New energy vehicle non-power failure identification method and system, and storage medium

Abstract

The present disclosure provides a new energy vehicle non-power failure identification method, system and storage medium, relating to the technical field of new energy vehicle failure, wherein the method comprises: acquiring the current multi-dimensional state parameters of the target vehicle; executing a first judgment logic, which is used to identify whether the target vehicle currently has a non-power failure based on the multi-dimensional state parameters; executing a second judgment logic, which is used to perform hierarchical fault tracing on the energy flow links with multiple levels in the target vehicle based on the multi-dimensional state parameters to obtain fault cause positioning information; and generating a new energy non-power failure identification result containing whether the target vehicle currently has a non-power failure and the fault cause positioning information based on the judgment results of the first judgment logic and the second judgment logic. The method combines the double-path judgment logic of independent fault phenomenon judgment and energy flow hierarchical diagnosis, and can realize accurate positioning of non-power failure in different complexity diagnosis scenarios.

Description

Technical Field

[0001] This disclosure belongs to the field of fault diagnosis technology for new energy vehicles, specifically relating to a method, system, and storage medium for identifying non-powered faults in new energy vehicles. Background Technology

[0002] During the operation of new energy vehicles, power failure is a common and typical problem that affects their safety.

[0003] In related technologies, the diagnosis of no-power faults in new energy vehicles mostly employs threshold-based independent judgment methods and fault code-based coarse localization methods. Threshold-based independent judgment methods often rely solely on simple logic involving vehicle speed and the accelerator pedal; for example, they report a no-power fault when a single phenomenon such as pressing the accelerator pedal without increasing vehicle speed is detected. Fault code-based coarse localization methods identify no-power faults by analyzing fault codes reported by various controllers.

[0004] The above methods generally have the following drawbacks: 1) Inability to accurately identify and locate the cause of power failure: Due to the single judgment dimension, it can only report the power failure, but cannot locate the cause of the failure, such as abnormal torque demand signal, failure of vehicle controller torque distribution, limited torque output, non-responsive torque actuator, actuator failure, or mechanical jamming at the wheel end; 2) Poor maintenance guidance: Since the cause of the failure cannot be located, maintenance can only rely on experience to check the accelerator pedal, vehicle controller, motor controller, vehicle parts, wiring harness, or mechanical structure one by one, which is inefficient and prone to misjudgment, resulting in difficult maintenance and poor user experience; 3) Delayed safety response: For severe power failure caused by torque loss of control or mechanical jamming, it is impossible to trigger differentiated fault handling strategies in a timely manner according to the cause of the failure, such as high voltage cut-off or emergency alarm, which may delay safety handling due to misjudgment. Summary of the Invention

[0005] This disclosure provides a method, system, and storage medium for identifying power failures in new energy vehicles, aiming to at least partially solve the technical problem of accurately identifying and locating the cause of power failures in new energy vehicles in related technologies.

[0006] At least one embodiment of this disclosure provides a method for identifying power failure in new energy vehicles, including: Obtain the current multidimensional state parameters of the target vehicle; Execute a preset first judgment logic, wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on the multi-dimensional state parameters; A second judgment logic, operating independently of the first judgment logic, is executed. This second judgment logic is used to perform hierarchical fault tracing of the energy flow links with multiple levels in the target vehicle based on the multi-dimensional state parameters, in order to obtain fault cause location information; and... Based on the judgment results of the first judgment logic and the second judgment logic, a new energy vehicle power failure identification result is generated, which includes whether the target vehicle is currently experiencing a power failure and the location information of the cause of the failure.

[0007] The above solution offers the following technical advantages: Addressing the technical challenge of accurately identifying and locating the causes of powerlessness faults in new energy vehicles, this method is proposed. It is applicable to various types of new energy vehicles, including pure electric and hybrid vehicles, and can systematically identify powerlessness faults and accurately locate their causes. This method adds a judgment dimension based on multi-dimensional state parameters. The first judgment logic is used to determine independent fault phenomena, while the second judgment logic is used to achieve hierarchical energy flow diagnosis. It is less susceptible to interference from single signals. Through a dual-path judgment logic combining independent fault phenomenon judgment and hierarchical energy flow diagnosis, it achieves accurate identification and location of powerlessness faults in diagnostic scenarios of varying complexity. This accurately distinguishes which level of the energy flow link the powerlessness fault originates from, enabling rapid location and significantly improving the accuracy and robustness of fault diagnosis. Furthermore, it provides clear guidance for precise maintenance and graded alarm systems.

[0008] The method provided in at least one embodiment of this disclosure further includes: When the target vehicle experiences a power failure, a target fault handling strategy matching the fault cause location information is executed, and corresponding maintenance guidance information is output. The target fault handling strategy is one of a number of preset candidate fault handling strategies, and different fault cause information is matched with different candidate fault handling strategies and maintenance guidance information.

[0009] The above solution has the following technical effects: enhanced maintenance guidance and safety response.

[0010] The method provided in at least one embodiment of this disclosure further includes: Adjustments are made to multiple levels of the energy flow link, wherein the adjustments include at least one of level merging and level subdivision; and, The adjusted energy flow path is used in the second judgment logic.

[0011] The above solution has the following technical advantages: the diagnostic level is adjustable and it can be applied to non-powered fault diagnosis scenarios of varying complexity.

[0012] In the method provided in at least one embodiment of this disclosure, the multidimensional state parameters include driving intention signals and actual motion response parameters, and the first determination logic is configured as follows: Determine whether the target vehicle has completed the vehicle power-on operation; Determine whether the target vehicle is in a non-parking state. If the target vehicle has completed the vehicle power-on operation and its gear is in the non-parking state, determine whether the strength of the driving intention signal is greater than a preset strength threshold. If the strength of the driving intention signal is greater than the preset strength threshold, the timing continues; and, When the duration for which the intensity of the driving intention signal is greater than the preset intensity threshold exceeds a first preset time and the actual motion response parameter is less than the preset parameter threshold, it is determined that the target vehicle is currently experiencing a power failure.

[0013] The above solution has the following technical effects: the first judgment logic is independent of the complex torque calculation logic and can reliably identify the phenomenon of no power failure.

[0014] In the method provided in at least one embodiment of this disclosure, the multidimensional state parameters include driving intention signals, gear signals, vehicle controller demand torque, vehicle controller actual output torque, torque execution unit actual response torque, and wheel-end motion state parameters. The energy flow link includes a demand generation layer, a control decision layer, an instruction execution layer, and a mechanical transmission layer. Furthermore, the second judgment logic is configured as follows: A first tracing strategy is executed for the demand generation layer, wherein the first tracing strategy is used to identify whether the demand generation layer has a fault based on the driving intention signal, the gear signal and the current power demand suppression condition; If the demand generation layer is fault-free, a second tracing strategy is executed for the control decision layer, wherein the second tracing strategy is used to identify whether the control decision layer is faulty based on the vehicle controller's required torque and the vehicle controller's actual output torque. If the control decision layer is fault-free, a third tracing strategy is executed for the instruction execution layer, wherein the third tracing strategy is used to identify whether the instruction execution layer is faulty based on the actual output torque of the vehicle controller and the actual response torque of the torque execution unit; and If the instruction execution layer is fault-free, the fourth-level tracing strategy for the mechanical transmission layer is executed. The fourth-level tracing strategy is used to identify whether the mechanical transmission layer is faulty based on whether the actual output torque of the torque execution unit and the motion state parameters of the wheel end match.

[0015] The above solution has the following technical effects: the second judgment logic checks the energy flow layer by layer, which greatly improves the accuracy and robustness of fault location.

[0016] In the method provided in at least one embodiment of this disclosure, in addition to identifying whether the control decision layer has a fault, the second tracing strategy is further configured as follows: If the control decision layer malfunctions, obtain the internal fault code of the vehicle controller in the target vehicle; and... Based on the internal fault codes of the vehicle controller, it is determined whether the current control decision layer fault is caused by a component failure in the control decision layer, resulting in a torque limiting condition. The component includes at least one of the battery management system and the motor controller.

[0017] The above solution has the following technical effects: it can further identify torque limitations caused by faults in the three electric components of new energy vehicles, such as MCU and BMS.

[0018] In the method provided in at least one embodiment of this disclosure, the first determination logic and the second determination logic are executed in one of the following ways: Parallel execution mode, wherein the first judgment logic and the second judgment logic run in parallel; In the serial execution mode, one of the first judgment logic and the second judgment logic is executed first. When the target vehicle is identified as having a power failure or the fault location information is obtained, the other of the first judgment logic and the second judgment logic is then triggered. In the primary / backup execution mode, one of the first judgment logic and the second judgment logic is set as the primary judgment path and the other as the verification path. When the primary judgment path identifies that the target vehicle is currently experiencing a power failure or obtains the fault location information, the verification path is invoked for cross-verification.

[0019] The above solution has the following technical effects: it selects different logic execution methods according to different scenarios, thereby improving the compatibility of the method for identifying non-powered faults in new energy vehicles.

[0020] In at least one embodiment of the method provided in this disclosure, the multidimensional state parameters include: The power system readiness state includes the status of the main positive relay used to determine whether the target vehicle has completed the vehicle power-on operation; Driving intention signal, the driving intention signal including accelerator pedal opening signal; Vehicle driving status parameters, which include gear position signals; Actual motion response parameters, which include actual vehicle speed; The required torque of the vehicle controller and the actual output torque of the vehicle controller; The actual response torque of the torque actuator; and, Wheel-end motion state parameters, which include the actual driving force at the wheel end and the wheel speed at the wheel end.

[0021] The above solution has the following technical effects: by setting multi-dimensional state parameters, the accuracy of the judgment results of the first judgment logic and the second judgment logic is ensured.

[0022] At least one embodiment of this disclosure also provides a new energy vehicle power failure identification system, including: The acquisition unit is configured to acquire the current multidimensional state parameters of the target vehicle. The first processing unit is configured to execute a preset first judgment logic, wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on the multi-dimensional state parameters; The second processing unit is configured to execute a second judgment logic that operates independently of the first judgment logic. This second judgment logic is used to perform hierarchical fault tracing of the energy flow links with multiple levels in the target vehicle based on the multi-dimensional state parameters, in order to obtain fault cause location information; and... The result generation unit is configured to generate a new energy vehicle power failure identification result based on the judgment results of the first judgment logic and the second judgment logic, which includes whether the target vehicle is currently experiencing a power failure and the location information of the cause of the failure.

[0023] At least one embodiment of this disclosure also provides a storage medium storing a program or instructions, wherein the program or instructions, when executed by a processor, implement the steps of the method provided in any embodiment of this disclosure.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1A flowchart of a method for identifying non-powered faults in new energy vehicles, provided for at least one embodiment of this disclosure; Figure 2 A flowchart of a first determination logic provided for at least one embodiment of this disclosure; Figure 3 A flowchart of a second judgment logic provided for at least one embodiment of this disclosure; Figure 4 Flowchart of another method for identifying non-powered faults in new energy vehicles provided in at least one embodiment of this disclosure; Figure 5 Flowchart of another method for identifying non-powered faults in new energy vehicles provided in at least one embodiment of this disclosure; Figure 6 Example flowchart of a method for identifying non-powered faults in new energy vehicles provided in at least one embodiment of this disclosure; Figure 7 A structural block diagram of a new energy vehicle power failure identification system provided in at least one embodiment of this disclosure; Figure 8 A structural block diagram of a program product provided for at least one embodiment of this disclosure.

[0027] Figure label: 101 - Acquisition unit; 102 - First processing unit; 103 - Second processing unit; 104 - Result generation unit; 201 - Processor; 202 - Memory; 203 - Input device; 204 - Output device. Detailed Implementation

[0028] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the disclosure. Similarly, the following embodiments are only some, not all, embodiments of the present disclosure, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this disclosure.

[0029] The terms "first," "second," and "third" used in the embodiments of this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," and "third" may explicitly or implicitly include at least one of that feature.

[0030] In the description of this disclosure, "multiple" means at least two, such as two or three, unless otherwise expressly and specifically limited.

[0031] In this disclosure, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0032] The terms “comprising” and “having”, and any variations thereof, used in this disclosure are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or components inherent to such processes, methods, products, or devices.

[0033] The term "no power failure" in this disclosure refers to a failure where the driver presses the accelerator but the vehicle cannot accelerate or move normally, which is one of the common failures of new energy vehicles.

[0034] The term "vehicle controller" (VCU) used in this disclosure refers to the brain of a new energy vehicle, which coordinates the operation of the power battery, drive motor, and various subsystems.

[0035] The term "battery management system" (BMS) used in this disclosure refers to the system used for battery status detection and battery management control.

[0036] The term "motor controller" in this disclosure, abbreviated as MCU, is used to control the state of the motor according to the instructions of the vehicle controller (VCU).

[0037] In this disclosure, the term "main positive relay" refers to a relay that is directly connected in series between the positive terminal of the power battery and the high-voltage bus in the high-voltage power distribution system of a new energy vehicle, and is responsible for connecting or disconnecting the high-voltage main circuit of the vehicle.

[0038] The term "energy flow link" in this disclosure refers to a complete energy transfer link from signal input to mechanical output, which includes multiple levels, including but not limited to the demand generation layer (also known as the input layer), the control decision layer (also known as the control layer), the instruction execution layer (also known as the execution layer), and the mechanical transmission layer (also known as the mechanical layer).

[0039] The term "demand generation layer" in this embodiment consists of the accelerator pedal, brake pedal, and vehicle controller. It is used to generate power demand commands based on driver operations and is the signal input terminal for power demand in the entire energy flow chain.

[0040] The term "control decision layer" in this embodiment consists of a vehicle controller, a battery management system, and a motor controller. It is used to analyze and process the received power demand commands, output corresponding control commands to the execution components, and complete the decision-making and control of power output.

[0041] The term "command execution layer" in this embodiment consists of execution components such as high-voltage relays, contactors, and power switches. It is used to connect or disconnect the corresponding high-voltage circuit according to control commands, and to complete the on / off control of electrical energy. It is the switching link for energy transmission.

[0042] The term "mechanical transmission layer" in this embodiment of the present disclosure consists of a drive motor, a transmission device and wheels, and is used to convert electrical energy into mechanical energy and then transmit it to the wheels to drive the vehicle. It is the final link in energy output.

[0043] The term "driving intention signal" in this disclosure includes, but is not limited to, at least one of accelerator signal, brake signal, and gear signal.

[0044] The term "actual motion response parameters" in this disclosure includes, but is not limited to, at least one of the following: actual vehicle speed, wheel speed at the wheel end, vehicle speed converted from motor speed, acceleration signal, and GPS vehicle speed.

[0045] The term "fault cause location information" in the embodiments of this disclosure includes at least the specific fault level, i.e., whether the fault occurs in the demand generation layer, control decision layer, instruction execution layer or mechanical transmission layer, and may further include the fault cause and the scope of the specific faulty component.

[0046] The terms and definitions defined in GB / T 19596-2017 and GB / T 18488-2024 apply to this disclosure.

[0047] To address the technical problem of accurately identifying and locating the cause of powerlessness faults in new energy vehicles, this disclosure proposes a method applicable to various types of new energy vehicles, including pure electric and hybrid vehicles, capable of systematically identifying powerlessness faults and accurately locating their causes. This method adds a judgment dimension based on multi-dimensional state parameters. The first judgment logic is used to determine independent fault phenomena, while the second judgment logic is used to achieve hierarchical energy flow diagnosis. This approach is less susceptible to interference from single signals. Through a dual-path judgment logic combining independent fault phenomenon judgment and hierarchical energy flow diagnosis, it achieves accurate identification and location of powerlessness faults in diagnostic scenarios of varying complexity. This accurately distinguishes which level of the energy flow link the powerlessness fault originates from, enabling rapid location and significantly improving the accuracy and robustness of fault diagnosis. Furthermore, it provides clear guidance for precise maintenance and graded alarm systems.

[0048] Based on this, in the method disclosed herein, the first judgment logic is mainly based on the driving intention signal and the actual motion response parameters, independent of the complex torque calculation logic, and can reliably capture the phenomenon of no power failure with intention but no response.

[0049] Based on this, in the method disclosed herein, the second judgment logic makes judgments based on the driving intention signal, gear signal, vehicle controller demand torque, vehicle controller actual output torque, torque execution unit actual response torque, and wheel end motion state parameters, checking layer by layer along the energy flow, avoiding misdiagnosis caused by abnormal signals in a single link, and accurately locating the specific level at which the fault occurs in the energy flow link.

[0050] Based on this, the method disclosed herein can accurately pinpoint the cause of the fault to the demand generation layer, control decision layer, command execution layer, and mechanical transmission layer, supporting the execution of differentiated fault handling strategies for different layers and improving the overall vehicle operation safety.

[0051] Based on this, the method disclosed herein supports merging or subdividing energy flow links, enabling adjustable diagnostic levels. Therefore, it has good adaptability and scalability and can be applied to diagnostic scenarios of varying complexity.

[0052] Figure 1 This document presents a flowchart illustrating a method for identifying powerlessness faults in new energy vehicles, provided in at least one embodiment of this disclosure. The method is applicable to new energy vehicles, including pure electric vehicles and hybrid vehicles. Its applicable scenarios include flat road conditions, unloaded conditions, and heavy-load conditions, demonstrating good universality across different vehicle types and operating conditions. This method can be executed by the vehicle controller or a new energy vehicle powerlessness fault identification system communicating with it. Figure 1 As shown, the method may include the following steps S10-S40 to achieve non-powered fault diagnosis.

[0053] Step S10: Obtain the current multidimensional state parameters of the target vehicle.

[0054] Step S20: Execute the preset first judgment logic (also known as the first judgment route), wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on multi-dimensional state parameters.

[0055] Step S30: Execute the second judgment logic (also known as the second judgment route) which runs independently of the first judgment logic. The second judgment logic is used to perform hierarchical fault tracing of the energy flow links with multiple levels in the target vehicle based on multi-dimensional state parameters in order to obtain fault cause location information.

[0056] Step S40: Based on the judgment results of the first judgment logic and the second judgment logic, generate a new energy vehicle power failure identification result containing information on whether the target vehicle is currently experiencing a power failure and the location of the cause of the failure.

[0057] It should be noted that both the multi-dimensional state parameters and the new energy vehicle powerlessness fault identification results are real-time results. Fault cause location information may include the specific fault level and fault cause, at least the fault level. Step S20, the first judgment logic, aims to determine the fault phenomenon, specifically implementing a direct fault judgment function based on the vehicle's multi-dimensional state parameters. It can only identify the occurrence of powerlessness faults but cannot identify the fault cause. Step S30, the second judgment logic, aims to diagnose the cause, specifically implementing a layer-by-layer cause diagnosis function based on energy flow. Through fault tracing based on energy flow, it performs layer-by-layer troubleshooting from signal input to mechanical output according to the direction of energy flow. Step S40 can output fault cause location information at at least two levels.

[0058] In the above scheme, this disclosure does not limit the multi-dimensional state parameters and their acquisition scheme in step S10. In application scenarios, in addition to the schemes described in the following embodiments, the multi-dimensional state parameters may also include at least one of the following: total battery pack voltage, individual cell voltage, motor speed, torque command value, actual torque feedback value, high-voltage contactor status, VCU output enable signal, fault code information of each controller, and charging / discharging status information. The method of acquiring the multi-dimensional state parameters can be flexibly selected according to the actual application scenario. It can directly read the real-time reported operating data of each controller through the vehicle CAN bus, retrieve the stored historical operating data from the vehicle storage unit, or obtain the relevant data uploaded by the target vehicle from the cloud in conjunction with vehicle network communication. When the system executes step S10, it can automatically select the appropriate multi-dimensional state parameters and their acquisition scheme according to the actual vehicle configuration and control requirements to adapt to the fault diagnosis scenarios of new energy vehicles of different models and different architectures.

[0059] In the above scheme, this disclosure does not limit the first judgment logic in step S20. In application scenarios, in addition to the scheme described in the following embodiments, the first judgment logic can be set to check whether there are abnormal items in the read multi-dimensional state parameters one by one according to the preset threshold range of each parameter. Alternatively, a preset fault feature matching method can be used to compare the obtained state parameter combination with a preset no-power fault feature library to determine whether the current vehicle meets the no-power fault triggering conditions. The judgment rules of the first judgment logic can be flexibly adjusted according to the actual application scenario, which can reduce unnecessary misjudgments and improve the overall efficiency of subsequent fault location and identification while covering common no-power fault scenarios as much as possible. When the system executes step S20, it can select the appropriate first judgment logic based on the obtained multi-dimensional state parameters and the preset fault detection accuracy requirements to adapt to the diagnostic needs of new energy vehicles with different hardware configurations and different control strategies.

[0060] In the above scheme, this disclosure does not limit the second judgment logic in step 30. In application scenarios, in addition to the schemes described in the following embodiments, the second judgment logic can be set to classify abnormal items according to fault level, filter out core abnormal parameters directly related to power output, and obtain fault cause location information based on the core abnormal parameters. The judgment rules of the second judgment logic can be flexibly configured according to the output results of the first judgment logic, which can further narrow down the fault range based on the preliminary screening of the first judgment logic and improve the accuracy of the final fault location. When the system executes step S30, it can select an appropriate second judgment logic based on the acquired multi-dimensional state parameters and the preset fault location accuracy requirements to adapt to the diagnostic needs of new energy vehicles with different hardware configurations and different control strategies.

[0061] In the above scheme, this disclosure does not limit the generation scheme of the new energy vehicle powerlessness fault identification result in step 40. In the application scenario, in addition to whether the target vehicle is currently experiencing a powerlessness fault and the fault cause location information, the new energy vehicle powerlessness fault identification result can also include the target fault handling strategy and maintenance guidance information generated for the located fault cause. The maintenance guidance information can be combined with a preset fault maintenance knowledge base and matched and pushed according to the brand and model of the target vehicle and the specific fault type to help maintenance personnel quickly master the fault handling solution. At the same time, the multi-dimensional status parameters, fault judgment results, and location information obtained in this identification can also be synchronously stored in the cloud database to provide data support for the subsequent identification and optimization of similar faults. When the system executes step S40, it can select the appropriate new energy vehicle powerlessness fault identification result generation scheme according to the usage requirements of different scenarios, so that the fault identification results can meet the usage requirements of different scenarios such as ordinary car owners' self-inspection, professional diagnosis of repair shops, and manufacturer's back-end monitoring.

[0062] Some embodiments of this disclosure also provide systems, storage media, and program products corresponding to the methods described above.

[0063] The method provided by at least one embodiment of this disclosure is applicable to any existing new energy vehicle application scenario that requires real-time and accurate identification of power failures. For example, ordinary car owners can quickly obtain easy-to-understand fault descriptions and emergency handling suggestions through the vehicle's built-in infotainment system to determine whether they can continue driving or need to pull over immediately for assistance. After taking over the vehicle, repair shops can access accurate fault location and matching professional repair guidance, shortening troubleshooting time and improving repair efficiency. Meanwhile, the vehicle manufacturer's back-end monitoring platform can acquire fault data from multiple vehicles in batches, promptly identifying common design or component issues present in batches of a particular model, facilitating early recall investigations and technical optimizations.

[0064] In some embodiments, Figure 1 Based on the proposed solution, to ensure the accuracy of the judgment results of the first and second judgment logics, step S10 can be refined as follows: After the target vehicle is powered on, the current multi-dimensional state parameters of the target vehicle are acquired in real time. These multi-dimensional state parameters include at least the power system readiness state, driving intention signal, vehicle driving state parameters, actual motion response parameters, vehicle controller required torque, vehicle controller actual output torque, torque execution unit actual response torque, and wheel-end motion state parameters. The power system readiness state is used to confirm whether the vehicle's power system has entered a drivable state. The driving intention signal can extract the driver's acceleration and deceleration operation requirements. The vehicle driving state parameters reflect basic operating conditions such as gear position. The actual motion response parameters provide the actual driving state. The different dimensions of parameters work together to cover the entire process of a power failure, from driver operation, vehicle control, and execution unit output to the final wheel-end operation. This provides a complete and comprehensive data foundation for subsequent fault diagnosis, avoiding missed or incorrect judgments due to missing parameters.

[0065] In the above scheme, controller signals (including the required torque of the vehicle controller and the actual output torque of the vehicle controller) can be obtained through the CAN bus. As an alternative, controller signals can also be obtained using any of the following methods: Ethernet communication, direct acquisition of hard-wired signals, and wireless transmission. Among these, Ethernet communication is suitable for diagnostics with higher bandwidth requirements, direct acquisition of hard-wired signals is suitable for critical signals such as the accelerator pedal, increasing redundant acquisition, and wireless transmission is suitable for after-sales diagnostic scenarios.

[0066] In some embodiments, to ensure traceability of the state of each link in each level of the energy flow chain, the powertrain readiness state includes at least the state of the main positive relay (also known as the high-voltage relay state). The main positive relay is used to determine whether the target vehicle has completed the vehicle power-on operation (also known as the high-voltage operation). Driving intention signals include accelerator pedal opening signals. Vehicle driving state parameters include gear signals, such as P, R, N, and D. Actual motion response parameters include actual vehicle speed. Wheel-end motion state parameters include actual driving force and wheel speed. The above parameter combination scheme allows for a step-by-step investigation of the location of power failures along the power output chain: First, the status of the main positive relay confirms whether the power system has completed power-on initialization, eliminating false power failures caused by system incompetence; then, the accelerator pedal opening signal confirms whether the driver has a clear acceleration demand, eliminating misjudgments of demand due to incorrect operation; next, the gear position signal confirms that the vehicle is in the correct driving gear, eliminating power output failure caused by abnormal gear engagement; then, the actual vehicle speed is compared with the required vehicle speed to confirm whether the actual vehicle movement response meets expectations; finally, wheel speed detection confirms whether power is actually transmitted to the wheels, completing the parameter implementation of the entire chain and making the status of each link traceable.

[0067] In some embodiments, Figure 1 Based on the scheme, the first judgment logic in step S20 and the second judgment logic in step S30 adopt one of the following execution methods: parallel execution, serial execution, and master-slave execution.

[0068] In the parallel execution mode, the first judgment logic and the second judgment logic run in parallel.

[0069] In the serial execution mode, one of the first and second judgment logics is executed first. When the target vehicle is identified as having a power failure or the fault location information is obtained, the other of the first and second judgment logics is then triggered. For example, the first judgment logic is executed first, and after it identifies a power failure, the second judgment logic is triggered to trace the fault cause. Alternatively, the second judgment logic is executed first to monitor the health status of the energy flow link. When the second judgment logic identifies a potential fault risk (fault cause location information) at a certain level of the energy flow link, the first judgment logic is then used to confirm whether a power failure has actually occurred. The parallel execution mode is logically equivalent to the serial execution mode, both of which can achieve the dual functions of fault identification and location.

[0070] In the primary / backup execution mode, one of the first and second judgment logics is designated as the primary judgment path, and the other as the verification path. When the primary judgment path identifies a power failure in the target vehicle or obtains fault location information, the verification path is invoked for cross-verification. For example, the first judgment logic can be designated as the primary judgment path, and the second judgment logic as the backup verification path. When the first judgment logic reports a power failure, the second judgment logic is invoked for cross-verification. The final result is output only when both logics confirm the fault, thus improving diagnostic accuracy. Conversely, the second judgment logic can be designated as the primary path, and the first judgment logic as the secondary path. This adjustment of the primary / backup mode does not change the essence of multi-path collaborative work.

[0071] Figure 2 A flowchart illustrating a first determination logic provided for at least one embodiment of this disclosure. Figure 1 Based on the proposed solution, to accurately identify powerlessness faults, the multi-dimensional state parameters must include at least the driving intention signal and the actual motion response parameters, and, as... Figure 2 As shown, the first judgment logic of step S20 may further include the following sub-steps S201-S205.

[0072] Sub-step S201: Determine whether the target vehicle has completed the vehicle power-on operation.

[0073] Sub-step S202: Determine whether the target vehicle is in a non-parking state.

[0074] Sub-step S203: If the target vehicle has completed the power-on operation and its gear is in the non-parking state, determine whether the strength of the driving intention signal is greater than the preset strength threshold.

[0075] Sub-step S204: If the strength of the driving intention signal is greater than the preset strength threshold, continue timing.

[0076] Sub-step S205: When the duration of the driving intention signal being greater than a preset intensity threshold exceeds a first preset time and the actual motion response parameter is less than a preset parameter threshold, it is determined that the target vehicle is currently experiencing a power failure.

[0077] It should be noted that in the above scheme, the first judgment logic is based on rule judgment and uses actual motion response parameters as the judgment basis. The actual motion response parameters are preferably the actual vehicle speed, but other parameters can also be used to replace or supplement the judgment.

[0078] Sub-step S201 aims to determine if the target vehicle is in a drivable state, sub-step S202 aims to determine if the target vehicle is in a non-parked state, and sub-step S203 aims to determine if there is a driving intention. Only when the vehicle is in a drivable state, a non-parked state, and has a driving intention, and the strength of the driving intention signal is greater than a preset strength threshold for a duration exceeding a first preset time, if the actual motion response parameters are still lower than the preset parameter threshold at this time, the system determines that a power failure has occurred and records the frozen frame data at the time of the failure. This path is independent of complex torque calculation logic and can reliably capture intentional but unresponsive power failures. The above solution enables independent determination of whether a vehicle meets the criteria for a power failure based on the matching degree of the vehicle's basic driving conditions (powered operation and not in a vehicle-mounted state), driving intention (driving intention signal), and motion response parameters. By sequentially judging the vehicle's power-on state, gear position, and driving intention signal, the solution eliminates scenarios that might lead to a power failure. Only when the duration condition (the duration for which the intensity of the driving intention signal is greater than a preset intensity threshold exceeds a first preset time) and the motion response parameter condition (the actual motion response parameter is less than a preset vehicle speed threshold) are met can the failure be confirmed. This effectively improves the accuracy of power failure identification, avoids interference from non-fault scenarios such as the vehicle not being powered on or being parked, and makes the identification logic more closely match the actual driving conditions of the vehicle.

[0079] In some embodiments, Figure 2 Based on this scheme, the driving intention signal is selected from the accelerator pedal opening signal, with a preset intensity threshold set as the accelerator pedal opening threshold. The actual motion response parameter is selected from the actual vehicle speed, with a parameter threshold set as the vehicle speed threshold. This scheme naturally adapts to different scenarios through multiple judgments of accelerator pedal continuity and vehicle speed response. When the response is slow on slopes or under heavy loads, a first preset time can be set for adaptive adjustment.

[0080] As an exemplary implementation, the throttle opening threshold, speed threshold, and first preset time can be selected as fixed values, taking into account the influence of factors such as different vehicle models, loads, and gradients. Adjustments can be made under special operating conditions; for example, the methods for obtaining the speed threshold and the first preset time are shown in Table 1.

[0081] Table 1

[0082] In the above scheme, the first preset time T1_adjusted can be obtained by the following formula: T1_adjusted = T1_base × K_load × K_slope × K_vehicle, In the formula, the base value of the first preset time T1_base = 2 s, the coefficient K_load = 1.0 when unloaded, the coefficient K_load = 1.3~1.5 when fully loaded, the coefficient K_slope = 1.0 when on flat road, the coefficient K_slope = 1.1 when on a 5° slope, the coefficient K_slope = 1.2 when on a 10° slope, the coefficient K_vehicle = 1.0 for pure electric vehicles, and the coefficient K_vehicle = 1.2~1.53 for hybrid vehicles.

[0083] As another exemplary implementation, the throttle opening threshold, speed threshold, and first preset time can also be selected as variable values, taking into account the influence of factors such as different vehicle models, loads, and gradients.

[0084] The throttle opening threshold Thr_acc setting scheme is as follows: base value 5%, calibrable range 3%~15%, calculation formula Thr_acc = idle travel × A + sensor noise × B + margin × C, coefficient A range 1%~3%, coefficient B range 0.5%~1%, coefficient C range 1%~2%.

[0085] The first preset time T_hold setting scheme is as follows: base value 2 s, calibrable range 1~5 s, classification suggestions: 2 s for starting on flat roads, 3~4 s for starting fully loaded / on a slope, and 1.5 s for acceleration while driving. The first adjustment scheme is adaptive based on load, T_hold = base value × (1 + k × load ratio), where k is the time coefficient. The second adjustment scheme is adaptive based on slope, T_hold = base value × (1 + k × sinθ), where θ is the slope. For example, k=1, the load ratio when unloaded = 0, and the load ratio when fully loaded = 0.3~0.5.

[0086] The vehicle speed threshold V_thr is set as follows: base value 5 km / h, calibrable range 3~10 km / h.

[0087] In some embodiments, Figure 2 Based on the proposed solution, sub-step S202 is configured to execute only when the target vehicle has completed the vehicle power-on operation, ensuring that sub-steps S201-S205 are executed sequentially. This design uses the vehicle power-on status as a prerequisite condition, eliminating erroneous judgment scenarios when the vehicle is not powered on—if the vehicle is not powered on, even if the gear is in drive, subsequent fault diagnosis will not be triggered. This further simplifies the fault identification logic, reduces abnormal judgment calculations, and aligns with the fault occurrence logic under actual vehicle operating conditions, ensuring that subsequent fault diagnosis is only performed in scenarios where vehicle malfunctions are likely.

[0088] In some embodiments, Figure 2Based on the scheme, in order to reduce the probability of misjudgment in the first judgment logic, sub-step S201 further includes the following sub-steps S201a-S201c.

[0089] Sub-step S201a: Determine whether the target vehicle has completed the whole vehicle power-on operation based on the status of the main positive relay (also known as the high voltage relay status).

[0090] Sub-step S201b: If so, proceed to step S202.

[0091] Sub-step S201c: If not, exit the first judgment logic or enter the high voltage power-on diagnosis.

[0092] The process, through sub-steps S201a and S201c, quickly confirms whether the target vehicle has completed the power-on operation by checking the status of the main positive relay. If the target vehicle has not completed the high-voltage connection, it is not considered a vehicle with no power while in motion and the process exits or enters the high-voltage power-on diagnostic stage. If the high-voltage connection is completed, the process continues. By directly excluding vehicles without high-voltage connection from the first judgment logic, non-fault scenarios are filtered out in advance, reducing unnecessary computational resource consumption and lowering the probability of false judgment from the very first step. This ensures that subsequent fault identification processes only target vehicles that have completed power-on preparation, further improving the accuracy and operational efficiency of the entire no-power fault identification process.

[0093] In some embodiments, Figure 2 Based on the scheme, in order to reduce the probability of misjudgment in the first judgment logic, sub-step S202 further includes the following sub-steps S202a-S202c.

[0094] Sub-step S202a: Determine whether the gear of the target vehicle is in a non-parking state based on the gear position signal. If the gear is N or P, it is determined that the gear is in a non-parking state.

[0095] Sub-step S202b: If so, proceed to step S203.

[0096] Sub-step S202c: If not, exit the first judgment logic.

[0097] In N or P gear, the lack of power when pressing the accelerator is a normal design feature and is not considered a power failure. Sub-steps S202a-S202c pre-filter out normal power failure scenarios in the parking gear, preventing misjudgments of normal operating conditions as power failures, further narrowing down the fault investigation scope. While ensuring the accuracy of fault identification, this reduces the consumption of computing resources in subsequent processes, further improving the overall efficiency of the identification process.

[0098] In some embodiments, Figure 2Based on the proposed solution, the actual motion response parameters in sub-step S205 can be selected from at least one of the following: real-time acquired actual vehicle speed, wheel speed at the wheel end, vehicle speed converted from motor speed, acceleration signal, and GPS vehicle speed. If more than two actual motion response parameters are selected, one can be used as a supplementary judgment scheme. Wheel speed at the wheel end can be acquired using wheel speed sensors, which are direct measurements and require less computation. Vehicle speed converted from motor speed can serve as a backup scheme in case of wheel speed sensor failure. When the actual vehicle speed or the vehicle speed converted from motor speed is consistently 0, acceleration is detected for fault determination. GPS vehicle speed can be used for redundancy verification. Although these parameters come from different sources, they are all used to characterize whether the vehicle produces the expected motion response and are equivalent technical features.

[0099] Figure 3 A flowchart illustrating the second determination logic provided for at least one embodiment of this disclosure. Figure 1 or Figure 2 Based on the solution, in order to accurately locate the cause of the fault, the multi-dimensional state parameters include at least the driving intention signal, gear signal, vehicle controller demand torque, vehicle controller actual output torque, torque execution unit actual response torque and wheel end motion state parameters. The energy flow link includes the demand generation layer, control decision layer, command execution layer and mechanical transmission layer. Furthermore, the second judgment logic is configured to include the following sub-steps S301-S304.

[0100] Sub-step S301: Execute the first tracing strategy for the demand generation layer, wherein the first tracing strategy is used to identify whether there is a fault in the demand generation layer based on the driving intention signal, the gear signal and the current power demand suppression condition.

[0101] Sub-step S302: If the demand generation layer is fault-free, execute the second tracing strategy for the control decision layer, wherein the second tracing strategy is used to identify whether there is a fault in the control decision layer based on the demand torque of the vehicle controller and the actual output torque of the vehicle controller.

[0102] Sub-step S303: If there is no fault in the control decision layer, execute the third tracing strategy for the instruction execution layer, wherein the third tracing strategy is used to identify whether there is a fault in the instruction execution layer based on the actual output torque of the vehicle controller and the actual response torque of the torque execution unit.

[0103] Sub-step S304: If there is no fault in the instruction execution layer, execute the fourth-level tracing strategy for the mechanical transmission layer. The fourth-level tracing strategy is used to identify whether there is a fault in the mechanical transmission layer based on whether the actual output torque of the torque execution unit and the motion state parameters of the wheel end match.

[0104] It should be noted that the second judgment logic in the above scheme is based on rule-based judgment. Sub-step S301 aims to check whether the driver's intention is correctly transmitted; sub-step S302 aims to check whether the vehicle controller normally issues a torque request after the driver's intention is correctly transmitted; sub-step S303 aims to check whether the torque execution unit reports torque after the vehicle controller normally issues a torque request; and sub-step S304 aims to check whether the mechanical transmission layer responds correctly after the torque execution unit reports torque. The above scheme uses torque, including the torque required by the vehicle controller, the actual output torque of the vehicle controller, and the actual response torque of the torque execution unit, as the judgment basis for the second judgment logic. Torque can be replaced or supplemented by other parameters. The wheel-end motion state parameter can be either the actual driving force at the wheel end or the wheel speed at the wheel end.

[0105] In particular, by gradually checking the faults at each level of the energy flow link through sub-steps S301-S304, the fault range can be gradually narrowed down along the power transmission path, and the specific level at which the power failure occurs can be accurately located. This avoids the problems of vague fault location and low troubleshooting efficiency caused by general fault checking in related technologies. At the same time, it can also provide a clear direction for subsequent rapid repair, shorten the fault handling time, and improve the efficiency of vehicle fault repair.

[0106] In some embodiments, Figure 3 Based on the proposed solution, to improve its applicability, the torque in the second judgment logic of sub-steps S301-S304 can be replaced or supplemented by any one of the following: power value, current value, duty cycle, and pressure value. The power value can be obtained by multiplying the torque by the motor speed, which better reflects the energy transfer state. The current value can be selected from the motor phase current, which can indirectly reflect the torque output. The duty cycle can be selected from the inverter control signal, which can reflect the torque command. The pressure value can be selected for hydraulic or pneumatic actuators.

[0107] In some embodiments, Figure 3 Based on the proposed solution, to improve its applicability, in the control decision-level diagnostic process of sub-step S302, the comparison of torque values ​​can be replaced by detecting whether the PWM duty cycle command issued by the VCU matches the actual duty cycle executed by the motor controller. This parameter substitution does not change the essential logic of the hierarchical diagnostic process.

[0108] In some embodiments, Figure 3 Based on the solution, in order to accurately troubleshoot faults in the requirement generation layer, sub-step S301 is configured to include the following sub-steps S301a-S301b.

[0109] Sub-step S301a: In response to any one of the following conditions being met: the driving intention signal, the gear signal, and the current power demand suppression condition are abnormal, the driving intention signal and the current power demand suppression condition have a logical conflict, or the gear signal is abnormal, a fault is determined in the demand generation layer.

[0110] Sub-step S301b: When a fault occurs in the demand generation layer, generate first fault location information to characterize the abnormal driving intention signal or the suppression of driving intention.

[0111] In sub-steps S301a and S301b, the system checks the source of the torque request, i.e., whether the driving intention signal is normal. If an abnormal driving intention signal, such as an accelerator pedal opening signal, is detected (i.e., it is in an invalid state), or if the driving intention signal has a logical conflict with the current power demand suppression condition (such as brake priority), or if driving state parameters, such as gear position signals, are abnormal, then it is determined to be a demand generation layer fault. At this time, the vehicle controller does not receive the correct acceleration command, so no power is a reasonable protection, and the fault is located as an abnormal driving intention signal or a suppressed driving intention.

[0112] When the demand generation layer is normal (without malfunction), it means that the driver's intention has been correctly transmitted. At this time, the control decision layer located downstream of the demand generation layer should be checked, which is usually the VCU.

[0113] In some embodiments, Figure 3 Based on the scheme, in order to accurately troubleshoot control decision-making layer faults, sub-step S302 is configured to include the following sub-steps S302a-S302b for the second tracing strategy of the control decision-making layer.

[0114] Sub-step S302a: In response to the duration of the vehicle controller's required torque being greater than zero and the actual output torque of the vehicle controller being zero or less than the set torque lower limit exceeding a second preset time, a control decision layer fault is determined, wherein the set torque lower limit is greater than zero.

[0115] Sub-step S302b: When a control decision layer failure occurs, generate second fault location information to characterize the limited or no torque output of the vehicle controller.

[0116] In sub-steps S302a-S302b, after the driver's intention has been correctly transmitted, the internal mechanisms of the vehicle controller are checked. The required torque and the actual output torque of the vehicle controller can be obtained through these sub-steps. Due to limitations in the VCU's own software logic (such as limp-home mode), hardware failure, or receiving torque limitation requests from other systems (such as the battery management system reporting limited discharge power or the motor controller reporting excessive temperature drop power), the requested torque sent by the VCU to the torque execution unit may be much less than the required torque, or even zero. In this case, by comparing the required torque of the vehicle controller with the actual output torque, if the required torque of the vehicle controller is greater than zero and the actual output torque of the vehicle controller is equal to zero or less than the set lower torque limit, and this condition persists for more than a second preset time, it is determined to be a control decision-making layer fault. The cause of the fault is that the torque output of the vehicle controller is limited or non-existent. In particular, when the required torque has reached a large value but the actual output torque fails to meet the requirements for a long time, it can be determined that the fault lies in the control decision layer. The cause of the fault is located as an abnormality in the torque decision logic inside the vehicle controller or an abnormality in the output of related execution commands, which leads to the torque output not meeting the expected requirements, and thus causes the vehicle to lose power.

[0117] In the above scheme, the required torque of the vehicle controller can be calculated from the driving intention signal and the actual vehicle speed.

[0118] In the above solution, the system further tests the torque decision output of the vehicle controller, which can be refined into sub-causes such as "battery power limitation" and "motor temperature limitation". The testing method can be selected through testing.

[0119] In the above scheme, the lower limit of torque and the second preset time can be set to fixed values, without considering the influence of factors such as different vehicle models, loads, and slopes. See Table 2 for the selection of the lower limit of torque and the second preset time.

[0120] Table 2

[0121] In some embodiments, Figure 3 Based on the scheme, in addition to identifying whether there is a fault in the control decision layer, the second tracing strategy is also configured to include the following sub-steps S302c-S302d.

[0122] Sub-step S302c: If there is a fault in the control decision layer, obtain the internal fault code of the vehicle controller in the target vehicle.

[0123] Sub-step S302d: Based on the internal fault codes of the vehicle controller, identify whether the current control decision layer fault is caused by a component fault in the control decision layer, which may result in a torque limiting condition. The component may include at least one of the battery management system and the motor controller.

[0124] Sub-steps S302c and S302d can be placed after sub-step S302b. Sub-steps S302c and S302d can further identify whether the torque limitation is caused by a fault in a control decision-making layer component, such as a fault in the three-electric components (MCU and BMS) of new energy vehicles. This step, after determining that a fault exists in the control decision-making layer, can further distinguish the root cause of the fault, avoiding misjudging the torque anomaly caused by the transmission of component faults as a fault in the decision logic of the vehicle controller itself. This effectively improves the accuracy of fault location, reduces the probability of misjudgment in subsequent maintenance and troubleshooting, and helps maintenance personnel quickly pinpoint the true source of the fault.

[0125] It should be noted that active torque reduction by an external system will not misjudge a control decision-making layer fault. Active torque reduction by an external system will generate messages or fault codes, allowing for the identification of the faulty component by tracing the signal source. A "either / or" approach can be used here: if the fault is not caused by an external system, then the fault is determined to be at the control decision-making layer.

[0126] When the control decision layer is functioning normally (without faults), continue to troubleshoot the faults in the instruction execution layer downstream of the control decision layer.

[0127] In some embodiments, Figure 3 Based on the solution, in order to accurately troubleshoot instruction execution layer faults, sub-step S303 is configured to include the following sub-steps S303a-S303b.

[0128] Sub-step S303a: In response to the vehicle controller's actual output torque (also known as VCU requested torque) being greater than zero and the torque execution unit's actual response torque being zero, or the deviation between the torque execution unit's actual response torque and the vehicle controller's actual output torque being less than the set torque deviation, a fault is determined in the command execution layer.

[0129] Sub-step S303b: In the event of a fault at the instruction execution layer, generate third fault location information to characterize the lack of response of the torque execution unit.

[0130] In sub-steps S303a-S303b, after the VCU has successfully issued a torque request (e.g., requesting 100Nm), the process checks whether the torque execution unit (MAU) has executed it. By comparing the actual output torque of the vehicle controller with the actual response torque of the MAU, if the actual output torque of the vehicle controller is greater than zero, but the actual response torque of the MAU is zero or the deviation from the actual output torque of the vehicle controller exceeds the set torque deviation (e.g., requesting 100Nm, feedback <10Nm), and there is no legitimate reason (e.g., the motor actively cuts off), then it is determined to be a command execution layer fault, and the cause of the fault is that the torque execution unit is not responding. This situation may be due to the torque execution unit freezing, communication interruption, or internal power transistor failure. This solution can accurately locate faults in scenarios where the vehicle controller has output the correct torque command, but the torque execution unit fails to respond normally, avoiding misjudging such faults as command sending layer faults, narrowing the scope of fault investigation, improving the accuracy of fault identification, and providing clear guidance for subsequent fault repair, helping maintenance personnel quickly locate the fault and shorten the troubleshooting and repair time.

[0131] In the above scheme, the torque deviation can be set to a fixed value, without considering the influence of factors such as different vehicle models, loads and slopes. For example, the typical value is 10 Nm, and the typical range is 5~20 Nm.

[0132] If the instruction execution layer is normal (fault-free), continue troubleshooting down to the mechanical transmission layer downstream of the instruction execution layer.

[0133] In some embodiments, Figure 3 Based on the solution, in order to accurately troubleshoot mechanical transmission layer faults, sub-step S304 is configured to include the following sub-steps S304a-S304b.

[0134] Sub-step S304a: In response to the actual output torque of the torque actuator being non-zero and the wheel end having no rotational speed related to the actual driving force at the wheel end, or the actual vehicle speed not matching the actual output torque of the torque actuator, a mechanical transmission layer fault is determined.

[0135] Sub-step S304b: When a mechanical transmission layer failure occurs, generate fourth fault location information to characterize output shaft abnormality, wheel lock-up, or transmission system jamming.

[0136] In this process, sub-steps S304a and S304b investigate the mechanical transmission layer when the torque actuator reports torque output (e.g., the motor actually outputs 100 Nm) but the vehicle still lacks power (vehicle speed is low or zero). The process checks whether the actual output torque of the torque actuator matches the actual driving force at the wheel end. For example, if the torque actuator has torque output but the wheel end has no rotational speed, or the actual vehicle speed is significantly inconsistent with the torque actuator's output torque, or if the ratio of the rotational speed converted from the actual output torque of the torque actuator to the wheel end rotational speed is abnormal (outside the set ratio range), or if an abnormal wheel speed sensor signal is detected, then a mechanical transmission layer fault is identified. The fault could be due to mechanical failures such as an abnormal output shaft (broken half-shaft), transmission system jamming (reducer gear wear), or wheel lockup (brake lockup). This solution avoids misjudging mechanical jamming and lockup faults as powertrain failures, improving the accuracy of fault location, helping maintenance personnel quickly pinpoint the fault location, and shortening troubleshooting and repair time.

[0137] Figure 4 A flowchart illustrating another method for identifying non-powered faults in new energy vehicles, provided for at least one embodiment of this disclosure. Figures 1-3 Based on any given solution, in order to trigger differentiated safety responses according to different fault causes, such as Figure 4 As shown, the method further includes the following step S50.

[0138] Step S50: When the target vehicle is currently experiencing a power failure, execute the target fault handling strategy that matches the fault cause location information and output the corresponding maintenance guidance information. The target fault handling strategy is one of a number of preset alternative fault handling strategies, and different alternative fault handling strategies and maintenance guidance information are matched with different fault cause location information.

[0139] In step S50, a suitable fault handling solution can be directly matched based on the specific fault cause obtained in advance, without the need for maintenance personnel to check the possibility of fault one by one before determining the handling direction. At the same time, the corresponding maintenance guidance information is output, which can intuitively guide maintenance personnel to carry out maintenance work according to the guidance, further improving the efficiency of fault repair, reducing the dependence on the experience level of maintenance personnel, and making fault repair more standardized.

[0140] In some embodiments, Figures 1-4Based on any of the proposed solutions, the fault cause location information in the new energy vehicle power failure identification results can be replaced or supplemented by fault codes or fault level codes. The fault codes can be decoded by external diagnostic equipment to display the specific fault cause. Fault level codes, such as "L1" representing the demand generation layer and "L2" representing the control decision layer, allow maintenance personnel to consult tables in the maintenance manual for detailed causes. This coded output method is essentially equivalent to the direct output method in terms of information transmission.

[0141] In some embodiments, Figures 1-4 Based on either approach, the judgment results of the two judgment logics in the new energy vehicle powerlessness fault identification result are merged into a comprehensive diagnostic conclusion. For example, when the first judgment logic reports a powerlessness fault and the second judgment logic locates a control layer fault, the comprehensive output is "abnormal VCU torque output causes the vehicle to lose power". This fused output does not change the fact of dual-path diagnosis.

[0142] In some embodiments, Figures 1-4 Based on any given solution, the first and second judgment logics can be model-based. The first judgment logic uses a vehicle dynamics reference model to calculate the desired vehicle speed in real time and compares it with the actual speed. When the deviation between the desired and actual speed consistently exceeds the allowable range, a fault is triggered in the first judgment logic. The second judgment logic uses a torque link reference model to calculate the expected value of each component and compares it with the actual value of the corresponding component, using deviation analysis to pinpoint the fault level. Both this model-based and rule-based approach essentially involve capturing fault phenomena and tracing fault causes.

[0143] In some embodiments, Figures 1-4 Based on the scheme, all preset parameters in the first and second judgment logics are dynamic thresholds. For example, the preset intensity threshold, parameter threshold, and first preset time in the first judgment logic are adaptively adjusted according to the current working conditions, such as relaxing the first preset time when the temperature is low.

[0144] In some embodiments, Figures 1-4 Based on the proposed solution, the preset parameters in the first and second judgment logics can be replaced by trends. For example, instead of comparing with a fixed threshold, the system can detect whether the rate of change of vehicle speed is consistently negative or close to zero.

[0145] In some embodiments, Figures 1-4 Based on the proposed solution, the first and second judgment logics can adopt the same logic as those used for motorcycle protection. For example, the inputs such as throttle opening signal and vehicle speed change rate can be fuzzified, and the fault confidence level can be output through a fuzzy rule table.

[0146] In some embodiments, Figures 1-4Based on the scheme, sub-step S205 can also use fuzzy judgment logic to fuzzify the inputs such as throttle opening and vehicle speed change rate, and output the fault confidence through the fuzzy rule table.

[0147] Figure 5 This is a flowchart illustrating another method for identifying powerless faults in new energy vehicles, provided in at least one embodiment of this disclosure. Figures 1-4 Based on any one of the options, such as Figure 5 As shown, the method further includes the following steps S01 and S02.

[0148] Step S01: Adjust multiple levels of the energy flow link, wherein the adjustment includes at least one of level merging and level subdivision.

[0149] Step S02: Use the adjusted energy flow path for the second judgment logic.

[0150] Step S01 can be placed before step S10, and step S02 can be placed before step S10 or between steps S30 and S40. Steps S01 and S02 allow this fault identification method to adapt to different diagnostic scenarios. When rapid preliminary fault diagnosis is required, the energy flow links are hierarchically merged according to the current diagnostic needs, reducing the number of nodes checked in a single check, shortening diagnostic time, and improving preliminary diagnosis efficiency. When precise location of specific fault points is required, the relevant link levels are subdivided according to the current diagnostic needs, breaking down into more detailed inspection nodes, thereby improving the accuracy of fault location. This makes the entire fault identification method both flexible and adaptable, better meeting the needs of non-powered fault diagnosis in different scenarios.

[0151] In some embodiments, Figure 5 Based on the scheme, step S01 further includes the following sub-step S011 to implement the first hierarchical merging scheme.

[0152] Sub-step S011: Merge the demand generation layer and the control decision layer into an instruction generation layer, while keeping the instruction execution layer and the mechanical transmission layer unchanged.

[0153] The sub-step S011 enables a three-layer diagnostic process: instruction generation layer → instruction execution layer → mechanical transmission layer. This three-layer architecture significantly reduces the number of diagnostic nodes, simplifies the diagnostic process, and enables the screening of a wide range of fault segments in a short time. It quickly locates the specific level of the fault, meeting the diagnostic needs for initial rapid troubleshooting. It is suitable for initial diagnostic scenarios where the fault range is completely unclear, rapidly narrowing down the fault investigation scope and improving the speed of identifying non-powered faults.

[0154] In some embodiments, Figure 5Based on the scheme, step S01 further includes the following sub-step S012 to implement the second hierarchical merging scheme.

[0155] Sub-step S012: Merge the control decision layer and the instruction execution layer into a response layer, while keeping the demand generation layer and the mechanical transmission layer unchanged.

[0156] In this process, sub-step S012 can form a three-layer diagnosis: demand generation layer → response layer → mechanical transmission layer. This layered merging method is designed for scenarios where the fault is suspected to occur in the original decision control and instruction execution interface. After merging, there is no need to perform additional verification on the interface between the two layers, reducing redundant cross-checking steps. It can directly focus on the two interface links between demand generation and response execution, and response execution and mechanical transmission for targeted troubleshooting. This is more suitable for diagnostic scenarios where there is already an initial fault direction and the problem is suspected to be in the intermediate control execution link. It controls the number of diagnostic nodes and focuses on the suspected fault area to improve troubleshooting efficiency.

[0157] In some embodiments, Figure 5 Based on the scheme, step S01 further includes the following sub-step S013 to implement the third hierarchical merging scheme.

[0158] Sub-step S013: Merge the instruction execution layer and the mechanical transmission layer into a response layer, while keeping the demand generation layer and the control decision layer unchanged.

[0159] Among them, sub-step S013 can form a three-layer diagnosis: demand generation layer → control decision layer → response layer.

[0160] Although the first, second, and third level merging schemes mentioned above reduce the number of diagnostic levels, they still retain the core logic of checking step by step along the energy flow direction, which can achieve the initial location of the fault link. They are equivalent alternatives to the second judgment logic of the four-level diagnosis.

[0161] In some embodiments, Figure 5 Based on the scheme, step S01 further includes the following sub-step S014 to implement the first hierarchical subdivision scheme.

[0162] Sub-step S014: Subdivide the control decision layer into a torque demand calculation layer and a torque output limiting layer. The torque demand calculation layer is used to diagnose torque degradation caused by VCU software logic faults, and the torque output limiting layer is used to diagnose torque degradation caused by component limitations.

[0163] In particular, sub-step S014 can split and adjust the original four-layer structure into a five-layer diagnostic architecture: demand generation layer → torque demand calculation layer → torque output limitation layer → command execution layer → mechanical transmission layer. During diagnosis, it can make more detailed fault differentiation for different logical nodes within the decision control link. It is especially suitable for suspected fault scenarios where the vehicle has power output but the power does not meet the expected demand. It can help maintenance personnel quickly locate whether the fault is in the software calculation logic level or in the limitation level of specific components. After the initial investigation concludes that the fault is located in the decision control link, it can further narrow down the fault range, improve the accuracy of fault location, and adapt to the needs of refined diagnosis. It is an improved judgment logic that is extended from the four-layer diagnosis.

[0164] In some embodiments, Figure 5 Based on the scheme, step S01 further includes the following sub-step S015 to realize the second hierarchical subdivision scheme.

[0165] Sub-step S015: Subdivide the instruction execution layer into a communication transmission layer and a power response layer. The communication transmission layer is used to diagnose CAN communication faults, and the power response layer is used to diagnose faults in the power devices inside the motor controller.

[0166] In particular, sub-step S015 can adjust the original four-layer structure into a five-layer diagnostic architecture: demand generation layer → control decision layer → communication transmission layer → power response layer → mechanical transmission layer. This architecture can more clearly classify faults for different functional nodes within the command execution stage, adapting to scenarios where power output is abnormal but the source of the fault is difficult to determine. It helps maintenance personnel quickly distinguish whether the fault is in the signal transmission path or in the power execution device itself after initially locating the fault in the command execution stage, further narrowing the scope of fault investigation, reducing diagnostic difficulty, and improving location efficiency. It also meets more refined fault diagnosis needs on the original basis.

[0167] In some embodiments, Figure 5 Based on the scheme, step S01 further includes the following sub-step S016 to realize the third hierarchical subdivision scheme.

[0168] Sub-step S016: Subdivide the mechanical transmission layer into a drive shaft layer and a wheel layer, wherein the drive shaft layer is used to diagnose half-shaft breakage and the wheel layer is used to diagnose brake lock-up.

[0169] In particular, sub-step S016 can adjust the original four-layer structure into a five-layer diagnostic architecture: demand generation layer → control decision layer → instruction execution layer → transmission shaft layer → wheel layer. This architecture can further differentiate faults based on the functional differences of different components within the mechanical transmission link, adapting to fault scenarios where power output is interrupted but external characteristics are not obvious. After initially locating the fault in the mechanical transmission link, maintenance personnel can quickly distinguish whether the fault comes from the transmission connection or the wheel braking unit, further improving the accuracy of fault location and avoiding unnecessary disassembly and repair. It also meets more refined fault diagnosis needs on the basis of the original architecture.

[0170] The first, second, and third hierarchical subdivision schemes mentioned above further subdivide a certain level to form a more multi-level diagnostic structure. This subdivision scheme is completely consistent with the four-level classification in terms of technical concept.

[0171] In some embodiments, Figure 5 Based on the proposed solution, step S01 can also employ a method of merging some levels and further subdividing others. For example, while maintaining the hierarchical division between the demand generation layer and the control decision layer, the originally independent instruction execution layer can be merged downwards into the mechanical transmission layer based on the functional correlation of components. Simultaneously, the mechanical transmission layer can be further subdivided into three sub-layers according to the power transmission path: power output unit, transmission connection unit, and terminal execution unit. This results in a five-layer diagnostic architecture: demand generation layer → control decision layer → power output unit → transmission connection unit → terminal execution unit. This adjustment simplifies the redundant hierarchical division between instruction execution and mechanical transmission, and provides a more detailed breakdown of the frequently faulty mechanical transmission components. It adapts to different vehicle models and non-powered fault identification scenarios with varying diagnostic accuracy requirements, flexibly adapting to diverse vehicle diagnostic needs while retaining the core idea of ​​hierarchical diagnosis.

[0172] Figure 6 A flowchart illustrating an example of a method for identifying a powerless fault in a new energy vehicle, provided in at least one embodiment of this disclosure. Figure 6 As shown, an example of this method includes the following steps.

[0173] 1) After the target vehicle is powered on, its multi-dimensional status parameters are acquired in real time. These parameters include at least the powertrain readiness status, driving intention signal, vehicle driving status parameters, actual motion response parameters, vehicle controller required torque, vehicle controller actual output torque, torque execution unit response torque, and wheel-end motion status parameters. The powertrain readiness status includes the status of the main positive relay used to confirm whether high voltage is applied. The driving intention signal includes the accelerator pedal opening signal. Vehicle driving status parameters include the gear signal. Actual motion response parameters include the actual vehicle speed. The vehicle controller required torque is the torque calculated internally by the VCU. The vehicle controller actual output torque is the requested torque sent by the VCU to the torque execution unit. The torque execution unit response torque is the actual executed torque fed back by the torque execution unit. Wheel-end motion status parameters include wheel speed.

[0174] 2) The first judgment logic is initiated to independently determine whether a vehicle has experienced a power failure based on the vehicle's basic driving conditions, the matching degree between driving intention and motion response. This first judgment logic is a direct, phenomenon-based judgment suitable for quickly capturing power failure events. It includes: determining whether the target vehicle has completed the vehicle power-on operation; determining whether the target vehicle is in a non-parking state; if the vehicle has completed the vehicle power-on operation, is in a non-parking state, and has a driving intention, and the duration of the driving intention signal strength exceeding a preset strength threshold (e.g., throttle opening > 5%) exceeds a first preset time (e.g., first preset time = 2 s), and if the actual vehicle speed is still lower than a preset speed threshold (e.g., preset speed threshold = 5 km / h), then the system determines that a power failure has occurred and records the frozen frame data at the time of the failure. This path is independent of complex torque calculation logic and can reliably capture intentional but unresponsive power failure phenomena.

[0175] 3) Execute the second judgment logic to perform hierarchical fault tracing based on the energy flow link. This path follows the direction of energy flow, from signal input to mechanical output, performing hierarchical investigation in sequence: demand generation layer, control decision layer, command execution layer, and mechanical transmission layer. The main judgment parameters for the demand generation layer include the driving intention signal. The main judgment parameters for the control decision layer include the vehicle controller's required torque and the vehicle controller's actual output torque. The main judgment parameters for the command execution layer include the vehicle controller's actual output torque and the torque execution unit's actual response torque. The main judgment parameters for the mechanical transmission layer include the execution unit's actual output torque and wheel-end motion state parameters.

[0176] 4) Combine the results of at least two judgment paths and output the power failure identification result, which includes fault cause location information.

[0177] 5) Based on the location information of different fault causes, implement differentiated fault handling strategies and different maintenance guidance information. For example, if the first judgment logic reports a no-power fault, and the second judgment logic traces the fault to the instruction execution layer, then the system will ultimately output a no-power fault, the cause of which is that the torque actuator is not responding. Based on this accurate result, maintenance personnel can quickly troubleshoot the torque actuator and its wiring, rather than blindly replacing parts.

[0178] Figure 7 This is a structural block diagram of a new energy vehicle power failure identification system provided in at least one embodiment of this disclosure. Figure 7 As shown, the new energy vehicle no-power fault identification system 100 integrates an acquisition unit 101, a first processing unit 102, a second processing unit 103, and a result generation unit 104.

[0179] The acquisition unit 101 is configured to acquire the current multidimensional state parameters of the target vehicle.

[0180] The first processing unit 102 is configured to execute a preset first judgment logic, wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on multi-dimensional state parameters.

[0181] The second processing unit 103 is configured to execute a second judgment logic that runs independently of the first judgment logic. The second judgment logic is used to perform hierarchical fault tracing on the energy flow links with multiple levels in the target vehicle based on multi-dimensional state parameters in order to obtain fault cause location information.

[0182] The result generation unit 104 is configured to generate a new energy vehicle power failure identification result based on the judgment results of the first judgment logic and the second judgment logic, which includes whether the target vehicle is currently experiencing a power failure and the location information of the cause of the failure.

[0183] The specific execution methods of each unit in the above system embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0184] In some embodiments, Figure 7 Based on the scheme, the acquisition unit 101 can be implemented by a corresponding sensor or receiving module, and the first processing unit 102, the second processing unit 103 and the result generation unit 104 can be implemented by a controller with a corresponding program.

[0185] In some embodiments, Figure 7Based on the solution, the new energy vehicle powerless fault identification system 100 also integrates an energy flow link adjustment unit. The energy flow link adjustment unit is configured to adjust multiple levels of the energy flow link, wherein the adjustment includes at least one of level merging and level subdivision, and the adjusted energy flow link is used for a second judgment logic.

[0186] In some embodiments, Figure 7 Based on the solution, the new energy vehicle power failure identification system 100 also integrates a control unit. The control unit is configured to execute a target fault handling strategy that matches the fault cause location information when the target vehicle is currently experiencing a power failure, and output corresponding maintenance guidance information. The target fault handling strategy is one of a number of preset candidate fault handling strategies, and different candidate fault handling strategies and maintenance guidance information are matched with different fault cause location information.

[0187] This disclosure also provides a storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method embodiments described above.

[0188] This disclosure also provides a program product, such as... Figure 8 As shown, the program product includes one or more processors 201 and memory 202. Figure 8 Take a processor 201 as an example.

[0189] The controller may also include an input device 203 and an output device 204.

[0190] The processor 201, memory 202, input device 203, and output device 204 can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0191] Processor 201 can be a central processing unit (CPU), or it can be 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, or combinations of the above types of chips. The general-purpose processor can be a microprocessor or any conventional processor.

[0192] The memory 202, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 201 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 202, thereby implementing the steps of the above-described method embodiments.

[0193] The memory 202 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the use of the processing device operated by the server. Furthermore, the memory 202 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 202 may optionally include memory remotely located relative to the processor 201, and these remote memories can be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0194] Input device 203 can receive input digital or character information, and generate key signal inputs related to driver settings and function control of the server's processing unit. Output device 204 may include display devices such as a display screen.

[0195] One or more modules are stored in memory 202, and when executed by one or more processors 201, they perform actions such as... Figure 1 The method shown.

[0196] Those skilled in the art will understand that all or part of the processes in the above method embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory (FM), hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0197] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and all such modifications and variations fall within the scope defined by the appended claims.

[0198] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for identifying power failure in new energy vehicles, characterized in that, include: Obtain the current multidimensional state parameters of the target vehicle; Execute a preset first judgment logic, wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on the multi-dimensional state parameters; A second judgment logic, operating independently of the first judgment logic, is executed. This second judgment logic is used to perform hierarchical fault tracing of the energy flow links with multiple levels in the target vehicle based on the multi-dimensional state parameters, in order to obtain fault cause location information; and... Based on the judgment results of the first judgment logic and the second judgment logic, a new energy vehicle power failure identification result is generated, which includes whether the target vehicle is currently experiencing a power failure and the location information of the cause of the failure. The multi-dimensional state parameters include driving intention signals, gear signals, vehicle controller demand torque, vehicle controller actual output torque, torque execution unit actual response torque, and wheel-end motion state parameters. The energy flow link includes a demand generation layer, a control decision layer, an instruction execution layer, and a mechanical transmission layer. Furthermore, the second judgment logic is configured as follows: A first tracing strategy is executed for the demand generation layer, wherein the first tracing strategy is used to identify whether the demand generation layer has a fault based on the driving intention signal, the gear signal and the current power demand suppression condition; If the demand generation layer is fault-free, a second tracing strategy is executed for the control decision layer, wherein the second tracing strategy is used to identify whether the control decision layer is faulty based on the vehicle controller's required torque and the vehicle controller's actual output torque. If the control decision layer is fault-free, a third tracing strategy is executed for the instruction execution layer, wherein the third tracing strategy is used to identify whether the instruction execution layer is faulty based on the actual output torque of the vehicle controller and the actual response torque of the torque execution unit; and If the instruction execution layer is fault-free, the fourth-level tracing strategy for the mechanical transmission layer is executed. The fourth-level tracing strategy is used to identify whether the mechanical transmission layer is faulty based on whether the actual output torque of the torque execution unit and the motion state parameters of the wheel end match. Furthermore, in addition to identifying whether the control decision layer has a fault, the second tracing strategy is also configured as follows: If the control decision layer malfunctions, obtain the internal fault code of the vehicle controller in the target vehicle; and... Based on the internal fault codes of the vehicle controller, it is determined whether the current control decision layer fault is caused by a component failure in the control decision layer, resulting in a torque limiting condition. The component includes at least one of the battery management system and the motor controller.

2. The method according to claim 1, characterized in that, Also includes: When the target vehicle experiences a power failure, a target fault handling strategy matching the fault cause location information is executed, and corresponding maintenance guidance information is output. The target fault handling strategy is one of a number of preset candidate fault handling strategies, and different candidate fault handling strategies and maintenance guidance information are matched with different fault cause location information.

3. The method according to claim 1 or 2, characterized in that, Also includes: Adjustments are made to multiple levels of the energy flow link, wherein the adjustments include at least one of level merging and level subdivision; as well as, The adjusted energy flow path is used in the second judgment logic.

4. The method according to claim 1 or 2, characterized in that, The multidimensional state parameters also include actual motion response parameters, and the first judgment logic is configured as follows: Determine whether the target vehicle has completed the vehicle power-on operation; Determine whether the target vehicle is in a non-parking state. If the target vehicle has completed the vehicle power-on operation and its gear is in the non-parking state, determine whether the strength of the driving intention signal is greater than a preset strength threshold. If the strength of the driving intention signal is greater than the preset strength threshold, the timer continues. as well as, When the duration for which the intensity of the driving intention signal is greater than the preset intensity threshold exceeds a first preset time and the actual motion response parameter is less than the preset parameter threshold, it is determined that the target vehicle is currently experiencing a power failure.

5. The method according to claim 1 or 2, characterized in that, The first judgment logic and the second judgment logic adopt one of the following execution methods: Parallel execution mode, wherein the first judgment logic and the second judgment logic run in parallel; In the serial execution mode, one of the first judgment logic and the second judgment logic is executed first. When the target vehicle is identified as having a power failure or the fault location information is obtained, the other of the first judgment logic and the second judgment logic is then triggered. In the primary / backup execution mode, one of the first judgment logic and the second judgment logic is set as the primary judgment path and the other as the verification path. When the primary judgment path identifies that the target vehicle is currently experiencing a power failure or obtains the fault location information, the verification path is invoked for cross-verification.

6. The method according to claim 1 or 2, characterized in that, The multidimensional state parameters include: The power system readiness state includes the status of the main positive relay used to determine whether the target vehicle has completed the vehicle power-on operation; The driving intention signal includes an accelerator pedal opening signal; Vehicle driving status parameters, which include gear position signals; Actual motion response parameters, which include actual vehicle speed; The required torque of the vehicle controller and the actual output torque of the vehicle controller; The torque execution unit actually responds to the torque; and The wheel end motion state parameters include the actual driving force at the wheel end and the wheel speed at the wheel end.

7. A new energy vehicle power failure identification system, characterized in that, include: The acquisition unit is configured to acquire the current multidimensional state parameters of the target vehicle. The first processing unit is configured to execute a preset first judgment logic, wherein the first judgment logic is used to identify whether the target vehicle is currently experiencing a power failure based on the multi-dimensional state parameters; The second processing unit is configured to execute a second judgment logic that operates independently of the first judgment logic. This second judgment logic is used to perform hierarchical fault tracing of the energy flow links with multiple levels in the target vehicle based on the multi-dimensional state parameters, in order to obtain fault cause location information; and... The result generation unit is configured to generate a new energy vehicle power failure identification result based on the judgment results of the first judgment logic and the second judgment logic, which includes whether the target vehicle is currently experiencing a power failure and the location information of the cause of the failure. The multi-dimensional state parameters include driving intention signals, gear signals, vehicle controller demand torque, vehicle controller actual output torque, torque execution unit actual response torque, and wheel-end motion state parameters. The energy flow link includes a demand generation layer, a control decision layer, an instruction execution layer, and a mechanical transmission layer. Furthermore, the second judgment logic is configured as follows: A first tracing strategy is executed for the demand generation layer, wherein the first tracing strategy is used to identify whether the demand generation layer has a fault based on the driving intention signal, the gear signal and the current power demand suppression condition; If the demand generation layer is fault-free, a second tracing strategy is executed for the control decision layer, wherein the second tracing strategy is used to identify whether the control decision layer is faulty based on the vehicle controller's required torque and the vehicle controller's actual output torque. If the control decision layer is fault-free, a third tracing strategy is executed for the instruction execution layer, wherein the third tracing strategy is used to identify whether the instruction execution layer is faulty based on the actual output torque of the vehicle controller and the actual response torque of the torque execution unit; and If the instruction execution layer is fault-free, the fourth-level tracing strategy for the mechanical transmission layer is executed. The fourth-level tracing strategy is used to identify whether the mechanical transmission layer is faulty based on whether the actual output torque of the torque execution unit and the motion state parameters of the wheel end match. Furthermore, in addition to identifying whether the control decision layer has a fault, the second tracing strategy is also configured as follows: If the control decision layer malfunctions, obtain the internal fault code of the vehicle controller in the target vehicle; and... Based on the internal fault codes of the vehicle controller, it is determined whether the current control decision layer fault is caused by a component failure in the control decision layer, resulting in a torque limiting condition. The component includes at least one of the battery management system and the motor controller.

8. A storage medium, characterized in that, The storage medium stores a program, wherein the program, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 6; or, the storage medium stores instructions, wherein the instructions, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

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