kits (autonomous driving systems).

[0004] As a method for diagnosing the deterioration of automotive components, a method using an estimation algorithm is considered that represents the relationship between a specified parameter (hereinafter also referred to as a "deterioration parameter") related to the automotive component and the degree of deterioration of the automotive component. In the estimation algorithm, if a value of the deterioration parameter related to the automotive component is input, the degree of deterioration of the automotive component is output. A computer installed in the vehicle inputs the value of the deterioration parameter detected in the vehicle being used into the estimation algorithm, thereby obtaining the degree of deterioration of the automotive component.

[0005] However, the degradation patterns of onboard components vary from vehicle to vehicle. Therefore, it is considered to optimize the estimation algorithm for each vehicle at the automotive manufacturing plant before they leave the factory, and to equip the vehicle with a storage device containing the optimized estimation algorithm. However, if the vehicles (base vehicles) are modified after leaving the factory to add functionality, the vehicle's characteristics will change, potentially reducing the estimation accuracy of the algorithm. Furthermore, optimizing the estimation algorithm for each vehicle individually at the automotive manufacturing plant is inefficient and increases vehicle costs. Summary of the Invention

[0006] This disclosure provides a technique for appropriately updating an estimation algorithm installed in a vehicle.

[0007] The vehicle of the first embodiment of this disclosure includes: a storage device configured to store an estimation algorithm, the estimation algorithm being configured to output the degradation degree of a component if a value of a parameter related to a component mounted on the vehicle is input; a sensor configured to detect the value of the parameter; and a control device. The control device is configured to: perform an autonomous driving performance test based on the vehicle; acquire data representing the performance of the component in the performance test; and update the estimation algorithm using the data acquired in the performance test.

[0008] Based on the above configuration, the control device performs performance tests of vehicle-based autonomous driving and updates the estimation algorithm based on the results of the performance tests (data obtained during the performance tests). In a vehicle with this configuration, the estimation algorithm can be appropriately updated to match the characteristics of the vehicle. Furthermore, with the updated estimation algorithm, the degree of degradation of on-board components can be estimated with high accuracy in the vehicle in use.

[0009] The estimation algorithm can be either a rule-based program or an algorithm implemented through AI (artificial intelligence). The estimation algorithm can also include at least one of a formula, a mapping graph, and a model. The aforementioned control device can consist of a single computer or include multiple computers.

[0010] In the vehicle according to the first embodiment of this disclosure, the control device may also be configured to perform automatic driving of the vehicle during a predetermined operating period. Alternatively, if a first operating period and a second operating period are set in the control device, the performance test may be performed from the end of the first operating period to the beginning of the second operating period. The second operating period may be the next operating period after the first operating period.

[0011] In the above configuration, the vehicle is used through autonomous driving. Furthermore, performance testing is performed during the intervals between vehicle operations. This prevents performance testing from interfering with vehicle operation.

[0012] In the vehicle of the first embodiment of this disclosure, the control device may also be configured to: during the operation period, input the value of the parameter detected by the sensor to the estimation algorithm, and use the degradation degree of the component output from the estimation algorithm to determine whether the degradation degree of the component exceeds a predetermined threshold.

[0013] In the above configuration, during vehicle operation, the output of an estimation algorithm is used to determine whether the degradation degree of a component exceeds a predetermined threshold. This configuration facilitates component replacement at appropriate time intervals. Furthermore, the updated estimation algorithm can estimate the component degradation degree with high accuracy, thus reducing the threshold margin. Therefore, components can be used easily until the end of their lifespan. Extending the lifespan of components contributes to reducing environmental impact.

[0014] In the vehicle of the first embodiment of this disclosure, the data obtained through the performance test may include at least one of data representing the vehicle's braking performance and data representing the vehicle's acceleration performance. Alternatively, the control device may be configured to use the data obtained through the performance test to perform calibration for the vehicle's autonomous driving.

[0015] In the above configuration, autonomous driving of the vehicle can be easily performed appropriately through calibration. Furthermore, the data used for calibration is obtained through performance testing, thereby enabling efficient calibration.

[0016] In the vehicle of the first embodiment of this disclosure, the control device may also be configured to use the estimation algorithm to estimate the degree of degradation of the component during the autonomous driving of the vehicle when the number of updates of the estimation algorithm reaches a predetermined number or more.

[0017] It is believed that the more times the estimation algorithm is updated, the higher its estimation accuracy. Based on the above structure, it is possible to suppress the use of estimation algorithms with low estimation accuracy.

[0018] In the vehicle of the first embodiment of this disclosure, the component mounted on the vehicle may be a brake pad constituting a hydraulic disc brake device. The parameter associated with the component may be the hydraulic pressure level of the brake pad. The data obtained through the performance test may include data representing the braking performance of the vehicle.

[0019] Based on the above configuration, the control device uses an updated estimation algorithm, which makes it easy to estimate the deterioration degree of the brake pad with high accuracy. Furthermore, by estimating the deterioration degree of the brake pad with high accuracy, the lifespan of the brake pad is extended.

[0020] In the vehicle of the first embodiment of this disclosure, the component mounted on the vehicle may also be a driving motor. The parameters associated with the component may also be at least one of the current used to drive the driving motor and the temperature of the driving motor. The data obtained through the performance test may also include data representing the acceleration performance of the vehicle.

[0021] Based on the above configuration, the control device uses an updated estimation algorithm, which makes it easy to estimate the degradation degree of the driving motor with high accuracy. Furthermore, by estimating the motor degradation degree with high accuracy, the lifespan of the motor is extended.

[0022] Alternatively, the vehicle of the first embodiment of this disclosure may also include: an autonomous driving kit; and a vehicle control interface that mediates the exchange of signals between the control device and the autonomous driving kit. Alternatively, the autonomous driving kit may be configured to send instructions for autonomous driving to the control device via the vehicle control interface. Alternatively, the control device may be configured to control the vehicle according to the instructions from the autonomous driving kit and send signals indicating the state of the vehicle to the autonomous driving kit via the vehicle control interface.

[0023] The aforementioned vehicles possess vehicle control interfaces, making them easy to retrofit with autonomous driving kits. These kits can also be adapted to commercially available base vehicles. Even with the autonomous driving kit removed, the control unit can operate independently. In vehicles retrofitted with autonomous driving kits, the aforementioned performance tests and update processes are performed to appropriately update the estimation algorithm to match the vehicle's characteristics.

[0024] The second embodiment of this disclosure includes a vehicle comprising: a storage device configured to store a first estimation algorithm and a second estimation algorithm; a first sensor; a second sensor; and a control device. Regarding the first estimation algorithm, if a value of a first parameter related to a first component mounted on the vehicle is input, the degradation degree of the first component is output. Regarding the second estimation algorithm, if a value of a second parameter related to a second component mounted on the vehicle is input, the degradation degree of the second component is output. The first sensor is configured to detect the value of the first parameter. The second sensor is configured to detect the value of the second parameter. The control device is configured to: perform an autonomous driving performance test based on the vehicle; acquire first data representing the performance of the first component and second data representing the performance of the second component in the performance test; update the first estimation algorithm using the first data acquired in the performance test; and update the second estimation algorithm using the second data acquired in the performance test.

[0025] The vehicle according to the second scheme described above, like the vehicle according to the first scheme described above, can also appropriately update the estimation algorithm installed on the vehicle. Furthermore, in the above configuration, estimation algorithms (first estimation algorithm and second estimation algorithm) are prepared for each of the multiple components (first component and second component). According to the above configuration, the estimation algorithm for each component can be appropriately updated.

[0026] In the vehicle of the second aspect of this disclosure, the first component mounted on the vehicle may be a component for braking the vehicle. The first data representing the performance of the first component may include data representing the braking performance of the vehicle. The second component mounted on the vehicle may be a component for driving the vehicle. The second data representing the performance of the second component may include data representing the acceleration performance of the vehicle.

[0027] Based on the above configuration, the control device uses updated estimation algorithms (first estimation algorithm and second estimation algorithm), thereby easily and accurately estimating the respective deterioration degree of the components used for braking the vehicle and the components used for driving the vehicle. This facilitates the long-term maintenance of appropriate vehicle driving performance.

[0028] The third aspect of this disclosure provides a method for diagnosing the degradation of an onboard component, comprising: performing an autonomous driving performance test based on the vehicle; acquiring data representing the performance of the component during the performance test; and updating an estimation algorithm using the data acquired during the performance test. The estimation algorithm is configured to output the degree of degradation of the component if a value of a parameter related to a component mounted on the vehicle is input.

[0029] Based on the aforementioned method for diagnosing the degradation of vehicle components, the estimation algorithm installed in the vehicle can also be appropriately updated, similar to the method used in the aforementioned vehicle. Furthermore, with the updated estimation algorithm, the degree of degradation of vehicle components can be estimated with high accuracy in the vehicles in use.

[0030] According to this disclosure, the estimation algorithm installed on the vehicle can be updated appropriately. Attached Figure Description

[0031] Hereinafter, with reference to the accompanying drawings, the features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will be described, wherein the same reference numerals denote the same elements, wherein:

[0032] Figure 1 This is a diagram illustrating the schematic configuration of a vehicle according to an embodiment of the present disclosure.

[0033] Figure 2 It means Figure 1 The diagram shows the details of the vehicle's configuration.

[0034] Figure 3 This is a flowchart illustrating the processing procedure of automatic driving control according to an embodiment of the present disclosure.

[0035] Figure 4 This is a diagram illustrating an outline of a method for diagnosing the deterioration of vehicle components according to an embodiment of this disclosure.

[0036] Figure 5 This is a flowchart illustrating the update process of the estimation algorithm in an embodiment of this disclosure.

[0037] Figure 6 It means that it was used Figure 4 The flowchart shows the degradation diagnosis method for on-board components based on the estimation algorithm.

[0038] Figure 7 It means Figure 4 A diagram showing a variation of the first estimation algorithm.

[0039] Figure 8 It means Figure 4 The diagram shows a first variation of the second estimation algorithm.

[0040] Figure 9 It means Figure 4 The figure shows a second variation of the second estimation algorithm. Detailed Implementation

[0041] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that the same or equivalent parts in the drawings are labeled with the same reference numerals, and their descriptions will not be repeated.

[0042] Figure 1 This is a diagram illustrating a schematic configuration of a vehicle according to an embodiment of the present disclosure. (See also...) Figure 1 Vehicle 1 is equipped with an autonomous driving kit (hereinafter referred to as "ADK" 200) and a vehicle platform (hereinafter referred to as "VP" 2).

[0043] VP2 includes the control system of the base vehicle 100 and the vehicle control interface box (hereinafter referred to as "VCIB"). The VCIB 111 can communicate with the ADK200 via an in-vehicle network such as CAN (Controller Area Network). It should be noted that although in... Figure 1 The base vehicle 100 and ADK200 are shown in a separate position, but in reality, ADK200 is mounted on the base vehicle 100. In this embodiment, ADK200 is mounted on the roof of the base vehicle 100. However, the mounting position of ADK200 can be appropriately changed.

[0044] The base vehicle 100 is, for example, a commercially available EV (electric vehicle). An EV is a vehicle that uses electricity as its power source, either entirely or partially. In this embodiment, a BEV (battery electric vehicle) is used as the base vehicle 100. However, it is not limited to this; the base vehicle 100 can also be an EV other than a BEV (HEV (hybrid electric vehicle), PHEV (plug-in hybrid electric vehicle), FCEV (fuel cell electric vehicle), etc.). The base vehicle 100 has, for example, four wheels. However, it is not limited to this; the base vehicle 100 can also have three or fewer wheels, or five or more wheels.

[0045] In addition to the integrated control manager 115, the control system of the base vehicle 100 includes various systems and sensors for controlling the base vehicle 100. The integrated control manager 115 performs integrated control of various systems related to the operation of the base vehicle 100 based on signals (sensor detection signals) from the various sensors included in the base vehicle 100.

[0046] In this embodiment, the integrated control manager 115 includes a control device 150. The control device 150 includes a processor 151, RAM (Random Access Memory) 152, and a storage device 153. The processor 151 may be, for example, a CPU (Central Processing Unit). The RAM 152 functions as a temporary working memory for storing data processed by the processor 151. The storage device 153 is configured to store stored information. The storage device 153 may include, for example, ROM (Read Only Memory) and rewritable non-volatile memory. In addition to the program, the storage device 153 stores information used in the program (e.g., mapping diagrams, formulas, and various parameters). In this embodiment, various vehicle controls are performed by executing the program stored in the storage device 153 via the processor 151. However, vehicle control in the control device 150 is not limited to software-based execution; it can also be performed using dedicated hardware (electronic circuitry). It should be noted that the number of processors in the control device 150 is arbitrary, and processors can be prepared for each specified control.

[0047] The base vehicle 100 includes a braking system 121, a steering system 122, a powertrain system 123, an active safety system 125, and a body system 126. These systems are controlled by a comprehensive control manager 115. In this embodiment, each system has a computer. Furthermore, the computer of each system communicates with the comprehensive control manager 115 via an in-vehicle network (e.g., CAN). Hereinafter, the computer of each system will be referred to as an "ECU (Electronic Control Unit)".

[0048] The braking system 121 includes braking devices for each wheel of the base vehicle 100 and an ECU for controlling the braking devices. In this embodiment, a hydraulic disc brake device is used as the braking device. The base vehicle 100 is equipped with wheel speed sensors 127A and 127B. Wheel speed sensor 127A is located on the front wheels of the base vehicle 100 and detects the rotational speed of the front wheels. Wheel speed sensor 127B is located on the rear wheels of the base vehicle 100 and detects the rotational speed of the rear wheels. The ECU of the braking system 121 outputs the rotational direction and rotational speed of each wheel detected by the wheel speed sensors 127A and 127B to the integrated control manager 115.

[0049] The steering system 122 includes a steering mechanism of the base vehicle 100 and an ECU that controls the steering mechanism. The steering mechanism may include, for example, a rack and pinion EPS (Electric Power Steering) that allows adjustment of the steering angle via an actuator. The base vehicle 100 includes a pinion angle sensor 128. The pinion angle sensor 128 detects the rotation angle (pinion angle) of the pinion gear connected to the rotation shaft of the actuator constituting the steering mechanism. The ECU of the steering system 122 outputs the pinion angle detected by the pinion angle sensor 128 to the integrated control manager 115.

[0050] The powertrain 123 includes: an EPB (Electric Parking Brake) located on at least one of the wheels of the base vehicle 100; a P-Lock device located on the transmission of the base vehicle 100; a shift mechanism configured to select a gear; a drive source for the base vehicle 100; and an ECU for controlling the various devices included in the powertrain 123. The EPB is separate from the aforementioned braking device and uses an electric actuator to fix the wheels. The P-Lock device, for example, uses a parking lock pawl that can be driven by an actuator to fix the rotational position of the transmission output shaft. Although details will be described later, in this embodiment, a motor supplied with power from a battery (see [reference]) is used. Figure 4The powertrain system 123's ECU serves as the driving source for the base vehicle 100. The integrated control manager 115 outputs information to the integrated control manager, including the presence or absence of the EPB and P-Lock devices, the gear selected by the shifting device, and the respective states of the battery and motor.

[0051] The active safety system 125 includes an ECU that determines the likelihood of a collision with the vehicle 1 while it is in motion. The base vehicle 100 is equipped with a camera 129A and radar sensors 129B and 129C that detect the surrounding conditions, including those in front of and behind the vehicle 1. The ECU of the active safety system 125 uses signals received from the camera 129A and radar sensors 129B and 129C to determine whether a collision is possible. If the active safety system 125 determines that a collision is possible, the integrated control manager 115 outputs a braking command to the braking system 121 to increase the braking force of the vehicle 1. The base vehicle 100 of this embodiment is equipped with the active safety system 125 from the outset (at the factory). However, the active safety system 125 is not limited to this; an active safety system that can be retrofitted to the base vehicle 100 may also be used.

[0052] The body system 126 includes body system components (e.g., turn indicators, horn, and wipers) and an ECU that controls the body system components. In manual mode, the ECU of the body system 126 controls the body system components according to user operation; in autonomous mode, it controls the body system components according to instructions received from the ADK200 via the VCIB111 and the integrated control manager 115.

[0053] Vehicle 1 is configured for autonomous driving. VCIB111 functions as the vehicle control interface. When Vehicle 1 is driving autonomously, the integrated control manager 115 and ADK200 exchange signals via VCIB111, and the integrated control manager 115 executes driving control (i.e., autonomous driving control) in Autonomous Mode according to instructions from ADK200. It should be noted that ADK200 can also be removed from the base vehicle 100. Even with ADK200 removed, the base vehicle 100 can still be driven independently by the user. When driving independently, the control system of the base vehicle 100 executes driving control (i.e., driving control corresponding to user operation) in Manual Mode.

[0054] In this embodiment, ADK200 exchanges signals with VCIB111 according to an API (Application Program Interface) that defines each communicated signal. ADK200 is configured to process various signals defined by the aforementioned API. For example, ADK200 creates a driving plan for vehicle 1 and outputs various commands to VCIB111 according to the aforementioned API, requesting control to make vehicle 1 drive according to the created driving plan. Hereinafter, each of the various commands output from ADK200 to VCIB111 will be referred to as an "API command". Furthermore, ADK200 receives various signals representing the state of the base vehicle 100 from VCIB111 according to the aforementioned API and reflects the received state of the base vehicle 100 in the creation of the driving plan. Hereinafter, each of the various signals received by ADK200 from VCIB111 will be referred to as an "API signal". API commands and API signals are examples of signals defined by the aforementioned API. Details regarding the configuration of ADK200 will be described later (see [link to documentation]). Figure 2 ).

[0055] VCIB111 receives various API commands from ADK200. When an API command is received from ADK200, VCIB111 converts the API command into a signal form that can be processed by the integrated control manager 115. Hereinafter, the API command converted into a signal form that can be processed by the integrated control manager 115 will also be referred to as a "control command". When an API command is received from ADK200, VCIB111 outputs the corresponding control command to the integrated control manager 115.

[0056] The control unit 150 of the integrated control manager 115 sends various signals (e.g., sensor signals or status signals) indicating the state of the base vehicle 100 detected in the control system of the base vehicle 100 via VCIB 111 to ADK 200. VCIB 111 sequentially receives signals indicating the state of the base vehicle 100 from the integrated control manager 115. VCIB 111 determines the value of the API signal based on the signals received from the integrated control manager 115. Furthermore, VCIB 111 converts the signals received from the integrated control manager 115 into API signal form as needed. Then, VCIB 111 outputs the obtained API signal to ADK 200. API signals indicating the state of the base vehicle 100 are sequentially output from VCIB 111 to ADK 200 in real time.

[0057] In this embodiment, signals with low universality, defined by the automobile manufacturer, are exchanged between the integrated control manager 115 and VCIB 111, while signals with higher universality (e.g., signals defined through a publicly available API) are exchanged between ADK 200 and VCIB 111. VCIB 111 enables the integrated control manager 115 to control the vehicle according to instructions from ADK 200 by performing signal conversion between ADK 200 and the integrated control manager 115. However, the function of VCIB 111 is not limited to performing the aforementioned signal conversion. For example, VCIB 111 can also make a predetermined judgment and send a signal based on the judgment result to at least one of the integrated control manager 115 and ADK 200 (e.g., a signal for notification, instruction, or request). Details regarding the configuration of VCIB 111 will be described later (see [reference]). Figure 2 ).

[0058] The base vehicle 100 also includes a communication device 130. The communication device 130 includes various communication I / Fs (interfaces). The control device 150 is configured to communicate with external devices of the vehicle 1 (e.g., the mobile terminal UT and server 500 described later) via the communication device 130. The communication device 130 includes a wireless communication device (e.g., a DCM (Data Communication Module)) capable of accessing a mobile communication network (telematics). The communication device 130 communicates with the server 500 via the mobile communication network. The wireless communication device may also include a 5G (fifth-generation mobile communication system) corresponding communication I / F. Furthermore, the communication device 130 includes a communication I / F for direct communication with the mobile terminal UT located within or around the vehicle. The communication device 130 and the mobile terminal UT can also perform short-range communication such as wireless LAN (Local Area Network), NFC (Near Field Communication), or Bluetooth (registered trademark).

[0059] The mobile terminal UT is a terminal carried by the user of vehicle 1. In this embodiment, a smartphone with a touch panel display is used as the mobile terminal UT. However, it is not limited to this; any mobile terminal can be used as the mobile terminal UT, such as a laptop computer, tablet computer, wearable device (e.g., smartwatch or smart glasses), or electronic key.

[0060] The aforementioned vehicle 1 can be adopted as one of the components of a MaaS (Mobility as a Service) system. MaaS systems include, for example, MSPF (Mobility Service Platform). MSPF is a unified platform connecting various mobility services (e.g., various mobility services provided by ride-sharing providers, car-sharing providers, insurance companies, rental car providers, taxi providers, etc.). Server 500 is a computer in MSPF that manages and exposes information used for mobility services. Server 500 manages information on various mobility services and provides information (e.g., APIs and information related to cooperation between mobility services) based on requests from providers. Providers of services can utilize the various functionalities provided by MSPF using the APIs exposed on MSPF. For example, the APIs required for the development of ADK are exposed on MSPF.

[0061] Figure 2 This is a diagram showing the details of the configuration of vehicle 1. (Refer to...) Figure 1 and Figure 2 ADK200 includes an autonomous driving system (hereinafter referred to as "ADS").202 for performing autonomous driving of vehicle 1. ADS202 includes a computer 210, an HMI (Human Machine Interface) 230, a recognition sensor 260, a posture sensor 270, and a sensor cleaner 290.

[0062] Computer 210 includes a processor and a storage device storing autonomous driving software utilizing APIs, and is configured to execute the autonomous driving software via the processor. The autonomous driving software performs controls related to autonomous driving (see below). Figure 3 The autonomous driving software can be updated sequentially using OTA (Over-The-Air) technology. Computer 210 also includes communication modules 210A and 210B.

[0063] HMI 230 is a device for exchanging information between a user and computer 210. HMI 230 includes input devices and reporting devices. The user can use HMI 230 to give instructions or make requests to computer 210, or to change the values ​​of parameters used in autonomous driving software (however, only permissible parameters). HMI 230 may be a touch panel display that functions as both an input device and a reporting device.

[0064] The identification sensor 260 includes various sensors that acquire information (hereinafter also referred to as "environmental information") for identifying the external environment of the vehicle 1. The identification sensor 260 acquires the environmental information of the vehicle 1 and outputs the environmental information to the computer 210. The environmental information is used for autonomous driving control. In this embodiment, the identification sensor 260 includes: a camera that captures images of the area around the vehicle 1 (including the front and rear); and an obstacle sensor (e.g., millimeter-wave radar and / or lidar) that senses obstacles via electromagnetic waves or sound waves. The computer 210 can, for example, use the environmental information received from the identification sensor 260 to identify people, objects (other vehicles, pillars, guardrails, etc.) and lines on the road (e.g., center lines) that are within the range that can be identified from the vehicle 1. Artificial intelligence (AI) or an image processing processor may also be used for identification.

[0065] The attitude sensor 270 acquires information related to the attitude of vehicle 1 (hereinafter also referred to as "attitude information") and outputs this information to computer 210. The attitude sensor 270 includes various sensors that detect the acceleration, angular velocity, and position of vehicle 1. In this embodiment, the attitude sensor 270 includes an IMU (Inertial Measurement Unit) and a GPS (Global Positioning System) sensor. The IMU detects the accelerations of vehicle 1 in the forward, left, right, and up / down directions, and the angular velocities of vehicle 1 in the roll, pitch, and yaw directions. The GPS sensor uses signals received from multiple GPS satellites to detect the position of vehicle 1. In the fields of automobiles and aircraft, techniques for combining IMUs and GPS to measure attitude with high accuracy are well known. Computer 210 can, for example, utilize such known techniques to measure the attitude of vehicle 1 based on the aforementioned attitude information.

[0066] Sensor cleaner 290 is a device for removing dirt from sensors (e.g., identification sensor 260) exposed to outside air outside the vehicle. For example, sensor cleaner 290 can be configured to clean camera lenses and obstacle sensor nozzles using cleaning fluid and a wiper.

[0067] In vehicle 1, redundancy is provided for specified functions (e.g., braking, steering, and vehicle fixation). The control system 102 of the base vehicle 100 includes multiple systems that perform equivalent functions. Specifically, the braking system 121 includes braking systems 121A and 121B. The steering system 122 includes steering systems 122A and 122B. The powertrain system 123 includes EPB system 123A and P-Lock system 123B. Each system has an ECU. Even if one of the multiple systems performing equivalent functions malfunctions, the other will still operate normally, thus ensuring that the function functions normally in vehicle 1.

[0068] VCIB111 includes VCIB111A and VCIB111B. Each of VCIB111A and VCIB111B includes a computer. Communication modules 210A and 210B of computer 210 are configured to communicate with the computers of VCIB111A and VCIB111B, respectively. VCIB111A and VCIB111B are connected in a manner that allows them to communicate with each other. Each of VCIB111A and VCIB111B can operate independently. Even if one of VCIB111A and VCIB111B malfunctions, the other will operate normally, thus ensuring the normal operation of VCIB111. Both VCIB111A and VCIB111B are connected to the aforementioned systems via integrated control manager 115. However, as... Figure 2 As shown, the connection destinations differ in parts of VCIB111A and VCIB111B.

[0069] In this embodiment, the function of accelerating vehicle 1 is not redundant. The powertrain 123 includes a propulsion system 123C as a system for accelerating vehicle 1.

[0070] Vehicle 1 is configured to switch between autonomous and manual modes. The API signals received by ADK200 from VCIB111 include a signal indicating whether Vehicle 1 is in autonomous or manual mode (hereinafter referred to as "autonomous mode"). The user can select either autonomous or manual mode via a designated input device (e.g., HMI230 or mobile terminal UT). When the user selects a driving mode, Vehicle 1 enters the selected driving mode, and the selection result is reflected in the autonomous mode. However, if Vehicle 1 is not in a state capable of autonomous driving, it will not switch to autonomous mode even if the user selects it. Switching between driving modes of Vehicle 1 can also be performed by the integrated control manager 115. The integrated control manager 115 can also switch between autonomous and manual modes based on the vehicle's status.

[0071] When vehicle 1 is in autonomous mode, computer 210 obtains the state of vehicle 1 from VP2 and sets the next action of vehicle 1 (e.g., acceleration, deceleration, and turning). Then, computer 210 outputs various instructions to implement the set next action of vehicle 1. By executing API software (i.e., autonomous driving software utilizing API) through computer 210, instructions related to autonomous driving control are sent from ADK200 to integrated control manager 115 via VCIB111.

[0072] Figure 3 This is a flowchart illustrating the processes performed by ADK200 in the autonomous driving control of this embodiment. The processes shown in this flowchart are repeatedly executed at a cycle corresponding to the API (API cycle) when vehicle 1 is in autonomous mode. Hereinafter, each step in the flowchart will be simply referred to as "S".

[0073] Reference Figure 1 , Figure 2 as well as Figure 3 In S101, computer 210 acquires current vehicle 1 information. For example, computer 210 acquires environmental and posture information of vehicle 1 from recognition sensor 260 and posture sensor 270. Furthermore, computer 210 acquires API signals. In this embodiment, when vehicle 1 is in either autonomous or manual mode, API signals representing the state of vehicle 1 are also sequentially output from VCIB111 to ADK200 in real time. The API signals acquired by computer 210 include, in addition to the aforementioned autonomous state, signals representing the rotation direction and speed of each wheel detected by wheel speed sensors 127A and 127B. When the autonomous state indicates manual mode, Figure 3 The series of processes shown has ended.

[0074] In S102, computer 210 creates a driving plan based on the information about vehicle 1 obtained in S101. For example, computer 210 calculates the behavior of vehicle 1 (e.g., the posture of vehicle 1) and creates a driving plan suitable for the state of vehicle 1 and the external environment. The driving plan is data representing the behavior of vehicle 1 within a specified period. If a driving plan already exists, it can also be modified in S102.

[0075] In S103, computer 210 extracts the physical quantities (acceleration, tire angle, etc.) required for vehicle control from the driving plan created in S102. In S104, computer 210 divides the physical quantities extracted in S103 into segments for each API cycle. In S105, computer 210 uses the physical quantities segmented in S104 to execute the API software. Thus, through the execution of the API software, API commands (propulsion direction command, propulsion command, braking command, vehicle fixation command, etc.) requesting control of the physical quantities according to the driving plan are sent from ADK200 to VCIB111. VCIB111 sends control commands corresponding to the received API commands to the integrated control manager 115, and the integrated control manager 115 performs automatic driving control of vehicle 1 according to the control commands.

[0076] The autonomous driving of vehicle 1 is performed by repeatedly executing the processes S101 to S105 described above. In this embodiment, it is assumed that autonomous driving of vehicle 1 is performed when vehicle 1 is occupied. However, it is not limited to this and can also be set to perform autonomous driving of vehicle 1 when vehicle 1 is unoccupied.

[0077] In this embodiment, vehicle 1 is configured to use an estimation algorithm installed on vehicle 1 to perform degradation diagnosis of on-board components. Figure 4 This is a diagram used to illustrate an overview of the deterioration diagnosis method for vehicle-mounted components according to this embodiment.

[0078] Reference Figure 1 , Figure 2 as well as Figure 4 The braking system 121 includes a hydraulic disc brake device 10. The hydraulic disc brake device 10 has a brake mechanism and a brake actuator. The hydraulic disc brake device 10 uses hydraulic pressure adjusted by the brake actuator to drive the brake mechanism.

[0079] Specifically, the braking mechanism includes: a brake caliper, fixed to the vehicle body; and a brake rotor, fixed to the wheel and rotating integrally with the wheel. The brake caliper has a wheel cylinder and a brake pad. The wheel cylinder is operated by the pressure (i.e., hydraulic pressure) of brake fluid supplied from the brake actuator. By operating the wheel cylinder, the brake pad is pressed against the brake rotor, thereby generating a frictional braking force. The brake pad is an example of a component used to brake vehicle 1. The higher the hydraulic pressure applied to the wheel cylinder, the greater the frictional braking force. There is a tendency that the smaller the remaining groove of the brake pad, the smaller the frictional braking force generated by the brake pad. The remaining groove of the brake pad will decrease due to wear. As the deterioration (e.g., wear) of the brake pad progresses, even with the same hydraulic pressure, the resulting frictional braking force will decrease.

[0080] The brake actuator includes: a hydraulic circuit that supplies hydraulic pressure from the master cylinder to each wheel cylinder of the four wheels; control valves (e.g., pressure reducing valves) located in each hydraulic circuit; and a pump for hydraulic adjustment (e.g., a pressurizing pump).

[0081] Braking system 121 includes hydraulic sensors 11 and 12. Detection results obtained by hydraulic sensors 11 and 12 are output to braking systems 121A and 121B, respectively. Hydraulic sensors 11 and 12 are configured to detect the hydraulic pressure of the master cylinder and wheel cylinders, respectively. By controlling the control valves and pumps of the brake actuators through the ECU included in each of braking systems 121A and 121B, the hydraulic pressure applied to each wheel cylinder (and, in other words, the braking force on each wheel) can be adjusted. In this embodiment, the brake pads constituting the hydraulic disc brake device 10 are an example of the "first component" of this disclosure. Furthermore, hydraulic sensors 11 and 12 are examples of the "first sensors" of this disclosure.

[0082] Vehicle 1 is equipped with a battery 160 that supplies power to the propulsion system 123C. Known vehicle energy storage devices (e.g., liquid secondary batteries, solid-state secondary batteries, or battery packs) can be used as battery 160. Examples of vehicle secondary batteries include lithium-ion batteries and nickel-metal hydride batteries.

[0083] The propulsion system 123C includes an MG (Motor Generator) 20, an MG sensor 20a for detecting the state of the MG 20, an ECU 21, and a PCU (Power Control Unit) 22. The propulsion system 123C uses electricity stored in the battery 160 to generate the driving force for the vehicle 1. The MG 20 is, for example, a three-phase AC motor generator. The PCU 22 includes, for example, an inverter, a converter, and a relay (hereinafter referred to as "SMR (System Main Relay)"). The PCU 22 is controlled by the ECU 21. The SMR is configured to switch the connection / disconnection of the circuit from the battery 160 to the MG 20. The SMR is in a closed state (connected state) when the vehicle 1 is in motion.

[0084] MG20 is driven by PCU22, causing the drive wheels of vehicle 1 to rotate. Furthermore, MG20 regenerates electricity and supplies the generated power to battery 160. PCU22 uses the power supplied from battery 160 to drive MG20. PCU22 drives MG20 based on the power value (e.g., current value) indicated by ECU21. In this embodiment, the drive voltage of MG20 (the voltage used to drive MG20) is maintained approximately constant. The greater the drive current of MG20 (the current used to drive MG20), the greater the force exerted by MG20 on vehicle 1 (the force that accelerates vehicle 1). MG sensor 20a is configured to detect the drive current, drive voltage, and temperature of MG20, respectively. The detection results obtained by MG sensor 20a are output to ECU21.

[0085] MG20 is an example of a component used to drive vehicle 1. In this embodiment, vehicle 1 includes one MG20. However, the number of driving motors (MG20) included in vehicle 1 is arbitrary, and can be two or more. The driving motor can also be an in-wheel motor. In this embodiment, MG20 is an example of a "second component" of this disclosure. Furthermore, MG sensor 20a is an example of a "second sensor" of this disclosure.

[0086] The control unit 150 included in the integrated control manager 115 is configured to communicate with each of the ECUs of the braking system 121A, the braking system 121B, and the propulsion system 123C. A first estimation algorithm E1 and a second estimation algorithm E2 are stored in the storage device 153 of the control unit 150. The first estimation algorithm E1 is prepared for each brake pad constituting the hydraulic disc brake device 10. In this embodiment, the vehicle 1 has four brake pads, therefore the storage device 153 stores four first estimation algorithms E1. Furthermore, the storage device 153 stores one second estimation algorithm E2 corresponding to MG20. However, in a configuration where the vehicle 1 has multiple driving motors, the second estimation algorithm E2 can also be prepared for each driving motor.

[0087] The first estimation algorithm E1 represents the relationship between the hydraulic level (first degradation parameter) associated with the corresponding brake pad and the degree of degradation of the brake pad. For a given brake pad, when the hydraulic level value is input, the first estimation algorithm E1 outputs the degree of degradation of the brake pad.

[0088] The second estimation algorithm E2 represents the relationship between the driving current (second degradation parameter) of the corresponding MG20 and the degradation degree of the MG20. For the corresponding MG20, when the value of the driving current (the current used to drive the MG20) is input, the second estimation algorithm E2 outputs the degradation degree of the MG20.

[0089] In this embodiment, algorithms implemented using AI (Artificial Intelligence) are employed as the aforementioned estimation algorithms. These estimation algorithms may also be machine learning models that have been trained using the large datasets possessed by server 500 (e.g., data measured in vehicles with the same technical specifications as vehicle 1). However, this is not a limitation; the aforementioned estimation algorithms may also be rule-based algorithms. For example, the aforementioned estimation algorithms may also be formulas or mapping graphs.

[0090] The control unit 150 is configured to perform automatic driving of vehicle 1 during a predetermined period (hereinafter referred to as "operation period"). Vehicle 1 may also provide predetermined services (e.g., logistics services or passenger transport services) via automatic driving during operation period. The automatic driving of vehicle 1 is performed... Figure 3 The process shown involves the control unit 150 controlling various systems of the vehicle 1 according to instructions from the ADK200 (e.g., Figure 2 The braking system 121, steering system 122, power transmission system 123, active safety system 125, and body system 126 are shown.

[0091] Furthermore, the control device 150 also performs autonomous driving of the vehicle 1 for performance testing of the on-board components. The control device 150 is configured to: perform performance testing of autonomous driving based on the vehicle 1, and acquire first data representing the performance of each brake pad constituting the hydraulic disc brake device 10 and second data representing the performance of the MG20 during the performance test. The control device 150 is configured to: update the first estimation algorithm E1 using the first data acquired during the performance test, and update the second estimation algorithm E2 using the second data acquired during the performance test. Hereinafter, using... Figure 5 The update process of the estimation algorithm is explained.

[0092] Figure 5 This is a flowchart illustrating the update process of the estimation algorithm. For example, under specified conditions, when the control system of vehicle 1 (including...) Figure 2 When the control system 102, VCIB111, and ADS202 shown are started, they undergo a series of processes illustrated in the flowchart. In this embodiment, if the control system of vehicle 1 is started during a break in operation, then... Figure 5 The series of processes shown begins. For example, if the control device 150 is set with a first operating period and a second operating period, and the vehicle 1's control system is activated during the period from the end of the first operating period to the start of the second operating period, then... Figure 5The processing shown begins. The second operating period is the next operating period after the first operating period. On the other hand, even if the control system of vehicle 1 is started during either the first or the second operating period, Figure 5 The processing shown does not begin.

[0093] It should be noted that the start / stop functions of VCIB111 and ADS202 can also be switched in conjunction with the start / stop function of the control system 102 of the base vehicle 100. In this embodiment, the start / stop function of the control system 102 of the base vehicle 100 is switched in conjunction with the ON / OFF operation of the start switch of the base vehicle 100 operated by the user. The start switch of the base vehicle 100 is generally referred to as a "power switch" or "ignition switch," etc.

[0094] Reference Figures 1-4 as well as Figure 5 In S11, the control device 150 requests the ADK200 to perform a first calibration for the autonomous driving of the vehicle 1. This first calibration is a separate calibration for the ADK200. Upon receiving the request, the computer 210 of the ADK200 performs adjustments, for example, to the recognition sensor 260 (camera, lidar, etc.). If a parameter subject to adjustment (e.g., the field of view of the recognition sensor 260) deviates from its normal range, the computer 210 may perform the adjustment automatically or prompt the user to do so.

[0095] In S12, the control device 150 performs a performance test of the autonomous driving system based on the vehicle 1. Specifically, the control device 150 executes the autonomous driving system of the vehicle 1 for the performance test by requesting the ADK200 to perform the test. Then, the control device 150 acquires data representing the performance of specified components during the performance test. In this embodiment, the brake pads constituting the hydraulic disc brake device 10 and MG20 are examples of the specified components described above.

[0096] The control unit 150 executes automated driving control for performance testing according to instructions from the ADK200. Specifically, the control unit 150 performs prescribed acceleration control in the stationary vehicle 1, measures prescribed data (hereinafter also referred to as "acceleration data") during the acceleration of vehicle 1, and then stabilizes vehicle 1 under prescribed conditions. Then, the control unit 150 performs prescribed braking control to bring vehicle 1 to a stop while it is stabilizing. At this time, the control unit 150 measures prescribed data (hereinafter also referred to as "braking data") during the deceleration and stopping of vehicle 1.

[0097] In the aforementioned acceleration control, a propulsion command (hereinafter also referred to as a "propulsion test signal") instructing vehicle 1 to initiate a specified acceleration performance is sent from ADK200 to the integrated control manager 115 via VCIB111. The aforementioned acceleration data includes data representing the acceleration performance of vehicle 1 (hereinafter also referred to as "acceleration performance data") and the drive current of MG20. The measured acceleration data is stored in storage device 153.

[0098] In this embodiment, the time from when vehicle 1 begins to accelerate from a stationary state until it reaches a predetermined speed (e.g., 100 km / h or 60 mph) (hereinafter also referred to as the "first acceleration time") is measured as acceleration performance data. Alternatively, the acceleration performance data may also include the weight of vehicle 1. The shorter the first acceleration time, the higher the acceleration performance of vehicle 1. However, even with the same acceleration performance, the greater the weight of vehicle 1, the more difficult it is to accelerate. The control device 150 estimates the degradation degree of MG20 based on the acceleration performance data (e.g., the first acceleration time). Specifically, the control device 150 estimates that the lower the acceleration performance of vehicle 1, the greater the degradation degree of MG20. Then, the control device 150 records the estimated degradation degree of MG20 and the drive current of MG20 in association in the storage device 153.

[0099] The acceleration performance data and the drive current of the MG20 mentioned above are examples of data representing the performance of the MG20. The better the motor's performance, the less drive current is required to achieve the same acceleration performance. The drive current of the MG20 is controlled by the MG sensor 20a (…). Figure 4 To measure.

[0100] It should be noted that the acceleration performance data mentioned above may also include the time from when vehicle 1 starts accelerating from a stationary state until it travels a specified distance (hereinafter also referred to as the "second acceleration time") instead of the first acceleration time, or the acceleration performance data may include the second acceleration time in addition to the first acceleration time. The shorter the second acceleration time, the higher the acceleration performance of vehicle 1.

[0101] In the aforementioned braking control, a braking command (hereinafter also referred to as a "brake test signal") instructing vehicle 1 to initiate a specified braking performance is sent from ADK200 to the integrated control manager 115 via VCIB111. The aforementioned braking data includes data representing the braking performance of vehicle 1 (hereinafter also referred to as "brake performance data") and the hydraulic pressure level of each brake pad. The measured braking data is stored in storage device 153.

[0102] In this embodiment, the braking distance (i.e., the distance traveled from the moment vehicle 1 begins braking until it comes to a stop) is measured as braking performance data. Alternatively, the braking performance data may also include the weight of vehicle 1. The shorter the braking distance, the higher the braking performance of vehicle 1. However, even with the same braking performance, the greater the weight of vehicle 1, the more difficult it is for vehicle 1 to decelerate. The control device 150 estimates the degree of deterioration of the brake pads based on the braking performance data (e.g., braking distance). Specifically, the control device 150 estimates that the lower the braking performance of vehicle 1, the greater the degree of deterioration of the brake pads. Then, the control device 150 records the estimated degree of deterioration of the brake pads and the hydraulic pressure level of the brake pads in a correlation in the storage device 153.

[0103] The aforementioned braking performance data and the hydraulic pressure level of the brake pads (i.e., the hydraulic pressure applied to the wheel cylinders) are examples of data representing the performance of the brake pads. The better the performance of the brake pads, the lower the hydraulic pressure level required to achieve the same braking performance. The hydraulic pressure level of each brake pad is measured by hydraulic sensors 11 and 12 (…). Figure 4 The braking data is measured using at least one of the following sensors: (1) brake system 121A and hydraulic sensor 11. In this embodiment, if no abnormality occurs in either the brake system 121A or the hydraulic sensor 11, the detection value obtained by the hydraulic sensor 11 is used as the braking data. Furthermore, if an abnormality occurs in either the brake system 121A or the hydraulic sensor 11, the detection value obtained by the hydraulic sensor 12 is used as the braking data.

[0104] It should be noted that the above braking performance data may also include the deceleration of vehicle 1 at the start of braking instead of the braking distance, or, in addition to the braking distance, the above braking performance data may also include the deceleration of vehicle 1 at the start of braking. The greater the deceleration of vehicle 1 at the start of braking, the higher the braking performance of vehicle 1.

[0105] In the next step, S13, the control unit 150 sends the results of the performance test in S12 (including measured acceleration performance data and braking performance data) to the ADK200 and requests the ADK200 to perform a second calibration for autonomous driving of vehicle 1. The second calibration is related to the exchange of signals between the base vehicle 100 (integrated control manager 115) and the ADK200. Upon receiving the request, the computer 210 of the ADK200 performs adjustments to match the behavior of vehicle 1 with commands related to autonomous driving (e.g., propulsion commands or braking commands). Specifically, if the acceleration performance obtained through the propulsion test signal deviates from the normal range, the computer 210 adjusts the propulsion command in a manner that achieves appropriate acceleration performance. Similarly, if the braking performance obtained through the braking test signal deviates from the normal range, the computer 210 adjusts the braking command in a manner that achieves appropriate braking performance. Thus, the control unit 150 is configured to perform autonomous driving calibration for vehicle 1 using data obtained through the performance test. With the first and second calibrations performed, the preparation for autonomous driving by the ADK200 is completed.

[0106] In the next step, S14, the control device 150 uses the results of the performance test in S12 to perform a degradation diagnosis on each brake pad and MG20 constituting the hydraulic disc brake device 10. This degradation diagnosis determines whether a component needs to be replaced. If the acceleration performance measured in the performance test is lower than a specified level (first level), the control device 150 determines that it is time to replace the MG20 (YES in S14). Then, the control device 150 proceeds to S19. Furthermore, if the braking performance measured in the performance test is lower than a specified level (second level), the control device 150 also determines that it is time to replace one of the brake pads constituting the hydraulic disc brake device 10 (YES in S14). Then, the control device 150 proceeds to S19.

[0107] In S19, control device 150 performs a prescribed replacement process. This process includes recording, reporting, and sending diagnostic results. The diagnostic results include information indicating which component (e.g., brake pad or MG20) needs replacement. The diagnostic results may also indicate which of the brake pads and MG20 needs replacement. Alternatively, control device 150 may determine which brake pad constituting the hydraulic disc brake device 10 needs replacement based on the hydraulic pressure level of each brake pad and append this determination to the aforementioned diagnostic results. Control device 150 may also record the diagnostic results in storage device 153. Control device 150 may also cause a prescribed reporting device (e.g., HMI230 or mobile terminal UT) to report the diagnostic results. Control device 150 may also send the diagnostic results to server 500. Alternatively, control device 150 may disable vehicle 1 in S19 and perform a process for arranging a replacement vehicle. For example, control device 150 may also delegate vehicle arrangement to server 500. When the process in S19 is executed, Figure 5 The series of processes shown has ended.

[0108] In S14 above, if the control device 150 determines that it is not necessary to replace any of the brake pads and MG20 constituting the hydraulic disc brake device 10 (in S14, this is "NO"), the process proceeds to S15. In S15, the control device 150 uses the results of the current performance test and the results of previously executed performance tests to update the first estimation algorithm E1 and the second estimation algorithm E2, respectively.

[0109] Specifically, the performance test results recorded in storage device 153 show the relationship between the deterioration degree of the brake pad and the hydraulic pressure level of the brake pad (hereinafter referred to as the "first relationship") and the relationship between the deterioration degree of MG20 and the drive current of MG20 (hereinafter referred to as the "second relationship"). Control device 150 uses the first relationship measured in the performance test to correct the first estimation algorithm E1 in a manner that the estimation result obtained by the first estimation algorithm E1 is close to the measured data. Furthermore, control device 150 uses the second relationship measured in the performance test to correct the second estimation algorithm E2 in a manner that the estimation result obtained by the second estimation algorithm E2 is close to the measured data. Control device 150 may also use known learning techniques (regression analysis, k-nearest neighbors algorithm, decision tree, clustering, Q-learning, etc.) to update the algorithm. In rule-based algorithm updates, for example, least squares may also be used.

[0110] In the next step, S16, the control device 150 determines whether the number of times the processing in S15 has been executed (i.e., the number of times the estimation algorithm has been updated) has exceeded a predetermined number. The number of times the estimation algorithm has been updated is the cumulative number of times since the vehicle 1 became the current technical specification. For example, if the technical specification of the vehicle 1 is changed to the current technical specification through modification, the number of times the estimation algorithm has been updated (cumulative number) from the time of modification to the present is equivalent to the aforementioned number of times the estimation algorithm has been updated. The predetermined number can be arbitrarily set. The predetermined number can be approximately 3 to 10 times, or it can be more than 10 times.

[0111] If the number of updates to the estimation algorithm exceeds the predetermined number ("Yes" in S16), the control device 150 sets the estimation flag to ON in S171. If the number of updates to the estimation algorithm is less than the predetermined number ("No" in S16), the control device 150 sets the estimation flag to OFF in S172. The estimation flag is stored in the storage device 153. When the estimation flag is ON, estimations performed by each of the first estimation algorithm E1 and the second estimation algorithm E2 are allowed. When the estimation flag is OFF, estimations performed by each of the first estimation algorithm E1 and the second estimation algorithm E2 are prohibited. When the estimation flag is set in either S171 or S172, Figure 5 The series of processes shown has ended.

[0112] When the estimation flag is enabled, the control device 150 executes estimations performed by each of the first estimation algorithm E1 and the second estimation algorithm E2 during operation. It should be noted that during operation, the control device 150 performs autonomous driving of the vehicle 1 according to instructions from the ADK200.

[0113] Figure 6 This is a flowchart illustrating a degradation diagnosis method for vehicle components using an estimation algorithm. The processing shown in the flowchart is performed on a per-component basis. For example, when the control device 150 receives a predetermined braking command (more specifically, the same command as the braking test signal described above) from the ADK200 during the operation of the control device 150, the control device 150 performs the following operations on each brake pad constituting the hydraulic disc brake device 10. Figure 6 The processing is shown. Furthermore, when the control device 150 receives a predetermined propulsion command (more specifically, the same command as the propulsion test signal described above) from the ADK200 during the operation of the control device 150, the control device 150 performs the following operations on the MG20: Figure 6 The processing shown.

[0114] Reference Figures 1-4 as well as Figure 6 In S21, control device 150 determines whether the presumption flag is enabled. The presumption flag is activated before the start of operation of vehicle 1. Figure 5 The process shown (S171 or S172) is used to set the parameters. If the presumption flag is off ("No" in S21), the processes after S22 are not executed. Figure 6 The series of processes ends. If the presumption flag is enabled ("Yes" in S21), the process proceeds to S22.

[0115] In S22, the control unit 150 inputs the values ​​of degradation parameters detected by the on-board sensors to the estimation algorithm. For example, in the processing related to brake pads, the control unit 150 inputs the hydraulic level of the brake pad detected by the hydraulic sensor 11 to the first estimation algorithm E1 corresponding to that brake pad. The hydraulic sensor 12 can also be used instead of the hydraulic sensor 11. The processing in S22 is performed for each brake pad. Furthermore, in the processing related to MG20, the control unit 150 inputs the drive current of MG20 detected by the MG sensor 20a to the second estimation algorithm E2 corresponding to MG20.

[0116] In S23, the control device 150 uses the component's degradation degree output from the estimation algorithm to perform a degradation diagnosis of the component. This degradation diagnosis determines whether the component needs to be replaced. For example, in the processing related to brake pads, the control device 150 determines whether the degradation degree of the brake pad output from the first estimation algorithm E1 exceeds a predetermined threshold (first threshold). If the degradation degree of the brake pad exceeds the first threshold, the control device 150 determines that it is time to replace the brake pad ("yes" in S23) and proceeds to S24. The processing in S23 is performed for each brake pad. Furthermore, in the processing related to MG20, the control device 150 determines whether the degradation degree of MG20 output from the second estimation algorithm E2 exceeds a predetermined threshold (second threshold). If the degradation degree of MG20 exceeds the second threshold, the control device 150 determines that it is time to replace MG20 ("yes" in S23) and proceeds to S24.

[0117] In S24, control device 150 performs the prescribed replacement procedure. In S24, with... Figure 5 Similarly, in S19, at least one of the following can be performed: recording, reporting, and sending diagnostic results. In S24, the control device 150 may also perform arrangements for interrupting autonomous driving (the operation of vehicle 1) and replacing the vehicle.

[0118] In S23 above, if it is determined that it is not necessary to replace any of the brake pads and MG20 constituting the hydraulic disc brake device 10 (in S23, "No"), Figure 6 The series of processes shown has ended. As described above, during the operation of vehicle 1, the control device 150 determines whether the degradation degree of the on-board components exceeds a predetermined threshold based on the output of the estimation algorithm. With this configuration, it is easy to replace the on-board components at appropriate intervals.

[0119] As explained above, the degradation diagnosis method for vehicle-mounted components in this embodiment includes... Figure 5 and Figure 6 The processing shown. In Figure 5 In S12, vehicle 1 performs a performance test based on autonomous driving and acquires data representing the performance of components during the performance test. Figure 5 In S15, vehicle 1 uses data obtained from performance testing to update the estimation algorithm. Figure 6 In steps S22 and S23, vehicle 1 uses the updated estimation algorithm to estimate the degree of component degradation. Based on this method for diagnosing the degradation of on-board components, the estimation algorithm installed in vehicle 1 can be appropriately updated. Furthermore, the updated estimation algorithm can estimate the degree of degradation of on-board components in vehicle 1 with high accuracy.

[0120] The braking device included in the braking system 121 is not limited to a hydraulic disc brake. An electric service brake may also be used instead of a hydraulic disc brake. Furthermore, the following description may also be stored in the storage device 153. Figure 7 The first estimation algorithm E1A shown is used to replace Figure 4 The first estimation algorithm E1 is shown.

[0121] Figure 7 This is a diagram illustrating a variation of the first estimation algorithm E1. (Refer to...) Figure 7 Regarding the first estimation algorithm E1A, if the drive current of the electric motor (a motor that replaces the hydraulic pump to push the piston of the master cylinder) constituting the electric service brake device is input, the corresponding brake pad deterioration degree is output. Alternatively, in... Figure 5 In the process shown in S15, the first estimation algorithm E1A is updated. Figure 6 In the process S22 shown, the drive current of the electric motor (sensor detection value) is input to the first estimation algorithm E1A.

[0122] In the above embodiments, the following description may also be stored in the storage device 153. Figure 8 The second estimation algorithm E2A shown is used to replace Figure 4 The second estimation algorithm E2 is shown.

[0123] Figure 8 This is a diagram representing the first variation of the second estimation algorithm E2. (Refer to...) Figure 8 Regarding the second estimation algorithm E2A, if the temperature of MG20 is input, then the degree of degradation of MG20 is output. Alternatively, in... Figure 5 In the process shown in S15, the second estimation algorithm E2A is updated. Figure 6 In the process S22 shown, the temperature of MG20 (sensor detection value) is input to the second estimation algorithm E2A.

[0124] The input to the estimation algorithm can also be multiple parameters. For example, in the above embodiment, the following description can also be stored in the storage device 153. Figure 9 The second estimation algorithm E2B shown is used instead Figure 4 The second estimation algorithm E2 is shown.

[0125] Figure 9 This is a diagram representing a second variation of the second estimation algorithm E2. (Refer to...) Figure 9 Regarding the second estimation algorithm E2B, if the driving current and temperature of MG20 are input, the output is the degradation degree of MG20. Alternatively, in... Figure 5 In the process shown in S15, the second estimation algorithm E2B is updated. Figure 6 In the process S22 shown, the drive current and temperature of MG20 (both sensor detection values) are input to the second estimation algorithm E2B.

[0126] In the above embodiments, either the first estimation algorithm E1 or the second estimation algorithm E2 may be omitted. Conversely, estimation algorithms may be added. For example, estimation algorithms may be prepared for components related to the steering of vehicle 1.

[0127] The vehicle's configuration is not limited to the configuration described in the above embodiments (see reference). Figure 1 , Figure 2 as well as Figure 4The base vehicle can also have autonomous driving capabilities without modification. The level of autonomous driving can be either fully autonomous (Level 5) or conditionally autonomous (e.g., Level 4). The vehicle configuration can also be appropriately modified for dedicated driverless operation. For example, a dedicated driverless vehicle may not have components for human operation (steering wheel, etc.). The vehicle can be equipped with solar panels and may also have flight capabilities. The vehicle is not limited to cars; it can also be a bus or truck. The vehicle can also be a privately owned vehicle (POV). The vehicle can also be a multi-purpose vehicle customized according to the user's intended use. The vehicle can also be a mobile shop vehicle, robot taxi, automated guided vehicle (AGV), or agricultural machinery. The vehicle can also be a small, driverless or single-passenger BEV (e.g., Micro-Palette).

[0128] The embodiments disclosed herein should be considered exemplary and not restrictive in all respects. The scope of the technology shown in this disclosure is indicated by the claims, rather than by the description of the embodiments above, and is intended to include all modifications in the same sense and scope as the claims.

Claims

1. A vehicle, characterized in that, include: The storage device is configured to store an estimation algorithm, which is configured to output the degradation degree of the component if the input is a value of a parameter related to the component mounted on the vehicle. A sensor is configured to detect the value of the parameter; as well as Control device, The control device is configured to: Perform performance tests on autonomous driving based on the vehicle; In the performance test, data representing the performance of the component are obtained; The data obtained in the performance test is used to obtain the relationship between the degradation degree of the component and the parameter, and the relationship is used to update the estimation algorithm; The vehicle shall perform automatic driving during the specified period of use; as well as When a first operating period and a second operating period are set in the control device, the performance test is performed during the period from the end of the first operating period to the beginning of the second operating period, where the second operating period is the next operating period after the first operating period.

2. The vehicle according to claim 1, characterized in that, The control device is configured to: During the application period, the values ​​of the parameters detected by the sensor are input into the estimation algorithm; and The degradation degree of the component, output from the estimation algorithm, is used to determine whether the degradation degree of the component exceeds a predetermined threshold.

3. The vehicle according to claim 1, characterized in that, The data obtained through the performance test includes at least one of data representing the vehicle's braking performance and data representing the vehicle's acceleration performance. Furthermore, the control device is configured to use the data obtained through the performance test to perform calibration for the autonomous driving of the vehicle.

4. The vehicle according to claim 1, characterized in that, The control device is configured to use the estimation algorithm to estimate the degree of degradation of the component during the autonomous driving of the vehicle when the number of updates of the estimation algorithm exceeds a predetermined number.

5. The vehicle according to any one of claims 1 to 4, characterized in that, The component mounted on the vehicle is a brake pad that constitutes a hydraulic disc brake device. The parameter associated with the component is the hydraulic pressure level of the brake pad. Furthermore, the data obtained through the performance test includes data representing the braking performance of the vehicle.

6. The vehicle according to any one of claims 1 to 4, characterized in that, The component mounted on the vehicle is a motor for driving. The parameter associated with the component is at least one of the current used to drive the driving motor and the temperature of the driving motor. Furthermore, the data obtained through the performance test includes data representing the vehicle's acceleration performance.

7. The vehicle according to any one of claims 1 to 4, characterized in that, Also includes: Autonomous driving kit; as well as The vehicle control interface is configured to mediate the exchange of signals between the control unit and the autonomous driving suite. The autonomous driving kit is configured to send commands for autonomous driving to the control device via the vehicle control interface. The control device is configured to: Control the vehicle according to the instructions from the autonomous driving suite; and The vehicle control interface sends signals indicating the vehicle's status to the autonomous driving suite.

8. A vehicle, characterized in that, include: The storage device is configured to store a first estimation algorithm and a second estimation algorithm. For the first estimation algorithm, if a value of a first parameter related to a first component mounted on the vehicle is input, the first estimation algorithm outputs the degree of degradation of the first component. For the second estimation algorithm, if a value of a second parameter related to a second component mounted on the vehicle is input, the second estimation algorithm outputs the degree of degradation of the second component. A first sensor is configured to detect the value of the first parameter; The second sensor is configured to detect the value of the second parameter; as well as Control device, The control device is configured to: Perform performance tests on autonomous driving based on the vehicle; In the performance test, first data representing the performance of the first component and second data representing the performance of the second component are obtained; The first data obtained in the performance test is used to obtain a first relationship between the degradation degree of the first component and the first parameter, and the first relationship is used to update the first estimation algorithm. The second data obtained in the performance test is used to obtain a second relationship between the degradation degree of the second component and the second parameter, and the second relationship is used to update the second estimation algorithm. The vehicle shall perform automatic driving during the specified period of use; as well as When a first operating period and a second operating period are set in the control device, the performance test is performed during the period from the end of the first operating period to the beginning of the second operating period, where the second operating period is the next operating period after the first operating period.

9. The vehicle according to claim 8, characterized in that, The first component mounted on the vehicle is a component for braking the vehicle. The first data representing the performance of the first component includes data representing the braking performance of the vehicle. The second component mounted on the vehicle is a component for driving the vehicle. Furthermore, the second data representing the performance of the second component is data representing the acceleration performance of the vehicle.

10. A method for diagnosing the deterioration of a component mounted on a vehicle, characterized in that, include: Perform performance tests on autonomous driving based on the vehicle; In the performance test, data representing the performance of the component are obtained; as well as The estimation algorithm is updated using the data obtained in the performance test. The estimation algorithm is configured to output the degree of degradation of a component if the input parameter is a value related to a component mounted on the vehicle. The updated estimation algorithm includes using the data obtained in the performance test to determine the relationship between the component's degradation degree and the parameters, and using the relationship to update the estimation algorithm. The method further includes: The vehicle shall perform automatic driving during the specified period of use; as well as When a first application period and a second application period are set, the performance test is performed during the period from the end of the first application period to the beginning of the second application period, where the second application period is the next application period after the first application period.

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