Fault processing method and device and new energy automobile

Abstract

The invention relates to the field of new energy automobiles, and provides a fault processing method and device and a new energy automobile. The method comprises the following steps: acquiring real-time state information, external environment state information and driver intention information of a vehicle; determining a fault risk coefficient of the vehicle based on the real-time state information; determining a current driving scene of the vehicle based on the real-time state information and the external environment state information; and determining a fault processing measure based on the current driving scene, the fault risk coefficient and the driver intention information. According to the method, multi-source information such as the real-time state of the vehicle, the external environment and the intention of a driver can be considered, fault processing measures which are more adaptive to the actual driving scene and meet the fault-tolerant requirement are formulated, and therefore the vehicle using experience of a user is improved, and meanwhile the overall usability is improved.

Description

Technical Field

[0001] This application relates to the field of new energy vehicles, and in particular to a fault handling method, device, and new energy vehicle. Background Technology

[0002] With the increasing complexity of automotive electronic and electrical architecture and the rapid development of autonomous driving and electrification technologies, the vehicle control unit (VCU), as the "nerve center" of the vehicle, is receiving increasing attention for its control logic and safety performance.

[0003] Currently, the VCU mainly undertakes tasks such as powertrain coordination, vehicle status management, and communication management. However, it still has obvious shortcomings in vehicle fault diagnosis and response. For example, when a vehicle system fault is detected, a one-size-fits-all response is often adopted, such as directly entering fault mode or forcibly reducing power, which leads to a decrease in vehicle availability. Another example is that the vehicle still restricts operation in some non-serious fault situations, failing to consider actual driving scenarios and fault tolerance requirements, thereby affecting the user's driving experience.

[0004] Therefore, existing vehicle fault handling methods still need further improvement and development. Summary of the Invention

[0005] In view of this, embodiments of this application provide a fault handling method, device, and new energy vehicle to solve the problem that the existing vehicle system fault response methods are too simple, resulting in a decrease in the availability of the whole vehicle and failing to consider actual driving scenarios and fault tolerance requirements, thereby affecting the user's driving experience.

[0006] A first aspect of this application provides a fault handling method, including: Acquire real-time vehicle status information, external environment status information, and driver intent information; Based on real-time status information, determine the vehicle's failure risk coefficient; Based on real-time status information and external environment status information, the current driving scenario of the vehicle is determined; Based on the current driving scenario, fault risk coefficient, and driver intent information, determine the fault handling measures.

[0007] A second aspect of this application provides a fault handling apparatus, comprising: The information acquisition module is configured to acquire real-time vehicle status information, external environment status information, and driver intent information; The first determination module is configured to determine the vehicle's failure risk coefficient based on real-time status information; The second determination module is configured to determine the vehicle's current driving scenario based on real-time status information and external environment status information. The third determination module is configured to determine fault handling measures based on the current driving scenario, fault risk coefficient, and driver intention information.

[0008] A third aspect of the embodiments of this application provides a new energy vehicle, including a vehicle controller, which includes the fault handling device of the second aspect.

[0009] Compared with the prior art, the beneficial effects of the embodiments of this application include at least the following: determining the fault risk coefficient based on the real-time status information of the vehicle, determining the current driving scenario based on the real-time status information of the vehicle and the external environment status information, and then determining fault handling measures based on the current driving scenario, fault risk coefficient and driver intention information. This can take into account multiple sources of information such as the real-time status of the vehicle, the external environment and the driver's intention, and formulate fault handling measures that are more adapted to the actual driving scenario and meet the fault tolerance requirements, thereby improving the user's driving experience and improving overall availability. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a fault handling method provided in an embodiment of this application; Figure 2 This is a structural block diagram of a fault handling device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] A fault handling method and apparatus according to an embodiment of this application will now be described in detail with reference to the accompanying drawings.

[0014] Figure 1 This is a flowchart illustrating a fault handling method provided in an embodiment of this application. Figure 1 The fault handling method can be executed by the vehicle control unit (VCU). For example... Figure 1 As shown, the fault handling method includes: Step S101: Obtain real-time vehicle status information, external environment status information, and driver intent information.

[0015] In the embodiments of this application, the vehicle may be a new energy vehicle, including battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), range-extended electric vehicles (EREV), hydrogen fuel cell vehicles (FCEV), etc.

[0016] External environmental status information includes vehicle positioning and navigation information, vehicle external environment perception information, and infrastructure communication information. Vehicle positioning and navigation information includes current road type, slope, curvature, and distance to the next exit or service area. Current road type includes, but is not limited to, highways, urban roads, and tunnels. Vehicle external environment perception information includes traffic flow density ahead (which can be collected through onboard cameras or radar sensors), weather, and road surface adhesion coefficient (which can be indirectly estimated through ESP (Electronic Stability Program) or ABS (Anti-lock Braking System). Infrastructure communication information includes information such as road construction ahead and road congestion.

[0017] Driver intent information includes driver input, driver mode, and driver behavior recognition information. Driver input includes accelerator pedal opening, brake pedal opening, steering input, and gear selection. Driver mode includes whether hazard lights are activated and driving mode selection (e.g., Eco / Sport / Comfort mode). Driver behavior recognition information indicates whether the driver is in an emergency or anxious state; for example, detecting sudden braking or acceleration generally indicates that the driver is currently in an emergency or anxious state.

[0018] As an example, the vehicle control unit (VCU) can directly or indirectly acquire real-time vehicle status information, external environment status information, and driver intent information through various sensors mounted on the vehicle.

[0019] Step S102: Determine the vehicle's fault risk coefficient based on real-time status information.

[0020] The fault risk coefficient is used to characterize the current fault risk level of a vehicle (such as low risk, medium risk, or high risk).

[0021] As an example, the correspondence between fault risk level and fault risk coefficient is shown in Table 1.

[0022] Table 1. Correspondence between Fault Risk Level and Fault Risk Coefficient Low risk generally indicates that the vehicle has no abnormalities and is driving normally. Medium risk generally indicates that some vehicle indicators are abnormal, requiring closer attention. High risk generally indicates that key vehicle indicators are abnormal, requiring immediate inspection and repair.

[0023] Step S103: Determine the current driving scenario of the vehicle based on real-time status information and external environment status information.

[0024] As an example, firstly, the VCU preprocesses the collected real-time state information and external environment state information (e.g., removing outliers, standardizing data formats, etc.) to eliminate noise and conflicts, thereby improving the reliability of subsequent feature extraction and feature matching. Then, key vehicle state features related to the driving scenario are extracted from the preprocessed real-time state information, and key external environment features related to the driving scenario are extracted from the preprocessed external environment state information. Finally, based on the correspondence between the combination of key vehicle state features and key external environment features and the driving scenario, the current driving scenario of the vehicle is determined.

[0025] Step S104: Determine fault handling measures based on the current driving scenario, fault risk coefficient, and driver intention information.

[0026] Fault handling measures include, but are not limited to: I. Information prompts; II. Fault-tolerant operation; III. Restricted operation; IV. Emergency shutdown.

[0027] Generally speaking, the higher the failure risk coefficient, the greater the degree of vehicle failure and the more restrictions the vehicle's functions will be placed on it.

[0028] The technical solution provided in this application determines the fault risk coefficient based on the vehicle's real-time status information, determines the current driving scenario based on the vehicle's real-time status information and external environment status information, and then determines fault handling measures based on the current driving scenario, fault risk coefficient, and driver intention information. This can take into account multiple sources of information such as the vehicle's real-time status, external environment, and driver intention, and formulate fault handling measures that are more adapted to actual driving scenarios and meet fault tolerance requirements, thereby improving the user's driving experience and enhancing overall availability.

[0029] In some embodiments, real-time status information includes key operating parameters of the powertrain system, key operating parameters of the chassis system, and vehicle motion status parameters; the fault risk coefficient includes a first fault risk coefficient, a second fault risk coefficient, and a third fault risk coefficient. Based on real-time status information, the vehicle's failure risk coefficient is determined, including: Based on key operating parameters of the powertrain system, determine the vehicle's first failure risk coefficient; Based on key operating parameters of the chassis system, the risk coefficient of the vehicle's second failure is determined. The third fault risk coefficient of the vehicle is determined based on the vehicle's motion state parameters.

[0030] The key operating parameters of the power system include engine status parameters, motor status parameters, and battery status parameters.

[0031] Key operating parameters of the chassis system mainly include: braking pressure, steering angle, EPS (electric power steering) status, and EBS (electronic braking system) status.

[0032] The main parameters of vehicle motion state include: vehicle speed, vehicle acceleration, and vehicle yaw rate.

[0033] In some embodiments, the step of determining the first failure risk coefficient of a vehicle based on key operating parameters of the powertrain system specifically includes: The engine failure risk coefficient is determined based on engine state parameters, the motor failure risk coefficient is determined based on motor state parameters, and the battery failure risk coefficient is determined based on battery state parameters. Based on the engine failure risk coefficient, motor failure risk coefficient, and battery failure risk coefficient, the vehicle's first failure risk coefficient is determined.

[0034] In some embodiments, engine state parameters include engine output torque, engine output power, and actual engine torque. Determining the engine failure risk coefficient based on these engine state parameters includes: The actual engine torque is corrected to obtain the corrected engine torque; Calculate the torque deviation rate based on the corrected engine torque and the engine output torque; Calculate the power deviation rate based on the corrected engine torque and engine output power; Determine the torque deviation risk level and first weighting coefficient corresponding to the torque deviation rate, and the power deviation risk level and second weighting coefficient corresponding to the power deviation rate; The engine failure risk coefficient is determined based on the torque deviation risk level, the first weighting coefficient, the power deviation risk level, and the second weighting coefficient.

[0035] As an example, the engine failure risk coefficient is determined based on engine output torque, engine output power, and actual engine torque, specifically including: First, the actual engine torque is corrected based on real-time operating conditions to avoid misjudgment due to differences in operating conditions. For example, the actual engine torque can be corrected according to formula (1) to obtain the corrected engine torque.

[0036] (1); In equation (1), This indicates the corrected engine torque; This indicates the actual torque of the engine; This represents a correction factor, which is related to the environment and operating conditions.

[0037] In practical applications, real-vehicle tests can be conducted under different environments and operating conditions to obtain results under different environments and operating conditions. .

[0038] Then, the torque deviation rate is calculated based on the corrected engine torque and the engine output torque. For example, the torque deviation rate can be calculated according to formula (2).

[0039] (2); In equation (2), Indicates the torque deviation rate; This indicates the engine's output torque; This indicates the corrected engine torque.

[0040] The power deviation rate is calculated based on the corrected engine torque and engine output power. For example, the power deviation rate can be calculated according to formula (3).

[0041] (3); In equation (3), Indicates the power deviation rate. Indicates engine output power. This represents the engine's theoretical reference power. , Indicates engine speed. This indicates the corrected engine torque.

[0042] Next, the torque deviation risk level corresponding to the torque deviation rate is determined according to formula (4) (0 = no risk, 1 = extremely high risk), and the power deviation risk level corresponding to the power deviation rate is determined according to formula (5) (0 = no risk, 1 = extremely high risk).

[0043] (4); In equation (4), This indicates the torque deviation risk level corresponding to the torque deviation rate; This represents the safe torque deviation threshold corresponding to the torque deviation rate, typically set to ±5%. This represents the critical torque deviation threshold corresponding to the torque deviation rate, which is typically set to ±20%.

[0044] (5); In equation (5), This indicates the power deviation risk level corresponding to the power deviation rate; This represents the safe power deviation threshold corresponding to the power deviation rate, typically set to ±5%. This represents the critical power deviation threshold corresponding to the power deviation rate, which is typically set to ±20%.

[0045] Next, the engine failure risk coefficient is calculated based on the torque deviation risk level, power deviation risk level, first weighting coefficient, and second weighting coefficient. For example, the engine failure risk coefficient is calculated according to formula (6). The value range of the engine failure risk coefficient is [0, 100].

[0046] (6); In equation (6), Indicates the engine failure risk coefficient; Indicates the first weighting coefficient; This represents the second weighting coefficient.

[0047] The sum of the first and second weighting coefficients is 1. Generally, the greater the impact of a parameter on the core function of the engine, the higher its weighting coefficient. Torque is a direct output indicator of the engine, and its deviation directly affects power performance and load adaptability, making it the most correlated with faults. Power is a derived parameter of torque and is used for cross-validation to avoid misjudgments caused by a single torque sensor failure; therefore, the first weighting coefficient is greater than the second weighting coefficient.

[0048] The motor status parameters include motor output torque, motor output power, and motor actual torque. The steps for determining the engine failure risk coefficient described above can be used to determine the motor failure risk coefficient based on the motor status parameters (value range [0,100]), which will not be repeated here.

[0049] Battery state parameters include battery SOC. The steps for determining the engine failure risk coefficient described above can be used to determine the battery failure risk coefficient based on the battery state parameters (value range [0,100]), which will not be repeated here.

[0050] In some embodiments, a first failure risk coefficient for the vehicle is determined based on the engine failure risk coefficient, the motor failure risk coefficient, and the battery failure risk coefficient, specifically including: Determine the first weight value corresponding to the engine failure risk coefficient, the second weight value corresponding to the motor failure risk coefficient, and the third weight value corresponding to the battery failure risk coefficient. The first failure risk coefficient of the vehicle is determined based on the engine failure risk coefficient, the first weight value, the motor failure risk coefficient, the second weight value, the battery failure risk coefficient, and the third weight value.

[0051] As an example, the first, second, and third weight values ​​can be determined based on functional safety priority and the scope of the fault's impact. Generally, the greater the impact of a system fault on overall vehicle safety and the stronger its irreplaceable function, the higher its weight. The sum of the first, second, and third weight values ​​is 1.

[0052] The allocation scheme for the first, second, and third weight values ​​differs depending on the type of vehicle. For example, for pure electric vehicles, the first, second, and third weight values ​​can be set to 0, 0.4, and 0.6, respectively. Similarly, for hybrid vehicles, the first, second, and third weight values ​​can be set to 0.4, 0.2, and 0.4, respectively.

[0053] For example, the first failure risk coefficient can be calculated according to formula (7).

[0054] (7); In equation (7), This represents the first failure risk coefficient; Indicates the engine failure risk coefficient; Indicates the first weight value; Indicates the motor failure risk coefficient; This represents the second weight value; Indicates the battery failure risk coefficient; This represents the third weight value.

[0055] The steps for determining the first fault risk coefficient can be referred to above. Based on the key operating parameters of the chassis system, the second fault risk coefficient of the vehicle can be determined, which will not be repeated here.

[0056] The steps for determining the first fault risk coefficient can be referred to above. Based on the vehicle motion state parameters, the third fault risk coefficient of the vehicle can be determined, which will not be repeated here.

[0057] In some embodiments, the current driving scenario of the vehicle is determined based on real-time status information and external environment status information, including: Extract key vehicle status features from real-time status information and key external environment features from external environment status information; Identify target driving scenarios that match key vehicle state characteristics and key external environment characteristics; If the changes in key vehicle status features and key external environment features within a preset time period are less than a preset change threshold, then the target driving scenario is determined as the vehicle's current driving scenario.

[0058] Key vehicle status characteristics include average vehicle speed (e.g., average vehicle speed over 3 seconds), vehicle speed fluctuation rate (e.g., the difference between the maximum and minimum vehicle speed over 3 seconds divided by the average vehicle speed), mean steering angle (e.g., the mean absolute value of steering angle over 3 seconds), steering frequency (number of steering turns per minute), and braking or acceleration frequency (e.g., the number of times the braking pressure is greater than 1 MPa within 3 seconds).

[0059] Key external environmental features include road features (such as the number of lane lines, speed limit, road curvature, and road type), traffic features (such as the density of vehicles ahead (such as the number of vehicles within 100 meters), average vehicle distance, vehicle congestion index (the ratio of real-time vehicle speed to road speed limit), and environmental auxiliary features (such as light intensity, precipitation, and intersection signs (traffic lights / zebra crossings)).

[0060] Driving scenarios include: highway cruising, smooth city traffic, city congestion, rural roads, mountain curves, night driving, rainy driving, intersection traffic (straight / left turn / right turn), parking, U-turns, and emergency avoidance.

[0061] As an example, suppose the combination of key external environmental features and key vehicle state features is as follows: road type is highway, average vehicle speed ≥ 80 km / h, vehicle speed fluctuation rate < 10%, number of lanes ≥ 3 and average steering angle < 5°, then the target driving scenario is a highway cruising scenario. If the changes in the above key external environmental features and key vehicle state features within a preset time period (which can be flexibly set according to actual conditions, for example, it can be set to 1 second, 2 seconds, etc.) are less than a preset change threshold, then the current driving scenario can be determined to be a highway cruising scenario.

[0062] As another example, suppose the combination of key external environmental features and key vehicle state features is as follows: road type is urban road, average vehicle speed ≤ 30 km / h, congestion index < 0.5, preceding vehicle density ≥ 3 vehicles / 100 meters, and braking or acceleration frequency ≥ 5 times / minute. Then the target driving scenario is an urban congestion scenario. If the changes in the above key external environmental features and key vehicle state features within a preset time period (which can be flexibly set according to actual conditions, for example, it can be set to 1 second, 2 seconds, etc.) are less than a preset change threshold, then the current driving scenario can be determined to be an urban congestion scenario.

[0063] The above methods can improve the accuracy and robustness of current driving scene recognition.

[0064] In some embodiments, fault handling measures are determined based on the current driving scenario, fault risk coefficient, and driver intent information, including: If the fault risk coefficient is less than or equal to the first preset threshold, and the current driving scenario is a low-complexity driving scenario, and the driver's intention information represents a non-emergency operation intention, then the fault handling measure is to output fault prompt information to the user.

[0065] As an example, the complexity of driving scenarios can be categorized based on environmental interference, operational difficulty, and decision-making pressure. Generally, low-complexity driving scenarios involve a simple environment, easy operation, and low decision-making pressure. Medium-complexity driving scenarios involve a variable environment, moderate operation, and require continuous monitoring. High-complexity driving scenarios involve a complex environment, precise operation, and high decision-making requirements.

[0066] As an example, based on the aforementioned complexity classification criteria, some common driving scenarios are categorized, resulting in the following scenario classification results: Low-complexity driving scenarios include highway cruising and smooth urban traffic. Medium-complexity driving scenarios include urban congestion, rural roads, intersection traffic (straight / left turn / right turn), nighttime driving, and rainy driving. High-complexity driving scenarios include mountain curves, parking, U-turns, and emergency avoidance.

[0067] The first preset threshold can be flexibly set according to the actual situation; for example, it can be set to 30.

[0068] Non-emergency operation intents represent low-priority operations initiated by the driver to meet "comfort, convenience, or scenario-based needs." These operations do not affect the vehicle's core driving status, can be delayed or canceled, and are less demanding than regular driving operations. Examples include adjusting the air conditioning temperature and fan speed, seat heating / ventilation, steering wheel adjustment, changing music, setting navigation destinations, and adjusting volume while driving.

[0069] As an example, if the fault risk coefficient is less than or equal to the first preset threshold (e.g., 30), and the current driving scenario is a high-speed cruise scenario (belonging to a low-complexity driving scenario), and the driver's intention information represents a non-emergency operation intention (e.g., adjusting the air conditioning temperature), then the actuator will not be intervened. Instead, a yellow indicator light will be illuminated on the instrument panel or a prompt message will be displayed to inform the user of the current fault status of the vehicle.

[0070] In some embodiments, fault handling measures are determined based on the current driving scenario, fault risk coefficient, and driver intent information, including: If the fault risk coefficient is greater than the first preset threshold and less than or equal to the second preset threshold, and the current driving scenario is a medium-complexity driving scenario, and the driver's intention information represents a normal operating intention, then the fault handling measure is determined to be to gradient limit the vehicle's torque and disable preset vehicle functions, or to control the vehicle to enter a fault-tolerant operation mode.

[0071] The second preset threshold can be flexibly set according to the actual situation; for example, it can be set to 70.

[0072] Routine operational intent refers to the smooth and predictable actions initiated by the driver in a anticipated driving scenario to achieve "normal driving, route planning, and condition adaptation." These are the most frequent types of operations during driving and conform to daily driving habits and traffic rules. For example, the driver controls the vehicle to drive straight / follow other vehicles (driving at a constant speed, lightly pressing the accelerator); change lanes / turn (using the turn signal, smoothly turning).

[0073] As an example, if the fault risk coefficient is greater than a first preset threshold (e.g., 30) and less than or equal to a second preset threshold (e.g., 70), and the current driving scenario is an urban congestion scenario (belonging to a medium-complexity driving scenario), and the driver's intention information represents a normal operating intention (e.g., driving straight at a constant speed), then the fault handling measure is to apply gradient limiting to the vehicle's torque and disable preset vehicle functions. Applying gradient limiting to the vehicle's torque can be done by gradually tightening the limit in the order of limiting peak torque → limiting continuous power → limiting maximum speed. In this case, functions related to vehicle comfort (e.g., air conditioning, seat heating, etc.) can be disabled first, then unnecessary power functions (e.g., start-up assist function) can be disabled, and finally core drive functions can be disabled.

[0074] As another example, if the fault risk coefficient is greater than the first preset threshold (e.g., 30) and less than or equal to the second preset threshold (e.g., 70), and the current driving scenario is an urban congestion scenario (belonging to a medium-complex driving scenario), and the driver's intention information represents a normal operating intention (e.g., smooth steering), then the fault handling measure is to control the vehicle to enter a fault-tolerant operation mode or a restricted operation mode.

[0075] As an example, controlling the vehicle to enter a restricted operating mode could specifically be controlling the vehicle to enter a limp home mode (vehicle speed is generally less than 10km / h). In this mode, power output is strictly limited (such as limiting the maximum speed and maximum power), but the driver is allowed to drive the vehicle to a safe location or repair shop. At the same time, a red warning light is illuminated and clear restriction information is displayed (such as "Power system failure, please stop safely and contact service").

[0076] As an example, controlling the vehicle to enter a fault-tolerant operating mode can specifically involve shutting down non-core functions (such as turning off the air conditioning compressor to protect the battery) and illuminating a yellow warning light, displaying specific warning information (such as "Low battery power, air conditioning power limited"). Alternatively, when the vehicle is in a location where immediate stopping is not possible, such as a highway, bridge, or tunnel, restrictions on certain vehicle functions can be temporarily relaxed, allowing the vehicle to leave these areas. Once the vehicle enters a safe area (such as a service area), stricter function restrictions can then be enforced.

[0077] In some embodiments, fault handling measures are determined based on the current driving scenario, fault risk coefficient, and driver intent information, including: If the fault risk coefficient is greater than the second preset threshold, or if the fault risk coefficient is greater than the second preset threshold and the current driving scenario is a high-complexity driving scenario, then the fault handling measure is determined to be to control the vehicle to enter the emergency stop mode.

[0078] As an example, if the fault risk coefficient is greater than the second preset threshold (e.g., 70), or if the fault risk coefficient is greater than the second preset threshold (e.g., 70), and the current driving scenario is a high-complexity driving scenario (e.g., parking scenario), then the fault handling measure is determined to be to control the vehicle to enter the emergency stop mode.

[0079] As an example, controlling a vehicle to enter emergency stop mode can specifically involve at least one or more of the following measures: 1) immediately disconnecting the vehicle's high-voltage power supply (disconnecting the main negative relay); 2) requesting zero torque output from the motor (MCU); 3) maintaining the normal operation of braking systems such as ESP to assist the driver in stopping the vehicle safely; 4) unlocking the doors and automatically turning on the hazard warning lights; 5) illuminating all relevant red warning lights, emitting a strong audible alarm, and displaying "Serious malfunction, stop safely immediately".

[0080] The above technical solution can take into account multiple sources of information such as the vehicle's real-time status, external environment, and driver's intentions, and formulate fault handling measures that are more adapted to actual driving scenarios and meet fault tolerance requirements, thereby improving the user's driving experience and enhancing overall availability.

[0081] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0082] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0083] Figure 2 This is a structural block diagram of a fault handling device provided in an embodiment of this application. For example... Figure 2 As shown, the fault handling device 200 includes: The information acquisition module 201 is configured to acquire real-time vehicle status information, external environment status information, and driver intent information; The first determining module 202 is configured to determine the vehicle's fault risk coefficient based on real-time status information; The second determining module 203 is configured to determine the current driving scenario of the vehicle based on real-time status information and external environment status information. The third determination module 204 is configured to determine fault handling measures based on the current driving scenario, fault risk coefficient and driver intention information.

[0084] In some embodiments, the real-time status information includes key operating parameters of the powertrain system, key operating parameters of the chassis system, and vehicle motion state parameters; the fault risk coefficient includes a first fault risk coefficient, a second fault risk coefficient, and a third fault risk coefficient. The aforementioned first determining module 202 includes: The first determining unit is configured to determine the first failure risk coefficient of the vehicle based on key operating parameters of the power system. The second determining unit is configured to determine the second failure risk coefficient of the vehicle based on key operating parameters of the chassis system. The third determining unit is configured to determine the third fault risk coefficient of the vehicle based on the vehicle motion state parameters.

[0085] In some embodiments, key operating parameters of the power system include engine status parameters, motor status parameters, and battery status parameters. The first determining unit described above includes: The first determining component is configured to determine the engine failure risk coefficient based on engine state parameters, the motor failure risk coefficient based on motor state parameters, and the battery failure risk coefficient based on battery state parameters. The second determining component is configured to determine the vehicle's first failure risk coefficient based on the engine failure risk coefficient, the motor failure risk coefficient, and the battery failure risk coefficient.

[0086] In some embodiments, engine state parameters include engine output torque, engine output power, and actual engine torque. The first determining component described above can be configured as follows: The actual engine torque is corrected to obtain the corrected engine torque; Calculate the torque deviation rate based on the corrected engine torque and the engine output torque; Calculate the power deviation rate based on the corrected engine torque and engine output power; Determine the torque deviation risk level and first weighting coefficient corresponding to the torque deviation rate, and the power deviation risk level and second weighting coefficient corresponding to the power deviation rate; The engine failure risk coefficient is determined based on the torque deviation risk level, the first weighting coefficient, the power deviation risk level, and the second weighting coefficient.

[0087] In some embodiments, the second determining module 203 described above includes: The extraction unit is configured to extract key vehicle status features from real-time status information and key external environment features from external environment status information. The first scenario determination unit is configured to determine the target driving scenario that matches the key vehicle state characteristics and key external environment characteristics. The second scenario determination unit is configured to determine the target driving scenario as the current driving scenario of the vehicle if the degree of change of key vehicle state features and key external environment features within a preset time period is less than a preset change threshold.

[0088] In some embodiments, the third determining module 204 described above includes: a first processing unit, configured to output fault prompt information to the user if the fault risk coefficient is less than or equal to a first preset threshold, the current driving scenario is a low-complexity driving scenario, and the driver's intention information represents a non-emergency operation intention.

[0089] In some embodiments, the third determining module 204 further includes: if the fault risk coefficient is greater than the first preset threshold and less than or equal to the second preset threshold, and the current driving scenario is a medium-complexity driving scenario or a high-complexity driving scenario, and the driver's intention information represents the intention of normal operation, then the fault handling measure is to gradient limit the torque of the vehicle and disable the preset vehicle function, or control the vehicle to enter the fault-tolerant operation mode or the restricted operation mode.

[0090] In some embodiments, the third determining module 204 further includes: if the fault risk coefficient is greater than the second preset threshold, then determining the fault handling measure as controlling the vehicle to enter the emergency stop mode.

[0091] This application embodiment also provides a new energy vehicle, which includes a vehicle controller, the vehicle controller including as follows: Figure 2 The fault handling device shown.

[0092] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0093] Figure 3 This is a schematic diagram of the electronic device 300 provided in an embodiment of this application. For example... Figure 3As shown, the electronic device 300 of this embodiment includes a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the various method embodiments described above. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the various device embodiments described above.

[0094] Electronic device 300 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 300 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. It may include more or fewer parts than shown, or different parts.

[0095] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0096] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 300. The memory 302 can also include both internal and external storage units of the electronic device 300. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium can be appropriately added to or subtracted according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, a computer-readable medium does not include electrical carrier signals and electrical signals.

[0099] It should be noted that in the description of this application, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0100] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0101] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A fault handling method, characterized in that, include: Acquire real-time vehicle status information, external environment status information, and driver intent information; Based on the real-time status information, the failure risk coefficient of the vehicle is determined; Based on the real-time status information and the external environment status information, the current driving scenario of the vehicle is determined; Based on the current driving scenario, fault risk coefficient, and driver intent information, fault handling measures are determined.

2. The method according to claim 1, characterized in that, The real-time status information includes key operating parameters of the power system, key operating parameters of the chassis system, and vehicle motion status parameters; the fault risk coefficient includes a first fault risk coefficient, a second fault risk coefficient, and a third fault risk coefficient. Based on the real-time status information, the failure risk coefficient of the vehicle is determined, including: Based on the key operating parameters of the power system, the first failure risk coefficient of the vehicle is determined; Based on the key operating parameters of the chassis system, the second failure risk coefficient of the vehicle is determined; Based on the vehicle motion state parameters, the third fault risk coefficient of the vehicle is determined.

3. The method according to claim 2, characterized in that, The key operating parameters of the power system include engine status parameters, motor status parameters, and battery status parameters; Based on the key operating parameters of the powertrain system, the first failure risk coefficient of the vehicle is determined, including: The engine failure risk coefficient is determined based on the engine status parameters, the motor failure risk coefficient is determined based on the motor status parameters, and the battery failure risk coefficient is determined based on the battery status parameters. Based on the engine failure risk coefficient, motor failure risk coefficient, and battery failure risk coefficient, the first failure risk coefficient of the vehicle is determined.

4. The method according to claim 3, characterized in that, The engine status parameters include engine output torque, engine output power, and engine actual torque; Determining the engine failure risk coefficient based on the engine state parameters includes: The actual torque of the engine is corrected to obtain the corrected engine torque; Calculate the torque deviation rate based on the corrected engine torque and the engine output torque; Calculate the power deviation rate based on the corrected engine torque and engine output power; Determine the torque deviation risk level and first weighting coefficient corresponding to the torque deviation rate, and the power deviation risk level and second weighting coefficient corresponding to the power deviation rate; The engine failure risk coefficient is determined based on the torque deviation risk level, the first weighting coefficient, the power deviation risk level, and the second weighting coefficient.

5. The method according to claim 1, characterized in that, Based on the real-time status information and external environment status information, the current driving scenario of the vehicle is determined, including: Extract key vehicle status features from the real-time status information, and extract key external environment features from the external environment status information; Determine the target driving scenario that matches the key vehicle state characteristics and key external environment characteristics; If the changes in the key vehicle state features and key external environment features within a preset time period are less than a preset change threshold, then the target driving scenario is determined as the current driving scenario of the vehicle.

6. The method according to claim 1, characterized in that, Based on the current driving scenario, fault risk coefficient, and driver intent information, fault handling measures are determined, including: If the fault risk coefficient is less than or equal to the first preset threshold, and the current driving scenario is a low-complexity driving scenario, and the driver's intention information represents a non-emergency operation intention, then the fault handling measure is to output fault prompt information to the user.

7. The method according to claim 1, characterized in that, Based on the current driving scenario, fault risk coefficient, and driver intent information, fault handling measures are determined, including: If the fault risk coefficient is greater than the first preset threshold and less than or equal to the second preset threshold, and the current driving scenario is a medium-complexity driving scenario or a high-complexity driving scenario, and the driver's intention information represents a normal operating intention, then the fault handling measure is determined to be to gradient limit the torque of the vehicle and disable preset vehicle functions, or to control the vehicle to enter a fault-tolerant operation mode or a restricted operation mode.

8. The method according to claim 7, characterized in that, Based on the current driving scenario, fault risk coefficient, and driver intent information, fault handling measures are determined, including: If the fault risk coefficient is greater than the second preset threshold, then the fault handling measure is determined to be to control the vehicle to enter the emergency stop mode.

9. A fault handling device, characterized in that, include: The information acquisition module is configured to acquire real-time vehicle status information, external environment status information, and driver intent information; The first determining module is configured to determine the failure risk coefficient of the vehicle based on the real-time status information; The second determining module is configured to determine the current driving scenario of the vehicle based on the real-time status information and the external environment status information. The third determining module is configured to determine fault handling measures based on the current driving scenario, fault risk coefficient, and driver intention information.

10. A new energy vehicle, characterized in that, It includes a vehicle controller, which includes the fault handling device as described in claim 9.

Images