A safety power-off control method and system for a new energy vehicle

Abstract

The application discloses a kind of safety power-off control method and system for new energy automobile, which comprises: based on the voltage and current signal of each electrical branch;The signals of the same branch are processed, the real-time phase difference between voltage and current and its rate of change with time are calculated;According to the comparison result of the phase difference and rate of change with preset threshold, whether the branch exists electrical anomaly is identified, and the abnormal degree is classified;According to the abnormal classification, perform graded power-off control: if it is first-level abnormality, the corresponding abnormal branch is disconnected;If it is second-level abnormality, the output power of the whole vehicle high-voltage system is limited while the abnormal branch is disconnected.The application can realize accurate identification and differentiated safety control of local fault without adding new sensors, effectively avoid the whole vehicle blind power-off, and improve the driving safety and system availability.

Description

Technical Field

[0001] This invention relates to the field of power-off control technology for new energy vehicles, specifically a safe power-off control method and system for new energy vehicles. Background Technology

[0002] The high-voltage electrical system of new energy vehicles comprises multiple parallel-operating branches, such as the power drive, auxiliary motor, air conditioning compressor, and electric heater. These branches are connected to the power battery via a high-voltage bus, forming the core network for the vehicle's energy supply. To ensure system safety, existing technologies typically employ a global power-off strategy when electrical anomalies such as short circuits or overcurrents are detected. This involves disconnecting the main relay, causing the entire high-voltage system to lose power completely.

[0003] However, in practice, most electrical faults are localized, occurring only in one or a few branches. For example, poor contact in the air conditioning compressor circuit or decreased insulation in the auxiliary motor branch. If a complete power outage is performed in such a situation, critical functions such as the drive motor and electric power steering will be lost. The vehicle may be unable to safely pull over due to the loss of power and braking assistance, especially at high speeds, posing a serious secondary safety risk. Furthermore, a complete power outage makes fault self-diagnosis and localization difficult, increasing the complexity and cost of subsequent repairs.

[0004] To address these issues, some improvement solutions attempt to more precisely locate faulty branches by adding extra current sensors or insulation detection devices. However, these solutions not only increase hardware costs and system complexity, but their reliability is also susceptible to sensor malfunctions or electromagnetic interference, making them difficult to apply stably in complex automotive environments.

[0005] Therefore, how to identify local faults and implement differentiated safety control using only the sampling signals of the existing high-voltage system, so as to maintain the basic driving capability of the vehicle to the greatest extent while ensuring the safety of personnel, has become an urgent technical problem to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a safe power-off control method and system for new energy vehicles to solve the problems mentioned in the background art.

[0007] In a first aspect, the present invention provides a safety power-off control method for new energy vehicles, comprising:

[0008] Acquire real-time voltage and current signals of each electrical branch in the high-voltage system;

[0009] The voltage and current signals from the same branch are processed to calculate the real-time phase difference between the voltage and current of that branch and its rate of change over time.

[0010] Based on the comparison results of the real-time phase difference and its rate of change with the preset threshold, the electrical abnormality of the corresponding branch is identified, and the degree of abnormality is classified. The classification includes at least a first-level abnormality that represents a local abnormality and a second-level abnormality that represents the spread of the abnormality.

[0011] Based on the anomaly classification, a graded power-off control is implemented. When a first-level anomaly is identified, the corresponding abnormal branch is disconnected. When a second-level anomaly is identified, the corresponding abnormal branch is disconnected and the output power of the vehicle's high-voltage system is limited.

[0012] Furthermore, the classification of the degree of anomaly includes: when the real-time phase difference or its rate of change exceeds a first preset threshold but the duration is less than a first time threshold, it is determined to be a first-level anomaly; when the real-time phase difference continuously exceeds the first preset threshold or the fluctuation amplitude exceeds a second preset threshold and the duration reaches a second time threshold, it is determined to be a second-level anomaly.

[0013] Furthermore, the method also includes: based on bus voltage fluctuations, when at least two branches are identified to be abnormal at the same time and the rate of decrease of the bus voltage exceeds a third preset threshold, it is determined to be a level 3 anomaly characterizing global risk;

[0014] In response to a Level 3 anomaly, a global power outage is executed, controlling the disconnection of the high-voltage system and all branches.

[0015] Furthermore, the method also includes: when at least two branches are identified as having anomalies, based on the starting timing of the phase difference change of each branch and the synchronicity of its change with the bus voltage fluctuation, inferring the abnormal source branch among multiple abnormal branches, and when executing the graded power-off control, prioritizing the execution of power-off control on the inferred abnormal source branch.

[0016] Furthermore, the inference of the abnormal source branch specifically includes: based on the time-series causal linked list, if the phase difference change of the branch starts earlier than other branches and its change characteristics are most synchronous with the start time of the bus voltage fluctuation, then the branch is presumed to be the abnormal source branch, wherein the time-series causal linked list is constructed based on the phase difference change events of each abnormal branch.

[0017] Furthermore, after executing the power-off control, the process also includes: confirming the completion of the disconnection action based on the status feedback signal of the corresponding relay; waiting for a preset delay reset time after the abnormal signal disappears; and if no new abnormality is detected during this period, the control is restored to normal operation.

[0018] Secondly, the present invention provides a safety power-off control system for new energy vehicles, using the safety power-off control method for new energy vehicles described in the first aspect, including:

[0019] The signal acquisition module is used to acquire the real-time voltage and current signals of each electrical branch in the high-voltage system.

[0020] The feature calculation module processes voltage and current signals from the same branch to calculate the real-time phase difference between the voltage and current of that branch and its rate of change over time.

[0021] The anomaly identification module identifies whether there is an electrical anomaly in the corresponding branch based on the comparison result of the real-time phase difference and its rate of change with a preset threshold, and classifies the degree of anomaly. The classification includes at least a first-level anomaly that represents a local anomaly and a second-level anomaly that represents the spread of the anomaly.

[0022] The power-off control module performs graded power-off control based on the anomaly classification. When a first-level anomaly is identified, the corresponding abnormal branch is disconnected. When a second-level anomaly is identified, the corresponding abnormal branch is disconnected and the output power of the vehicle's high-voltage system is limited.

[0023] Furthermore, the classification of the degree of anomaly includes: when the real-time phase difference or its rate of change exceeds a first preset threshold but the duration is less than a first time threshold, it is determined to be a first-level anomaly; when the real-time phase difference continuously exceeds the first preset threshold or the fluctuation amplitude exceeds a second preset threshold and the duration reaches a second time threshold, it is determined to be a second-level anomaly.

[0024] Furthermore, it also includes an inference module, which, when at least two branches are identified as having anomalies, infers the abnormal source branch among multiple abnormal branches based on the starting timing of the phase difference change of each branch and the synchronicity of its change with the bus voltage fluctuation, and prioritizes the execution of power-off control on the inferred abnormal source branch when the graded power-off control is executed.

[0025] This invention utilizes existing voltage and current sampling signals from each branch of the high-voltage system to calculate and monitor the temporal characteristics of their phase differences and rates of change. This allows for accurate identification of branch-level electrical anomalies caused by poor contact, partial short circuits, etc., without the need for additional sensors. Based on the duration and fluctuation of the anomaly characteristics, a graded judgment is made, and a progressive control strategy is implemented, ranging from partial power outage and power-limiting operation to global power outage. This avoids blind, full-scale power outages of the entire vehicle's high-voltage system in most local fault scenarios, effectively maintaining the vehicle's core safety functions such as power, steering, and braking, and ensuring basic controllability and safety during driving. Furthermore, by analyzing the temporal causal relationships of multiple branch anomalies to infer the source of the anomaly, priority is given to controlling the root cause branch, reducing the false disconnection of normal branches due to fault chain reactions. This further improves the accuracy of system control and the availability of vehicle functions, creating favorable conditions for safe parking and repair diagnosis after a fault. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic flowchart of a safety power-off control method for new energy vehicles provided in an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the signal processing flow provided in an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the time-series causal linked list construction process provided in an embodiment of the present invention;

[0030] Figure 4 This invention provides a safety power-off control system for new energy vehicles. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be noted that all user information (including but not limited to user device information, user personal information, object information corresponding to device usage data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, device usage data, etc.) involved in all embodiments of this disclosure are information and data authorized by the user or fully authorized by all parties.

[0033] Please see Figure 1 , Figure 1 This is a schematic flowchart of a safety power-off control method for new energy vehicles provided in an embodiment of the present invention.

[0034] S1, acquire the real-time voltage and current signals of each electrical branch in the high-voltage system;

[0035] S2 processes the voltage and current signals from the same branch, and calculates the real-time phase difference between the voltage and current of that branch and its rate of change over time.

[0036] S3, based on the comparison results of the real-time phase difference and its rate of change with the preset threshold, identify whether there is an electrical abnormality in the corresponding branch, and classify the degree of abnormality. The classification includes at least a first-level abnormality that represents a local abnormality and a second-level abnormality that represents the spread of the abnormality.

[0037] S4 executes graded power-off control based on anomaly classification. When a first-level anomaly is identified, the corresponding abnormal branch is disconnected. When a second-level anomaly is identified, the output power of the vehicle's high-voltage system is limited in addition to disconnecting the corresponding abnormal branch.

[0038] According to an embodiment of the present invention, for step S1, the primary step in performing the safety power-off control is to synchronously acquire the real-time voltage and current signals of each electrical branch in the high-voltage system. Specifically, the high-voltage system of a new energy vehicle includes multiple electrical branches, such as an air conditioning compressor branch, an electric heater branch, an auxiliary motor branch, and a drive motor branch. Each branch is equipped with corresponding voltage sampling elements and current sampling elements. These sampling elements can reuse the sampling circuits in the vehicle's existing high-voltage system, without the need for additional hardware equipment, thereby reducing system modification costs.

[0039] To ensure the accuracy of subsequent phase difference calculations, the voltage and current signals of each branch can be sampled synchronously. For example, a unified clock synchronization signal is used to trigger the sampling action of all sampling elements, ensuring that the voltage and current signals of each branch complete data acquisition at the same time node, forming a discretized voltage signal sequence. With current signal sequence ,in Used to identify different electrical branches Indicates the sampling time. To capture the dynamic changes in the conduction state of the branch and avoid missing abnormal signals due to excessively low sampling frequency, the sampling frequency can be set to no less than 1kHz. This sampling frequency can fully cover the signal change characteristics under normal operation and abnormal conditions of the branch.

[0040] During the sampling process, the sampling element converts the acquired voltage and current signals into electrical signals and transmits them to the signal processing module in the vehicle control unit. It is understood that ensuring the operational stability of the sampling element during sampling is crucial to prevent signal distortion due to electromagnetic interference. Therefore, shielding layers or filtering elements can be incorporated into the sampling circuit to initially suppress the influence of external interference on the sampling signal; this will not be elaborated upon further in this invention.

[0041] In some embodiments, for step S2, after signal acquisition is completed, the voltage signal and current signal from the same branch are processed to calculate the real-time phase difference between the voltage and current of the branch, and the rate of change of the voltage and current over time is calculated based on the real-time phase difference.

[0042] Specifically, please refer to Figure 2 , Figure 2 This is a schematic diagram of the signal processing flow provided in an embodiment of the present invention. The signal processing process includes filtering, normalization, phase difference calculation, and rate of change calculation in sequence.

[0043] In S201, filtering is first performed. Due to electromagnetic interference and jitter of the sampling elements during vehicle operation, the acquired voltage and current signals will contain noise. If directly used for phase difference calculation, the calculation results will be biased, affecting the accuracy of anomaly identification. Therefore, for example, a low-pass filtering algorithm is used to filter the voltage and current signals of each branch. The low-pass filtering algorithm can effectively filter out high-frequency noise signals and retain the low-frequency effective components in the signal, making the signal curve smoother. For example, the cutoff frequency of the low-pass filter can be set to 100Hz. This frequency can filter out most of the electromagnetic interference noise while avoiding filtering out the effective variation characteristics in the signal.

[0044] After filtering, in step S202, amplitude normalization is performed on the voltage and current signals of each branch. Because the load characteristics of different branches differ, the amplitude ranges of their voltage and current signals vary. Directly calculating the phase difference may lead to inaccurate feature extraction due to amplitude differences. Through normalization, the amplitudes of the voltage and current signals of each branch are uniformly mapped to a preset range, resulting in a standardized voltage signal. With standardized current signals This makes the signals from different branches comparable.

[0045] After normalization, in step S203, the real-time phase difference between the voltage and current of the same branch is calculated. Preferably, a Fast Fourier Transform (FFT) or a phase-locked loop algorithm is used to perform the phase difference calculation. Both of these algorithms have the characteristics of high computational efficiency and high accuracy, which can meet the requirements of real-time control.

[0046] Taking the Fast Fourier Transform as an example, its specific calculation process includes: processing the standardized voltage signal... With current signal Perform Fourier transforms on each signal to obtain the corresponding frequency domain signal. and ,in Represents frequency; extracts the phase information of the fundamental component in the frequency domain signal, i.e., the voltage fundamental phase. Phase with the fundamental current ,in The fundamental frequency; obtained through the formula The calculation yields the branch at time [time]. Real-time phase difference .

[0047] After obtaining the real-time phase difference, in step S204, its rate of change over time is calculated based on the real-time phase difference. The rate of change of the phase difference reflects the dynamic trend of the phase difference and, compared to the static phase difference value, can more quickly capture abnormal states of the branch. Specifically, the rate of change is calculated based on the phase difference values ​​at two consecutive sampling times, using the following formula:

[0048]

[0049] in, For the first Phase difference at each sampling time, For the first Phase difference at each sampling time, The time interval between two sampling moments is determined by the sampling frequency; when the sampling frequency is 1 kHz, this time interval is 1 ms. Using the above formula, the phase difference change rate corresponding to each sampling moment can be obtained, forming a phase difference change rate sequence.

[0050] In some embodiments, for step S3, the real-time phase difference and its rate of change are compared with a preset threshold range. Based on the comparison result, the existence of electrical abnormalities in the corresponding branch is identified, and the degree of abnormality is classified. The classification includes at least a first-level abnormality representing a local abnormality and a second-level abnormality representing the spread of the abnormality. This process, through quantitative threshold judgment and continuous analysis over time, achieves the identification and classification of abnormal branch states, serving as the basis for subsequent graded power-off control.

[0051] Specifically, firstly, threshold parameters for anomaly detection can be preset. These threshold parameters are determined based on experimental data and simulation analysis, and can be adaptively adjusted according to the high-voltage system configuration and branch load characteristics of different vehicle models. Specifically, the preset thresholds include a first preset threshold, a second preset threshold, a first time threshold, and a second time threshold. The first preset threshold is used to determine whether the phase difference or its rate of change exceeds the normal range. The second preset threshold is used to determine whether the fluctuation amplitude of the phase difference reaches the anomaly propagation standard. The first and second time thresholds are used to further distinguish the anomaly level by combining the duration.

[0052] Preferably, the specific process of anomaly identification and classification is as follows:

[0053] For a single electrical branch, its real-time phase difference and its rate of change are continuously monitored. When the real-time phase difference or its rate of change exceeds a first preset threshold, but the duration is less than a first time threshold, the branch is determined to be a Level 1 anomaly. For example, in the first preset threshold, the phase difference threshold is set to 15°, the phase difference rate of change threshold is set to 50° / ms, and the first time threshold is set to 100ms. When the real-time phase difference of a branch suddenly rises to 20°, and the duration of this state is 80ms, which does not reach the first time threshold of 100ms, the branch can be determined to have experienced a Level 1 anomaly. Level 1 anomalies are usually local transient anomalies, such as transient fluctuations caused by poor contact of branch connectors, instantaneous changes in load, etc. These types of anomalies do not spread to the main circuit and have little impact on the overall operation of the vehicle's high-voltage system. Therefore, only local power outage control needs to be performed on this branch.

[0054] When the real-time phase difference continuously exceeds the first preset threshold, or the fluctuation amplitude of the phase difference exceeds the second preset threshold and the duration reaches the second time threshold, the branch is determined to be a Level 2 anomaly. Specifically, if the real-time phase difference of the branch exceeds the first preset threshold of 15° and the duration reaches the second time threshold of 500ms, or the fluctuation amplitude of the phase difference exceeds the second preset threshold of 20% in a short period of time and the fluctuation state lasts for 500ms, it is determined to be a Level 2 anomaly. Level 2 anomalies are persistent conduction anomalies, which have shown a spreading trend and may affect the power stability of the main circuit. For example, the risk of a continuous short circuit caused by abnormal conduction of the branch semiconductor device may not be completely eliminated if only partial power disconnection is performed. Therefore, it is necessary to perform power limiting control on the entire vehicle high-voltage system after disconnecting the abnormal branch.

[0055] According to a preferred embodiment of the present invention, the method further includes the determination of a third-level anomaly. Specifically, while monitoring the abnormal state of each branch, the fluctuation of the high-voltage system bus voltage is monitored simultaneously. When at least two branches are identified as having anomalies simultaneously, and the rate of decrease in bus voltage exceeds a third preset threshold, it is determined to be a third-level anomaly characterizing global risk.

[0056] For example, the third preset threshold is set to 10V / ms. For instance, if a phase difference is detected simultaneously in the air conditioning compressor branch and the electric heater branch, and the bus voltage drops by 12V within 1ms, exceeding the third preset threshold of 10V / ms, it can be determined as a Level 3 anomaly. A Level 3 anomaly indicates that the fault has spread to multiple branches, posing a serious threat to the overall safety of the high-voltage system. If all high-voltage circuits are not disconnected in time, it may cause serious safety accidents such as battery overheating and circuit fires; therefore, a global power outage must be performed immediately.

[0057] In the anomaly classification process, it is essential to ensure the rigor and timeliness of the judgment logic. Specifically, continuous sampling data is used for sliding window analysis, with each sliding window lasting 10ms. Statistical analysis of the signal characteristics within the window is performed to avoid misjudgments caused by anomalies in a single sampling data session. Furthermore, to accommodate the different load characteristics of various branches, the preset thresholds for each branch can be customized. For example, the normal phase difference range for a purely resistive load branch is ±5°, while the normal phase difference range for a branch containing inductive components is 20°-40°. The corresponding first preset threshold can be adjusted according to its own normal range to ensure the accuracy of anomaly judgment.

[0058] In some embodiments, for step S4, according to the determination result of the abnormality level, the corresponding graded power-off control is executed. Different levels of abnormality correspond to different control logics, so as to maintain the availability of the whole vehicle to the maximum extent while ensuring safety.

[0059] Specifically, when a Level 1 anomaly is detected, the system controls the disconnection of the relay in the corresponding anomalous branch. Specifically, after determining that a branch is experiencing a Level 1 anomaly, the vehicle control unit immediately sends a disconnection command to the corresponding high-voltage relay. This high-voltage relay is a branch-specific relay, independent of the main circuit relays, and is solely responsible for controlling the on / off state of the corresponding branch. After sending the disconnection command, the vehicle control unit continuously monitors the relay's status feedback signal. This feedback signal is provided by the relay's built-in status detection element and is used to confirm whether the relay has successfully disconnected. If a disconnection feedback signal is received within a preset time, such as 50ms, the disconnection action is considered complete. The system maintains normal power supply to the main high-voltage circuit, other branches are unaffected, and the vehicle's core safety functions, such as drive function and brake assist function, remain normal.

[0060] For example, when a first-level anomaly occurs in the auxiliary air conditioning circuit while the vehicle is traveling at high speed, the system only disconnects the relay of the auxiliary air conditioning circuit to shut down the air conditioning system. Meanwhile, core circuits such as the drive motor circuit and the high-voltage circuit of the braking system continue to operate normally. The driver can still control the vehicle to safely drive or park, avoiding the risk of vehicle malfunction caused by a traditional global power outage and significantly improving driving safety. Simultaneously, the system records relevant information about this abnormal event in the vehicle control unit log, including the abnormal circuit identifier, the time of the anomaly, and phase difference change characteristics, providing a basis for subsequent fault diagnosis and repair.

[0061] According to an embodiment of the present invention, when a second-level abnormality is identified, the vehicle's high-voltage system is controlled to enter an operating mode that limits output power, based on disconnecting the corresponding abnormal branch relay.

[0062] Specifically, this includes: First, following the same control logic as the first-level anomaly, a disconnection command is sent to the high-voltage relay of the abnormal branch, and the disconnection is confirmed through a status feedback signal; then, the vehicle control unit sends a power limiting control command to the motor controller, adjusting the current limiting and voltage limiting parameters of the motor controller to reduce the upper limit of the vehicle's output power.

[0063] For example, under normal conditions, the maximum output power of the vehicle is 150kW. After entering the power-limited operation mode, the upper limit of the output power is limited to 50kW. This power level can meet the basic safety needs of the vehicle, such as low-speed driving, steering, and braking, while preventing excessive power in the main circuit from causing further fault propagation. The power limit parameter can be dynamically adjusted according to the current driving state of the vehicle. For example, when the vehicle is driving at high speed, the upper limit of the power limit can be appropriately increased to 70kW to ensure that the vehicle has sufficient power to complete a safe lane change or pull over to the side of the road; while when the vehicle is driving at low speed, the upper limit of the power limit can be set to 30kW to further reduce the risk of fault propagation.

[0064] During power-limited operation, the system continuously monitors voltage fluctuations, current changes, and the status of other branches in the main circuit. If no new anomalies are detected and the system continues to operate for a period of time, such as 30 seconds, the upper limit of output power can be gradually increased until the normal power level is restored. If the anomaly is detected to be worsening, the control is immediately upgraded, and a global power outage is executed. For example, during maintenance and testing, if a poor connector contact causes a second-level anomaly in a branch, the system disconnects the abnormal branch and enters power-limited operation. Maintenance personnel can perform troubleshooting while the vehicle remains basically running, without interrupting the troubleshooting work due to a power outage of the entire vehicle, thus improving maintenance convenience.

[0065] According to an embodiment of the present invention, when a Level 3 anomaly is detected, a global power-off is executed, controlling the disconnection of the main relay and all branch relays of the high-voltage system. Specifically, after the vehicle control unit detects a Level 3 anomaly, it immediately initiates an emergency power-off procedure, sending disconnect commands according to a preset power-off sequence: first, disconnecting the dedicated relays of all branches to cut off the high-voltage power supply to each branch; then, disconnecting the main relay to cut off the connection between the power battery and the main circuit, ensuring complete isolation of the high-voltage system.

[0066] To ensure the safety of the power-off process, the power-off sequence must be set to avoid arcing caused by sudden changes in circuit current. For example, the disconnection interval for each branch relay is set to 10ms. The main relay is disconnected 100ms after all branch relays have disconnected. This time interval allows the circuit current to gradually decay, reducing the risk of arcing. After sending the disconnection command, the vehicle control unit also needs to monitor the status feedback signals of each relay. Once it confirms that all relays have successfully disconnected, it sends a notification to the driver that the high-voltage system has been de-energized, for example, through a warning light on the instrument panel or an audible alarm.

[0067] After a complete power outage, the vehicle's high-voltage system completely stops working, and the drive motor, high-voltage auxiliary equipment, and other components lose their power source. At this point, the driver can rely on the vehicle's mechanical braking system to bring it to a stop. Understandably, a complete power outage is only implemented when the fault has seriously threatened safety; its purpose is to minimize the risk of an accident and protect the safety of the driver and the vehicle.

[0068] According to a preferred embodiment of the present invention, when at least two branches are identified as having anomalies, the starting timing of the phase difference change in each branch and its synchronicity with the bus voltage fluctuation are analyzed. Based on the timing and synchronicity, the abnormal source branch among multiple abnormal branches is inferred. When performing graded power-off control, power-off control is preferentially performed on the inferred abnormal source branch. This process can effectively avoid false power-offs caused by cascading fluctuations in multiple branches and improve the accuracy of control.

[0069] Specifically, the abnormal source branch inference process includes: First, recording the start time of the phase difference change of each abnormal branch, that is, the moment when the phase difference first exceeds the first preset threshold, and marking it as... ,in Record the number of abnormal branches; simultaneously, record the start time when the bus voltage begins to fluctuate. Subsequently, a temporal causal linked list of phase difference change events for each abnormal branch is constructed. This linked list uses the chronological order as the axis to associate and sort the change events of each abnormal branch with the bus voltage fluctuation events.

[0070] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the construction process of a time-series causal linked list provided in an embodiment of the present invention. For the construction of the time-series causal linked list, the time-series causal linked list of the present invention is a data structure that uses the chronological order as the axis to associate and sort the phase difference change events of each abnormal branch with the bus voltage fluctuation events. When constructing this linked list, in S301, firstly, all recorded time nodes... and Sort the records in ascending order of numerical value to obtain a unified time series. For example, if the recorded time points are respectively... , , , The sorted time series is then... .

[0071] After completing the time sorting, in step S302, the event type and branch identification information corresponding to each time node are associated with the time series to form a complete time-series causal linked list. Each node in the linked list contains three core pieces of information: the event occurrence time, the event type (e.g., branch phase difference abnormal event or bus voltage fluctuation event), and the identifier of the abnormal branch or bus corresponding to the event's associated object. For example, the linked list nodes corresponding to the sorted time series are as follows: Node 1, including time 100ms, event type branch phase difference abnormal, associated object branch 1; Node 2, including time 105ms, event type bus voltage fluctuation, associated object bus; Node 3, including time 120ms, event type branch phase difference abnormal, associated object branch 2; Node 4, including time 130ms, event type branch phase difference abnormal, associated object branch 3.

[0072] Preferably, during the construction process, feature parameter information corresponding to each event can be supplemented. For example, for a branch phase difference abnormal event, the specific value when the branch phase difference first exceeds the first preset threshold, the initial value of the phase difference change rate, and the phase difference fluctuation trend within 10ms before the abnormality occurs can be supplemented and recorded; for a bus voltage fluctuation event, the offset of the bus voltage from the normal range for the first time, the initial value of the voltage change rate, and the stable state of the bus voltage within 10ms before the fluctuation occurs can be supplemented and recorded.

[0073] According to a preferred embodiment of the present invention, the specific process of inferring the abnormal source branch may include: analyzing the constructed time-series causal chain list, and if the phase difference change start time of the branch is earlier than the change start time of all other abnormal branches, and its phase difference change characteristics are most synchronous with the bus voltage fluctuation start time, then the branch is presumed to be the abnormal source branch.

[0074] Synchronization is determined by calculating the correlation coefficient between the phase difference change curve and the bus voltage fluctuation curve. The correlation coefficient ranges from -1 to 1; the closer the absolute value of the correlation coefficient is to 1, the stronger the synchronization between the two. For example, the start time of the phase difference change in branch A is... The starting time of the phase difference change in branch B is The bus voltage fluctuation start time is The correlation coefficient between the phase difference change curve of branch A and the bus voltage fluctuation curve was calculated to be 0.92, and the correlation coefficient between branch B and branch A was 0.35. Therefore, branch A was determined to be an abnormal source branch.

[0075] According to an embodiment of the present invention, after the abnormal source branch is deduced, when performing graded power-off control, the abnormal source branch is given priority for power-off operation. If the abnormality level is Level 1 or Level 2, after disconnecting the abnormal source branch, the status of other abnormal branches is continuously monitored. If the abnormal signals of other branches disappear with the disconnection of the abnormal source branch, there is no need to power off the other branches; only the relevant abnormal information needs to be recorded. If the abnormal signals of other branches still exist, the corresponding control is executed sequentially according to the abnormality level. For example, if branch A is the abnormal source branch and a Level 2 abnormality occurs, and branch B is a related abnormal branch, after disconnecting branch A, the phase difference of branch B returns to the normal range. In this case, the system only needs to disconnect branch A and perform power limiting control, without disconnecting branch B, thereby avoiding erroneous power-off of normal branches and maintaining the partial functional availability of the vehicle.

[0076] Preferably, to further improve the accuracy of anomaly source localization and reduce inference errors caused by signal interference or special fault modes under complex operating conditions, the method also includes verifying the inference results obtained based on the time series and synchronization analysis using a pre-trained Bayesian network causal model.

[0077] Specifically, during the model building and training phase, historical fault data from high-voltage systems of new energy vehicles are collected. This data covers various branch circuit anomaly cases under different vehicle models and operating conditions, including multiple scenarios such as chain anomalies caused by a single branch circuit fault and simultaneous occurrence of independent faults in multiple branches. For each fault data case, key feature parameters are extracted, including the range, rate of change, and duration of abnormal phase difference changes in each branch, the amplitude of bus voltage fluctuations, and the time difference between the fluctuation start time and the branch circuit anomaly start time. At the same time, the actual anomaly source branch and risk propagation path in each fault are clearly recorded.

[0078] Based on the aforementioned historical data, the Bayesian network model is trained. Through continuous iteration and optimization of the model parameters, the model learns the correspondence between different combinations of abnormal features and abnormal sources and risk paths, forming a reasoning model with empirical judgment capabilities. For example, when the model learns historical data on the fault mode "after a sudden change in phase difference in the motor branch exceeding 15° and lasting for 50ms, the bus voltage drop rate exceeds 10V / ms, followed by a phase difference anomaly in the electric heater branch," a causal relationship of "motor branch anomaly, bus voltage fluctuation, and associated anomaly in the electric heater branch" will be established.

[0079] Understandably, since the model training process is completed offline, online runtime only requires calling the pre-trained model, inputting the currently monitored abnormal characteristic parameters of each branch and bus voltage fluctuation data into the model, and the model can quickly infer based on the learned causal relationships, outputting the determination result of the abnormal source branch. This inference process does not require complex real-time calculations, only data matching and probability calculations, and the response time can be controlled within the system's allowable range, ensuring that it will not affect the timeliness of the overall power outage control.

[0080] Validation using a Bayesian network causal inference model allows for secondary confirmation of preliminary inferences derived from time-series and synchronicity analysis. Specifically, when the anomaly source identification results from the two methods are consistent, the reliability of the identification results can be further enhanced. If the results from the two methods differ, the rationality of the two inference results can be comprehensively analyzed by combining the specific characteristics of the current fault scenario, such as referring to the probability distribution of fault modes in similar scenarios in historical data, to ultimately determine the most likely anomaly source branch. Thus, dual identification can effectively reduce the risk of misjudgment that may exist with a single analysis method, and can significantly improve the accuracy of anomaly source localization, especially in complex fault scenarios.

[0081] According to a preferred embodiment of the present invention, after the power outage control is executed, monitoring and recovery are also included, which are used to ensure the effectiveness of the power outage action and to avoid the system being in an abnormal state for a long time due to transient abnormalities.

[0082] Specifically, the system first monitors the status feedback signals of the corresponding relays to confirm the completion of the disconnection action. Whether it's a partial or global power outage, the vehicle control unit must continuously receive relay status feedback signals after sending the disconnection command until it confirms that all target relays have successfully disconnected. If no relay disconnection feedback signal is received within a preset timeout period, such as 100ms, it indicates that the relay may be stuck or faulty. In this case, the system will issue a fault alarm signal and activate the backup power-off scheme, such as forcibly disconnecting the relay by cutting off the backup control circuit, or directly escalating to a higher level of power outage to ensure the safety of the high-voltage system.

[0083] After the abnormal signal disappears, a preset delay reset time is waited for. If no new abnormality is detected during this period, the control resumes normal operation. For example, the delay reset time is set to 500ms. When the phase difference and rate of change of the abnormal branch return to the normal range and the duration reaches 500ms, and no new abnormal signal appears during this period, the abnormality is determined to be eliminated. The vehicle control unit sends a closing command to the corresponding relay to gradually restore power to the branch. For systems operating with limited power, a gradual power increase is used when resuming normal operation to avoid sudden power surges impacting the system. For example, the output power is increased by 10% every 100ms until it returns to the normal power level.

[0084] During the recovery process, the signal status of each branch circuit and the operating parameters of the main circuit are continuously monitored. If an abnormal signal is detected again during the recovery process, the recovery operation is immediately stopped, and the corresponding power-off control is re-executed. At the same time, all relevant information about the recovery operation, including the recovery time and parameter changes during the recovery process, is recorded in the vehicle control unit log and stored in association with previous abnormal event records for easy fault tracing and optimization.

[0085] Furthermore, preferably, all abnormal events and handling processes are recorded in detail in the vehicle control unit log. The records include the time of the abnormality, the abnormal branch identifier, phase difference and rate of change data, the abnormality level determination result, the executed control, relay operation status, recovery time, and other information. This recorded data can be read through the diagnostic interface, providing maintenance personnel with complete fault diagnosis data, helping them quickly locate the fault, analyze the cause of the fault, improve maintenance efficiency, and reduce maintenance costs.

[0086] In summary, this invention achieves safe, accurate, and efficient power-off control of the high-voltage system in new energy vehicles by synchronously acquiring signals, accurately calculating phase differences and rates of change, hierarchically identifying anomalies, executing targeted power-off control, inferring the anomaly source branch, and implementing a robust monitoring and recovery mechanism. This method eliminates the need for additional sensors, fully utilizing existing hardware resources and reducing implementation costs. Hierarchical control avoids the vehicle's incapacity caused by blind global power-offs, improving driving safety and vehicle availability. The anomaly source branch inference mechanism reduces the occurrence of erroneous power-offs, further optimizing the control effect.

[0087] According to another aspect of the invention, please refer to Figure 4 , Figure 4 This invention provides a safety power-off control system for new energy vehicles. The system uses the safety power-off control method for new energy vehicles described in the foregoing embodiments, specifically including:

[0088] The signal acquisition module is used to acquire the real-time voltage and current signals of each electrical branch in the high-voltage system.

[0089] The feature calculation module processes voltage and current signals from the same branch to calculate the real-time phase difference between the voltage and current of that branch and its rate of change over time.

[0090] The anomaly identification module identifies whether there is an electrical anomaly in the corresponding branch based on the comparison result of the real-time phase difference and its rate of change with a preset threshold, and classifies the degree of anomaly. The classification includes at least a first-level anomaly that represents a local anomaly and a second-level anomaly that represents the spread of the anomaly.

[0091] The power-off control module performs graded power-off control based on the anomaly classification. When a first-level anomaly is identified, the corresponding abnormal branch is disconnected. When a second-level anomaly is identified, the output power of the vehicle's high-voltage system is limited in addition to disconnecting the corresponding abnormal branch.

[0092] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A safety power-off control method for a new energy vehicle, characterized in that, The method comprises the following steps: obtaining real-time voltage signals and real-time current signals of each electrical branch in a high-voltage system; processing the voltage signals and the current signals from the same branch to calculate the real-time phase difference between the voltage and the current of the branch and the rate of change of the real-time phase difference over time; based on the comparison result of the real-time phase difference and the rate of change thereof with a preset threshold, identifying whether an electrical abnormality exists in the corresponding branch and grading the abnormality, wherein the grading at least includes a first-level abnormality representing a local abnormality and a second-level abnormality representing abnormality diffusion; based on the abnormality grading, performing graded power-off control, wherein when the first-level abnormality is identified, the corresponding abnormal branch is controlled to be disconnected, and when the second-level abnormality is identified, the corresponding abnormal branch is disconnected and the output power of the entire high-voltage system is limited; the grading of the abnormality includes: when the real-time phase difference or the rate of change thereof exceeds a first preset threshold but the duration is lower than a first time threshold, the first-level abnormality is determined, and when the real-time phase difference continuously exceeds the first preset threshold or the fluctuation amplitude exceeds a second preset threshold and the duration reaches a second time threshold, the second-level abnormality is determined.

2. The safety power-off control method for a new energy vehicle according to claim 1, characterized in that, The method further comprises: based on bus voltage fluctuation, when it is identified that at least two branches simultaneously have an abnormality and the falling rate of the bus voltage exceeds a third preset threshold, a third-level abnormality representing a global risk is determined; and in response to the determination of the third-level abnormality, performing global power-off control to control the disconnection of the high-voltage system and all branches.

3. The method according to claim 2, wherein, The method further comprises: when at least two branches are identified as having an abnormality, based on the starting timing of the phase difference change of each branch and the synchronism of the change thereof with the bus voltage fluctuation, inferring an abnormal source branch among the multiple abnormal branches, and when the graded power-off control is performed, preferentially performing power-off control on the inferred abnormal source branch.

4. The safety power-off control method for a new energy vehicle according to claim 3, characterized in that, The inference of the abnormal source branch specifically comprises: based on a timing causal chain table, if the starting time of the phase difference change of a branch is earlier than that of other branches and the change characteristic thereof is most synchronous with the starting time of the bus voltage fluctuation, the branch is determined as the abnormal source branch, wherein the timing causal chain table is constructed based on each abnormal branch phase difference change event.

5. The method according to claim 1, wherein, After the power-off control is performed, the method further comprises: confirming the completion of the disconnection action based on the state feedback signal of the corresponding relay, waiting for a preset delay reset time after the abnormality signal disappears, and if no new abnormality is detected during this period, controlling the resumption of the normal operation state.

6. A safety power-off control system for a new energy vehicle, using the safety power-off control method for a new energy vehicle according to any one of claims 1 to 5, characterized in that, The method comprises: a signal acquisition module configured to obtain real-time voltage signals and real-time current signals of each electrical branch in a high-voltage system; a feature calculation module configured to process the voltage signals and the current signals from the same branch to calculate the real-time phase difference between the voltage and the current of the branch and the rate of change of the real-time phase difference over time; an abnormality identification module configured to identify whether an electrical abnormality exists in the corresponding branch based on the comparison result of the real-time phase difference and the rate of change thereof with a preset threshold, and grade the abnormality, wherein the grading at least includes a first-level abnormality representing a local abnormality and a second-level abnormality representing abnormality diffusion; The power-off control module performs hierarchical power-off control based on the abnormality level, wherein when a first-level abnormality is identified, the corresponding abnormal branch is controlled to be disconnected, and when a second-level abnormality is identified, the corresponding abnormal branch is disconnected and the output power of the whole vehicle high-voltage system is limited.

7. The safety power-off control system for a new energy vehicle according to claim 6, characterized in that, The step of classifying the abnormality level comprises: determining a first-level abnormality when the real-time phase difference or its change rate exceeds a first preset threshold but the duration is lower than a first time threshold, and determining a second-level abnormality when the real-time phase difference continuously exceeds the first preset threshold or the fluctuation amplitude exceeds a second preset threshold, and the duration reaches a second time threshold.

8. The safety power-off control system for a new energy vehicle according to claim 7, characterized in that, The inference module is further configured to infer an abnormal source branch among the multiple abnormal branches based on the starting time sequence of the phase difference change of each branch and the synchronism between the change and the bus voltage fluctuation when at least two branches are identified as abnormal, and to preferentially perform power-off control on the inferred abnormal source branch when performing the hierarchical power-off control.

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