A test method for electric vehicle fire safety linkage of whole vehicle thermal runaway

Abstract

The application relates to the technical field of vehicle safety testing, and discloses a kind of electric vehicle whole car thermal runaway fire safety linkage test method, the electric vehicle whole car thermal runaway fire safety linkage test method includes: establishing fire safety linkage time sequence topology matrix;According to the thermal runaway decay data of battery cell, generate basic injection control sequence;Obtain the action intensity vector of whole car key actuator state extraction, and output the inhibition efficiency vector through dynamic mapping model;Calculate actual injection control sequence and apply boundary constraint, control external device output injection signal;Get node actual action time and network timestamp, calculate full-link actual delay and compare with matrix, combined with timestamp, carry out fault tracing determination.The application makes the signal injection of electric vehicle whole car thermal runaway fire safety linkage test fit real decay working condition, standardizes time limit and jurisdiction benchmark, realizes the accurate tracing of linkage lag fault.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety testing technology, specifically to a method for testing the thermal runaway fire prevention and safety linkage of an electric vehicle. Background Technology

[0002] Electric vehicles rely on batteries as their core power source, and batteries are prone to thermal runaway under extreme conditions. Therefore, vehicles typically incorporate fire safety linkage mechanisms that regulate various key actuators to slow heat spread. Conducting fire safety linkage tests is a necessary engineering method to verify the vehicle's safety protection capabilities and the effectiveness of its thermal runaway prevention.

[0003] Traditional linkage testing typically inputs fixed physical quantity simulation signals into the vehicle's test port to create a thermal runaway trigger environment. Then, external instruments record the total time from the trigger alarm to the actuator completing the response action, and this total time is directly used as the evaluation criterion for judging the timeliness of the vehicle's safety linkage response.

[0004] Existing testing methods reveal a disconnect between verification and actual application. Fixed signal inputs fail to consider the actual suppression effectiveness of actuators in thermal runaway environments, causing test inputs to deviate from real-world physical degradation conditions. A single total time evaluation criterion masks the inherent response time of the actuators themselves, leading to timeliness assessments that are detached from the vehicle's original design benchmarks. Furthermore, the overall timing method cannot separate the time ratio between command network communication delays and physical action delays. Once a response lag occurs during testing, it becomes impossible to determine whether the anomaly originates from the electronic control system or the physical execution layer, resulting in test results lacking a definitive basis for fault localization. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a vehicle thermal runaway fire safety linkage test method for electric vehicles. This method aims to solve the technical problems in existing technologies, such as verification distortion caused by fixed signal input, deviation of a single timing standard from the original design benchmark, and inability to accurately trace and respond to delayed faults due to the lack of decoupling of delay time.

[0006] The technical solution adopted in this invention is as follows: To achieve the above objectives, this invention is implemented through the following technical solution: A method for testing the thermal runaway fire safety linkage of an electric vehicle, comprising the following steps: Establish a fire safety linkage time-series topology matrix; Generate a basic injection control sequence based on the thermal runaway decay data of the target battery pack cells; Acquire real-time operating status data of the actuators of the tested electric vehicle, extract the actuator action intensity vector, input a propagation suppression dynamic mapping model containing a preset weight influence matrix to output the overall propagation suppression effectiveness vector; The actual injection control sequence is calculated based on the basic injection control sequence and the overall propagation suppression effectiveness vector. Asymmetric unidirectional boundary constraints are applied to the actual injection control sequence to control the external test equipment to output injection signals to the sensors of the electric vehicle under test. The actual action time of the execution node in the electric vehicle under test to complete the real response action is obtained, as well as the network timestamp of the controller local area network bus of the electric vehicle under test to issue the corresponding control command. The actual delay of the whole link is calculated and compared with the fire safety linkage timing topology matrix for verification. The fault source analysis is determined by combining the network timestamp.

[0007] Preferably, the step of establishing the fire safety linkage time-series topology matrix includes: The external testing equipment is controlled to output a simulated physical quantity signal. When the simulated physical quantity signal reaches the alarm threshold value calibrated by the battery management unit inside the electric vehicle under test, the thermal runaway trigger threshold time is recorded. Read the controller local area network communication matrix file and the execution component specification of the electric vehicle under test, and extract the safety response design parameters corresponding to each execution node in the electric vehicle under test; The fire safety linkage timing topology matrix is ​​generated based on the safety response design parameters. The fire safety linkage timing topology matrix defines the qualified theoretical delay time interval for the execution node to complete the actual response action.

[0008] Preferably, the qualified theoretical delay time interval is defined by the minimum theoretical delay time and the maximum theoretical delay time; The minimum theoretical delay time is set as the sum of the network instruction transmission time and the inherent response time required for the execution node to complete an effective action. The maximum theoretical delay time is set as the limit of the tolerance time allowed by the vehicle thermal runaway propagation prevention and control safety standard.

[0009] Preferably, the step of generating the basic injection control sequence based on the thermal runaway decay data of the target battery pack cells includes: The thermal runaway decay data is time-axis aligned, and the temperature field data features, gas field data features, and voltage field data features that change with time are extracted according to the acquisition channels. The temperature field data features are converted into a heating power control reference sequence, the gas field data features are converted into a characteristic gas flow control reference sequence, and the voltage field data features are converted into a cell voltage drop rate control reference sequence. The heating power control reference sequence, the characteristic gas flow control reference sequence, and the cell voltage drop rate control reference sequence are packaged and spliced ​​in parallel according to a unified test timestamp to generate the basic injection control sequence.

[0010] Preferably, the steps of acquiring real-time operating status data of key actuators of the tested electric vehicle, extracting actuator action intensity vectors, and inputting a propagation suppression dynamic mapping model containing a preset weighted influence matrix to output an overall propagation suppression effectiveness vector include: The real-time operating status data is filtered using a moving average filtering algorithm to eliminate electrical noise. The filtered real-time operating status data value is divided by the rated full-load parameter value corresponding to the key actuator to calculate the percentage value. The percentage values ​​are arranged sequentially according to a preset node order to form the actuator action intensity vector; The overall propagation suppression effectiveness vector is obtained by multiplying the preset weight influence matrix with the actuator action intensity vector.

[0011] Preferably, the step of calculating the actual injection control sequence based on the basic injection control sequence and the overall propagation suppression effectiveness vector includes: The overall propagation suppression effectiveness vector is transformed by equivalent normalization, and the transformed overall propagation suppression effectiveness vector is integrated and accumulated along the test time axis at each sampling time to calculate the total physical suppression deduction vector from the initial test time to the sampling time. The actual injection control sequence is obtained by subtracting the total physical suppression deduction vector from the basic injection control sequence at the corresponding sampling time to complete the negative feedback deduction of the control command.

[0012] Preferably, the step of applying asymmetric unidirectional boundary constraints to the actual injection control sequence and controlling the external test equipment to output injection signals to the sensors of the electric vehicle under test includes: Read the multidimensional array elements in the actual injection control sequence one by one. When the element value of the actual injection control sequence is greater than zero, retain the positive calculation value. When the value of an element in the actual injected control sequence is less than or equal to zero, the corresponding element value is truncated and assigned the value of zero to obtain the final output control sequence. The final output control sequence is decoupled into actual heating power command, actual characteristic gas flow rate command and actual cell voltage drop rate command according to the type of physical quantity. Based on the actual heating power command, the actual characteristic gas flow command, and the actual cell voltage drop rate command, the external testing equipment is driven to output the injection signal to the sensors of the electric vehicle under test.

[0013] Preferably, the step of obtaining the actual action time of the execution node within the tested electric vehicle completing the real response action, and the network timestamp of the corresponding control command issued by the controller local area network bus of the tested electric vehicle, calculating the actual end-to-end delay, and comparing and verifying it with the fire safety linkage timing topology matrix includes: When the real-time running status data of a certain execution node indicates that the execution node has completed a real response action, the corresponding absolute time is recorded as the actual action time; Monitor the data stream of the controller local area network bus of the electric vehicle under test, capture specific message frames that control the actions of the execution node, and record the transmission time of the specific message frames on the controller local area network bus as the network timestamp; Subtracting the thermal runaway trigger threshold time from the actual action time yields the actual end-to-end latency of the corresponding execution node. Extract the qualified theoretical delay time interval of the corresponding execution node from the fire safety linkage timing topology matrix, and determine whether the actual delay of the entire link falls within the qualified theoretical delay time interval.

[0014] Preferably, the step of combining the network timestamp for fault tracing and analysis includes: When it is determined that the actual delay of the entire link does not fall within the qualified theoretical delay time range, the fault source analysis is performed in conjunction with the network timestamp. The actual end-to-end latency is decoupled and calculated as network communication latency and physical execution latency, wherein the network communication latency is the difference between the network timestamp and the thermal runaway trigger threshold time, and the physical execution latency is the difference between the actual action time and the network timestamp. The network communication delay and the physical execution delay are independently and jointly verified, and the corresponding fault tracing and analysis results are output.

[0015] Preferably, the step of independently verifying and combining the network communication delay and the physical execution delay, and outputting the corresponding fault tracing and analysis result includes: If the network communication delay is greater than the preset network communication tolerance threshold, and the physical execution delay is less than or equal to the preset physical execution tolerance threshold, the fault tracing and analysis result is output as a fault in the electronic control communication link. If the physical execution delay is greater than the physical execution tolerance threshold, and the network communication delay is less than or equal to the network communication tolerance threshold, the fault tracing and analysis result is output as a physical execution layer lag fault. If both the network communication delay and the physical execution delay are greater than their respective tolerance thresholds, the fault tracing and analysis result is output as a full-link cascade timeout fault. If the sum of the network communication tolerance threshold and the physical execution tolerance threshold is greater than the maximum theoretical delay time, and if both the network communication delay and the physical execution delay are less than or equal to their respective tolerance thresholds, and the actual delay of the entire link exceeds the maximum theoretical delay time, the fault tracing and analysis judgment result is output as an overall timing tolerance superposition fault.

[0016] This invention provides a method for testing the thermal runaway fire safety linkage of an electric vehicle. It has the following beneficial effects: 1. This invention extracts the action intensity by acquiring the actuator state and combines it with the suppression effectiveness output by a dynamic mapping model to perform negative feedback deduction and boundary constraints on the basic sequence. This dynamic calculation method of the injected sequence changes the limitations of the previous fixed signal input, solves the problem of environmental distortion in the verification of electric vehicles when dealing with thermal runaway, makes the fire safety linkage process closely resemble the real decay conditions, and improves the reliability of the testing method.

[0017] 2. This invention extracts safety response parameters from communication files and component specifications to establish a fire safety linkage timing topology matrix that defines a qualified theoretical delay time interval. This matrix establishes an objective boundary for evaluating the timeliness of actions when an electric vehicle experiences thermal runaway, overcomes the shortcomings of rigid delay time standards, and ensures that the timeliness judgment of fire safety linkage is based on the original vehicle design, thus standardizing the evaluation benchmark for relevant test methods.

[0018] 3. This invention decouples the actual end-to-end latency into network communication latency and physical execution latency by comparing the absolute time of the execution node's action with the instruction network timestamp and performing cross-validation. This data decoupling method solves the problem of difficulty in identifying lagging links in electric vehicle thermal runaway scenarios, accurately identifying whether the abnormality is due to electronic control communication or physical component execution, refining the fault tracing mechanism for fire safety linkage, and improving the overall testing method. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0020] Figure 1 This is a diagram of the electric vehicle thermal runaway fire safety linkage test system architecture of the present invention. Figure 2This is a flowchart of the electric vehicle thermal runaway fire safety linkage test method of the present invention; Figure 3 This is a schematic diagram of the fire safety linkage timing topology matrix established in this invention; Figure 4 This is a schematic diagram of the basic injection control sequence for the generation of this invention; Figure 5 This is a schematic diagram of the overall propagation suppression effectiveness vector in real time for the output of this invention; Figure 6 This is a schematic diagram of the actual injection control sequence after dynamic correction according to the present invention; Figure 7 This is a schematic diagram illustrating the fault source analysis and determination method of the present invention.

[0021] Among them, 10 is the matrix building module; 20 is the sequence generation module; 30 is the acquisition and mapping module; 40 is the constraint injection module; and 50 is the source tracing and parsing module. Detailed Implementation

[0022] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0023] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0024] Example 1: Reference Figure 1 This invention provides a vehicle thermal runaway fire safety linkage test system. The vehicle thermal runaway fire safety linkage test system includes a matrix establishment module 10, a sequence generation module 20, a data acquisition and mapping module 30, a constraint injection module 40, and a source tracing and analysis module 50.

[0025] The electric vehicle thermal runaway fire safety linkage test system is deployed in the test host computer, which is equipped with a processor and communication board. The test host computer is connected to external test equipment and data acquisition equipment through a communication bus, and the test host computer is connected to the controller local area network bus of the electric vehicle under test through a message reading interface.

[0026] The matrix establishment module 10 runs in the test host computer and is used to establish the fire safety linkage timing topology matrix. The fire safety linkage timing topology matrix defines the qualified theoretical delay time interval for each execution node in the electric vehicle under test to complete its actual response action from the moment the output signal of the external test equipment reaches the thermal runaway trigger threshold.

[0027] The sequence generation module 20 is used to generate a basic injection control sequence based on the thermal runaway decay data of the target battery pack cells. The basic injection control sequence includes a heating power control reference sequence, a characteristic gas flow control reference sequence, and a cell voltage drop rate control reference sequence.

[0028] The acquisition and mapping module 30 is used to acquire real-time operating status data of key actuators of the electric vehicle under test through data acquisition equipment and extract actuator action intensity vectors. The acquisition and mapping module 30 internally constructs a propagation suppression dynamic mapping model containing a preset weighted influence matrix, and inputs the actuator action intensity vector into the propagation suppression dynamic mapping model to output a real-time overall propagation suppression effectiveness vector.

[0029] The constraint injection module 40 is used to calculate the dynamically corrected actual injection control sequence based on the basic injection control sequence and the overall propagation suppression effectiveness vector. After applying asymmetric unidirectional boundary constraints to the actual injection control sequence, the constraint injection module 40 controls the external test equipment to output injection signals to the sensors of the electric vehicle under test.

[0030] The source tracing and analysis module 50 is used to obtain the actual action time of each execution node completing the real response action and the network timestamp of the corresponding control command issued by the local area network bus of the vehicle controller of the tested electric vehicle. The source tracing and analysis module 50 calculates the actual end-to-end delay, compares and verifies the actual end-to-end delay with the fire safety linkage timing topology matrix, and combines the network timestamp to determine the fault source tracing and analysis.

[0031] Reference Figure 2 This invention provides a method for testing the thermal runaway fire safety linkage of an electric vehicle, applicable to an electric vehicle thermal runaway fire safety linkage testing system, comprising the following steps: S1, Matrix Establishment Module 10 establishes a fire safety linkage timing topology matrix in the test host computer.

[0032] S2, the sequence generation module 20 generates a basic injection control sequence based on the thermal runaway decay data of the target battery pack cells.

[0033] S3. During the test run, the acquisition and mapping module 30 acquires the real-time operating status data of the key actuators of the electric vehicle under test through the data acquisition device, extracts the actuator action intensity vector, and pre-builds a propagation suppression dynamic mapping model containing a preset weight influence matrix in the test host computer. The actuator action intensity vector is input into the propagation suppression dynamic mapping model to output the real-time overall propagation suppression effectiveness vector.

[0034] S4, the constraint injection module 40 calculates the dynamically corrected actual injection control sequence based on the basic injection control sequence and the overall propagation suppression effectiveness vector. After applying asymmetric unidirectional boundary constraints to the actual injection control sequence, it controls the external test equipment to output injection signals to the sensors of the electric vehicle under test.

[0035] S5, the source tracing and analysis module 50 obtains the actual action time of each execution node completing the real response action and the network timestamp of the corresponding control command issued by the local area network bus of the vehicle controller of the tested electric vehicle, calculates the actual delay of the whole link, compares and verifies the actual delay of the whole link with the fire safety linkage timing topology matrix, and combines the network timestamp to perform fault source tracing and analysis.

[0036] To further clarify the implementation of each technical aspect of the present invention, the following will provide a detailed description of the functional modules and methods involved above.

[0037] Reference Figure 3 In this embodiment, step S1 is implemented through the following sub-steps: S11, perform a recoverable equivalent topology reconstruction to construct a signal injection channel.

[0038] In this embodiment, a test-specific battery pack cover with a pre-installed airtight connector is used to replace the original battery pack cover of the electric vehicle under test. This test-specific battery pack cover can be separately manufactured based on the original battery pack cover's mounting interface, and a waterproof aviation connector is added by drilling holes on its surface.

[0039] Using physical wiring, a non-destructive bypass harness is used to bring out the temperature sensor probe, characteristic gas sensor probe, and cell voltage sampling point inside the electric vehicle under test, and connect them to external testing equipment. The non-destructive bypass harness can use an extension adapter cable with male and female plug-in terminals. External testing equipment may include, but is not limited to, a comprehensive signal generation platform with miniature heating sleeves, micro gas flow valves, and programmable voltage sources.

[0040] When connecting external test equipment via a non-destructive bypass harness, the original electrical topology of the electric vehicle under test is maintained, ensuring normal communication between the battery management unit and the vehicle controller within the vehicle. Maintaining normal communication prevents the electric vehicle from entering a low-level fault protection state due to sensor circuit disconnection, thus providing a physical basis for receiving externally injected signals and making realistic responses.

[0041] The disassembly of conventional physical components of the battery pack cover, the crimping of standard terminals of the non-destructive bypass harness, the insertion and mating of connectors, and the sealing and tightening operations can be performed by those skilled in the art in accordance with the corresponding vehicle model repair manual. These are well-known technologies in the field and will not be described in detail here.

[0042] S12, calibrates the thermal runaway trigger threshold time.

[0043] The system controls external testing equipment to output analog physical quantity signals to temperature sensor probes, characteristic gas sensor probes, or cell voltage sampling points. When the analog physical quantity signal reaches the alarm threshold calibrated by the battery management unit inside the electric vehicle under test, the system records the current global physical timestamp.

[0044] The alarm threshold can be determined through the vehicle model calibration file or the alarm parameters of the battery management unit read from the diagnostic interface. As a preferred method, the alarm threshold can be set to the factory-set cell temperature of the tested electric vehicle reaching 60 degrees Celsius, or the voltage drop of a single cell exceeding 0.5 volts.

[0045] When any preset trigger channel for temperature, characteristic gas, or cell voltage reaches its corresponding alarm threshold for the first time, the earliest time of reaching this threshold will be recorded as the thermal runaway trigger threshold time. Under single-channel test conditions, the moment when the preset trigger channel reaches the corresponding alarm threshold is taken as the thermal runaway trigger threshold moment. The thermal runaway trigger threshold time. This serves as the timing starting point for subsequent test instruction flow and execution action response calculations.

[0046] S13, Establish a fire safety linkage time sequence topology matrix.

[0047] The matrix creation module 10 establishes a fire safety linkage timing topology matrix in the test host computer. To adapt to the testing requirements of different vehicles under test, the matrix creation module 10 reads the controller local area network communication matrix file and the execution component specification of the electric vehicle under test. The controller local area network communication matrix file is used to define the message identifiers, transmission cycles, and signal arrangement formats between various controllers in the vehicle; the execution component specification is used to record the inherent response delay, action stroke, and drive current of the components, and other electrical and physical characteristics. The matrix creation module 10 extracts the safety response design parameters corresponding to each execution node in the vehicle from the above files and generates a fire safety linkage timing topology matrix in the form of an internal data form.

[0048] Furthermore, the acceptable theoretical delay time interval is defined by the minimum theoretical delay time. and maximum theoretical delay time Defining. The fire safety linkage timing topology matrix generated by module 10 is used for definition. For example, its matrix representation is as follows: ; In the formula, This represents the fire safety linkage time sequence topology matrix; Indicates the first The identifier of the execution node within the tested electric vehicle, where are positive integers and ; This indicates the total number of execution nodes within the tested electric vehicle. This indicates the type of actual response action bound to the corresponding execution node. For example, the actual response action type can include the action of starting the cooling water pump and building up pressure, the action of disconnecting the high-voltage contactor, and the action of lowering the car window. This represents the acceptable theoretical delay time interval for the corresponding execution node to complete the actual response action.

[0049] Minimum theoretical delay time This is set as the sum of the network command transmission time and the inherent response time required for the execution node to complete its effective action. This inherent response time can be obtained from the corresponding execution node's manufacturer's manual, and its value typically ranges from 10 milliseconds to 500 milliseconds. Minimum theoretical latency. It is used to characterize the shortest response time that the execution node can achieve under normal communication and normal driving conditions, and is used in subsequent tests to investigate premature false alarms of signals caused by underlying communication errors.

[0050] Maximum theoretical delay time This is set as the maximum tolerable time limit allowed by the vehicle's thermal runaway propagation safety standards. For example, the maximum theoretical delay time for a window lowering action. The delay time can be set to no more than 3000 milliseconds based on the overall vehicle design safety standards. After the host computer generates the above-mentioned qualified theoretical delay time interval for the dual boundaries, it can provide a digital benchmark for subsequent evaluation of whether there is any lag in the vehicle's action commands.

[0051] Reference Figure 4 In this embodiment, step S2 is implemented through the following sub-steps: S21, acquire thermal runaway decay data of the target battery pack cells and extract multidimensional features.

[0052] Real-world thermal runaway testing at the vehicle level involves irreversible physical damage and is costly. This embodiment employs an external signal injection method to non-destructively and equivalently reproduce the thermal runaway environment; therefore, it is necessary to obtain the physical evolution patterns during the actual fire process beforehand as the data benchmark for generating the simulation signal.

[0053] The sequence generation module 20 reads the historical thermal runaway decay data file of the target battery pack cells. The target battery pack cells are sample cells from the same batch and model as those inside the battery pack of the electric vehicle under test. The historical thermal runaway decay data file originates from the earlier cell-level safety certification phase: after the sample cells were subjected to forced heating or nail penetration to trigger actual thermal runaway, the physical characterization data recorded by the testing instruments were compiled. This historical thermal runaway decay data file contains records of the changes in multiple physical parameters of the target battery pack cells throughout the entire thermal runaway process.

[0054] The historical thermal runaway decay data file is a multi-channel time-series data file. Each sampling record includes at least the sampling time, the acquisition channel name, and the sampling value. The acquisition channel names include the cell surface temperature acquisition channel, the characteristic gas concentration acquisition channel, and the single cell terminal voltage acquisition channel.

[0055] The sequence generation module 20 first performs time axis alignment processing on the historical thermal runaway decay data file. When performing time axis alignment processing, the sequence generation module 20 uses the common time range in which the cell surface temperature acquisition channel, the characteristic gas concentration acquisition channel, and the single cell terminal voltage acquisition channel all have sampling records as the effective alignment time range. The starting point of the effective alignment time range is the first common sampling moment in which the above three acquisition channels all have sampling records, and the ending point is the last common sampling moment in which the above three acquisition channels all have sampling records.

[0056] The sequence generation module 20 establishes a unified test time axis within the effective alignment time range. The sampling interval of the unified test time axis is consistent with the control cycle of the external test equipment, which is 10 milliseconds or 20 milliseconds. For original sampled values ​​that do not fall on the sampling points of the unified test time axis, the sequence generation module 20 uses linear interpolation between two adjacent original sampled points to obtain the data value of the corresponding sampled point.

[0057] After completing the time axis alignment process, the sequence generation module 20 reads the data values ​​of the cell surface temperature acquisition channel, the characteristic gas concentration acquisition channel, and the single cell terminal voltage acquisition channel according to the acquisition channel name under the unified test time axis, forming aligned temperature time series data, gas time series data, and voltage time series data.

[0058] The sequence generation module 20 uses the static sampling data of the sample cell before forced heating or needle penetration as the reference data. The static sampling data is taken from the data within 10 consecutive seconds before forced heating or needle penetration begins; if the continuous sampling time before forced heating or needle penetration is less than 10 seconds, all existing continuous static sampling data before forced heating or needle penetration is taken. The sequence generation module 20 calculates the average temperature value of the cell surface temperature acquisition channel, the average concentration value of the characteristic gas concentration acquisition channel, and the average voltage value of the single cell terminal voltage acquisition channel in the static sampling data, and uses the average temperature value, average concentration value, and average voltage value as the reference values ​​for the corresponding channels.

[0059] The sequence generation module 20 forms temperature field data features based on the increase in temperature value at each sampling moment relative to the average temperature value in the aligned temperature time-series data; forms gas field data features based on the increase in characteristic gas concentration value at each sampling moment relative to the average concentration value in the aligned gas time-series data; and forms voltage field data features based on the decrease in average voltage value relative to the single cell terminal voltage value at each sampling moment in the aligned voltage time-series data. Therefore, the temperature field data features are the temperature rise curve of the cell surface temperature over time; the gas field data features are the concentration curve of the characteristic gas release concentration over time; and the voltage field data features are the voltage drop curve of the single cell terminal voltage over time.

[0060] If temperature field data features, gas field data features, and voltage field data features can be obtained simultaneously at a certain sampling moment on the unified test time axis, then the data at that sampling moment is considered valid data; if any one of the temperature field data features, gas field data features, or voltage field data features is missing at a certain sampling moment on the unified test time axis, then the data at that sampling moment will not participate in the generation of the basic injection control sequence. The sequence generation module 20 generates the temperature field data features, gas field data features, and voltage field data features required for subsequent basic injection control sequences based on the continuous valid data.

[0061] S22, the sequence generation module 20 generates a basic injection control sequence based on the extracted multidimensional features and through numerical mapping rules.

[0062] To convert the raw physical parameters into control parameters that can be executed by external testing equipment, the sequence generation module 20 establishes a mathematical mapping relationship between physical characterization data and device driving instructions.

[0063] The sequence generation module 20 converts the temperature field data characteristics into a heating power control reference sequence. This heating power control reference sequence is an instruction array built on the test time axis, used to indicate the real-time output power (watts) of the external micro-heating sleeve. To ensure that the sensor probes inside the tested electric vehicle reach the same heating rate as in actual thermal runaway, the heat output from the external micro-heating sleeve needs to compensate for the attenuation of thermal conductivity and environmental heat dissipation. The sequence generation module 20 calculates the heating power control reference sequence based on the thermal conductivity physical characteristics of the external micro-heating sleeve; the corresponding sampling time in this heating power control reference sequence... Heating power control command value The conversion formula is as follows: ; In the formula, Indicates at the sampling time The heating power control command value; This represents the equivalent specific heat capacity of the temperature sensor probe area inside the tested electric vehicle. This equivalent specific heat capacity is obtained based on the physical parameters of the probe material. This represents the equivalent thermal mass of the corresponding temperature sensor probe area, which is calculated by multiplying the probe volume by the material density. This represents the thermal conductivity coefficient from the external miniature heating sleeve to the temperature sensor probe, and its value is set between 0.6 and 0.9. This indicates the sampling time extracted from the characteristics of the temperature field data. The true rate of temperature change; This represents the static heat loss compensation value in the test connection channel. The static heat loss compensation value is calculated based on Newton's law of cooling by multiplying the surface area of ​​the outer shell of the external micro heating sleeve, the preset air convection heat transfer coefficient, and the difference between the sleeve temperature and the room temperature of the test environment at the sampling time. The air surface heat transfer coefficient under natural convection conditions can be taken as 5 to 25 Kelvin per square meter.

[0064] In the gas dimension, the sequence generation module 20 transforms the gas field data features into a characteristic gas flow control reference sequence. This characteristic gas flow control reference sequence is an array of flow control commands on a unified test time axis, used to control the opening degree of external micro-gas flow valves and release target gas at a specific concentration. As a preferred method, the sequence generation module 20, based on the pipeline volume of the external test equipment and the preset carrier gas velocity, uses the ideal gas law to convert the target gas concentration curve into the flow valve control proportional coefficient for the corresponding time node.

[0065] In the voltage dimension, the sequence generation module 20 transforms the voltage field data features into a cell voltage drop rate control reference sequence. This voltage field data feature is a true curve recording of the sharp drop in voltage across a single cell over time when an internal short circuit occurs. The cell voltage drop rate control reference sequence is an array of control commands used to instruct an external programmable voltage source to simulate the voltage drop slope caused by an internal short circuit in the cell.

[0066] In this embodiment, the sequence generation module 20 calculates the voltage difference between adjacent time sampling points in the voltage field data features, and divides the voltage difference by the corresponding sampling time interval to obtain the real-time voltage drop slope; then, the voltage drop slope is converted into the voltage reduction rate command of the external programmable voltage source at the corresponding time, so that the external programmable voltage source can reproduce the voltage change characteristics when the real cell catches fire.

[0067] After obtaining the control reference sequences of the three independent dimensions mentioned above, the sequence generation module 20 packages and splices the heating power control reference sequence, the characteristic gas flow control reference sequence, and the cell voltage drop rate control reference sequence in parallel according to a unified test timestamp to generate a complete basic injection control sequence. The basic injection control sequence, as a multi-channel synchronous timing control file set, serves as the basis for calculating the intensity changes and timing distribution of the initial output analog signal from the external test equipment to the electric vehicle under test in the subsequent closed-loop testing phase.

[0068] Reference Figure 5 In this embodiment, step S3 is implemented through the following sub-steps: S31 acquires real-time operating status data of key actuators of the tested electric vehicle through data acquisition equipment.

[0069] During the test run, the electric vehicle under test will trigger a corresponding safety response after receiving a simulated thermal runaway signal injected by external test equipment. The acquisition and mapping module 30 acquires real-time operating status data of the key actuators of the electric vehicle under test through data acquisition equipment, which is used to reflect the actual action status of the key actuators during the test process.

[0070] Critical actuators refer to the physical components in the vehicle control strategy of the electric vehicle under test that need to perform safety actions after a thermal runaway alarm. Examples include the thermal management coolant pump, battery pack vent valve, and window motors in the electric vehicle under test. The thermal management coolant pump is usually located in the thermal management integration area of ​​the front engine compartment or in the external liquid cooling pipeline of the battery pack to drive the circulation of coolant.

[0071] Real-time operational status data consists of physical quantities that characterize the current actual operating intensity of key actuators, such as the supply current of the thermal management cooling water pump or the physical opening displacement of the battery pack vent valve. In this embodiment, the data acquisition device includes current acquisition clamps and displacement sensors, etc. To ensure that high-frequency characteristics are not lost during the data acquisition process, the sampling frequency of the data acquisition device is typically set to 100 Hz to 1000 Hz.

[0072] Testers non-destructively fitted current acquisition clamps onto the outside of the power supply harness of the thermal management coolant pump to read its transient operating current; and placed displacement sensors near the actuating components of the battery pack exhaust valve to monitor its physical displacement. The sensor placement and signal reading for actuator operating current and physical displacement can be performed by those skilled in the art according to conventional vehicle testing standards, and the hard-wired signal acquisition method is well-known in the field and will not be described further here.

[0073] S32 processes real-time operating status data to extract actuator motion intensity vectors.

[0074] The acquisition and mapping module 30 filters and normalizes the acquired real-time operating status data and extracts the actuator motion intensity vector. The actuator motion intensity vector is a dimensionless structured array used to represent the current motion output ratio of different key actuators in a uniform percentage form.

[0075] The raw state data of different key actuators have different dimensions, such as current in amperes and displacement in millimeters, and cannot be directly combined. To facilitate subsequent unified calculations, the acquisition and mapping module 30 uses a moving average filtering algorithm to eliminate high-frequency electrical noise in the real-time operating status data. The size of the filtering window of the moving average filtering algorithm is determined based on the noise fluctuation period collected by the data acquisition device when the key actuator is not in operation, so that the filtering window covers a complete noise fluctuation period. For example, when the sampling frequency of the data acquisition device is 100 Hz to 1000 Hz, the filtering window size can be 5 to 20 sampling points.

[0076] After filtering is completed, the acquisition and mapping module 30 retrieves the rated full-load parameters from the specifications of each key actuator in the electric vehicle under test, divides the current filtered real-time operating status data value by the corresponding rated full-load parameter value, and obtains the proportion value of each component.

[0077] The acquisition and mapping module 30 arranges the above percentage values ​​in a preset node order (for example, in ascending order of node identifiers in the local area network communication matrix file of the electric vehicle controller under test), thereby forming the actuator action intensity vector. The superscript This represents the transpose of a vector. Indicates the first A key actuator at the sampling time The normalized action intensity, the normalized action intensity The value range is from 0 to 1. , This refers to the total number of key actuators involved in the testing and monitoring.

[0078] S33, construct a propagation suppression dynamic mapping model containing a preset weight influence matrix, and output the overall propagation suppression effectiveness vector.

[0079] In actual operation, the vehicle's fire protection system physically intervenes in the thermal runaway propagation environment. For example, the thermal management cooling water pump, after circulating and pressurizing, can remove internal heat, thereby suppressing a rapid rise in the temperature field. To quantify this intervention effect, the acquisition and mapping module 30 constructs a propagation suppression dynamic mapping model containing a preset weighted influence matrix within the test host computer.

[0080] The propagation suppression dynamic mapping model is used to equivalently map the actual physical actions of critical actuators to the quantified suppression effectiveness of various dimensions of the thermal runaway environment. The mathematical relationship of this propagation suppression dynamic mapping model is a matrix multiplication equation obtained by multiplying a preset weighted influence matrix by the actuator action intensity vector. The preset weighted influence matrix serves as a fixed parameter, the actuator action intensity vector serves as the real-time input, and the overall propagation suppression effectiveness vector serves as the output. The preset weighted influence matrix reflects the unit suppression capability of a single critical actuator to the abnormal evolution trends of the temperature field, gas field, and voltage field when it reaches full-load operating conditions.

[0081] As a preferred approach, the acquisition and mapping module 30 achieves dynamic mapping through the following matrix multiplication mathematical logic, and outputs the overall propagation suppression effectiveness vector in real time for subsequent calculation of the cumulative deduction amount of the closed-loop feedback: ; ; ; In the formula, Indicates at the sampling time The overall propagation suppression effectiveness vector of the key actuator actions of the tested electric vehicle in the thermal runaway environment; Indicates at the sampling time The equivalent power change rate of thermal management cooling actions such as cooling water pumps suppressing temperature field accumulation; Indicates at the sampling time The rate of change of the equivalent flow rate of the venting action of the battery pack vent valve and other venting actions on the concentration of characteristic gases. Indicates at the sampling time The equivalent slope change rate of the suppression of abnormal voltage evolution on the sampling side voltage of the battery management unit by protective actions such as high voltage contactor disconnection; This represents the preset weight influence matrix; This indicates that the preset weight influence matrix is ​​a 3-row matrix. A matrix of columns; Represents the preset weight influence matrix The Middle Line 1 The column's weight coefficient; Indicates the first A key actuator at the sampling time Normalized action intensity. This weighting coefficient. Indicates the first The key actuator is for the first The suppression conversion coefficient of each thermal runaway decay dimension, where The value can be 1, 2 or 3. Values ​​range from 1 to Positive integers.

[0082] To determine the weighting coefficients , for the Several key actuators underwent vehicle-level single-component response calibration testing. The key actuator is any component in the electric vehicle under test that participates in the thermal runaway fire prevention linkage control, such as the thermal management coolant pump, battery pack vent valve, etc. The vehicle-level single-component response calibration test is conducted under the condition that the battery pack, battery management unit, thermal management pipelines, vent passages, and high-voltage connection components of the electric vehicle under test are all in the fully assembled state; except for the first... Apart from one key actuator, the remaining key actuators remain inactive. The calibration ambient temperature is 25℃±2℃, the calibration ambient pressure is 101 kPa±5 kPa, and the sampling period is consistent with the control period of the external test equipment, which is 10 milliseconds or 20 milliseconds.

[0083] Before performing the calibration test, make the first The acquisition channel corresponding to the first thermal runaway decay dimension is in a measurable initial state. When the first... When the thermal runaway decay dimension is the temperature field dimension, the corresponding measuring point of the cell surface temperature acquisition channel is heated to 60℃±2℃ and then heating is stopped; when the... When the thermal runaway decay dimension is a gas field dimension, the sampling chamber containing the characteristic gas concentration acquisition channel is filled with a characteristic gas of the same type as that in the historical thermal runaway decay data file. The filling is stopped once the characteristic gas concentration reaches the midpoint of the gas sensor's range. For example, if the gas sensor's range is 0 ppm to 1000 ppm, the characteristic gas concentration is set to 500 ppm. When the... When the thermal runaway decay dimension is the voltage field dimension, the voltage acquisition channel of the individual cell is connected to a DC voltage signal that is the same as the nominal single cell voltage of the target battery pack cell.

[0084] Sequence generation module 20 or calibration recording device first in Data was collected continuously for 60 seconds while the key actuators were inactive, resulting in the first... The calibration baseline data for the acquisition channel corresponds to the thermal runaway decay dimension; subsequently, the first thermal runaway decay dimension is driven. Each key actuator independently reaches its rated operating state and maintains it for 60 seconds, while synchronously collecting the data. Each thermal runaway decay dimension corresponds to the timing data of the acquisition channel. The rated operating state refers to the thermal management cooling water pump reaching its rated speed, the exhaust valve reaching its fully open state, the high-voltage contactor reaching its fully open state, or other key actuators reaching their corresponding maximum operating state in the vehicle fire prevention linkage control.

[0085] The acquired time-series data are transformed according to the generation method of control quantities of the same dimension in the basic injection control sequence. Specifically, the temperature field dimension is converted into the unit time deduction of the heating power control quantity according to the method of generating the heating power control quantity based on the temperature field data characteristics; the gas field dimension is converted into the unit time deduction of the characteristic gas flow control quantity according to the method of generating the characteristic gas flow control quantity based on the gas field data characteristics; and the voltage field dimension is converted into the unit time deduction of the cell voltage drop rate control quantity according to the method of generating the cell voltage drop rate control quantity based on the voltage field data characteristics.

[0086] After completing the dimension conversion, the calculation window is 1 second, and the result is calculated at the second second. Within a 60-second data acquisition interval after each key actuator reaches its rated operating state, the decrease in the corresponding control quantity relative to the calibration reference data is calculated within each 1-second calculation window, and this decrease is converted into a decrease per second. The largest decrease per second within the 60-second data acquisition interval is selected as the first... The key actuator is for the first Weighting coefficients for each thermal runaway decay dimension The value. Due to For the first The normalized motion intensity of the key actuators, and the first When each key actuator is in its rated operating state Therefore, the weighting coefficient Indicates the first The key actuator operates in its rated state for the first... The maximum unit time deduction generated by each thermal runaway decay dimension.

[0087] When the The first key actuator failed to activate the second key actuator under rated operating conditions. When the control quantity corresponding to the thermal runaway decay dimension decreases, or when the corresponding control quantity changes in the direction that promotes thermal runaway evolution, the first... The key actuator is for the first There is no direct inhibitory effect on the thermal runaway decay dimension, and the corresponding weighting coefficients are... The value is 0.

[0088] By introducing the aforementioned propagation suppression dynamic mapping model, the test system transforms the discrete and heterogeneous vehicle protection actions into a digital dimension consistent with the test input source, providing a theoretical calculation basis for dynamically deducting the injected signal based on the actual vehicle response in subsequent closed-loop control.

[0089] Reference Figure 6 In this embodiment, step S4 is implemented through the following sub-steps: S41, Calculate the dynamically corrected actual injection control sequence.

[0090] The basic injection control sequence is used to characterize the thermal runaway evolution trend without considering vehicle protection actions; in actual testing, the safety actuators of the tested electric vehicle continuously counteract this evolution trend. The safety actuators of the tested electric vehicle, such as the thermal management cooling water pump, have an intervening characteristic over time, so the constraint injection module 40 uses a discrete time step accumulation algorithm to calculate this cumulative effect.

[0091] Before the overall propagation suppression effectiveness vector participates in the deduction calculation, it is first transformed into an equivalent normalization according to the control quantity dimension corresponding to the basic injection control sequence. This ensures that the suppression components of the temperature field, gas field, and voltage field have the same deduction dimension as the heating power control quantity, characteristic gas flow control quantity, and cell voltage drop rate control quantity, respectively. The equivalent normalization transformation includes channel correspondence transformation and unit time deduction confirmation; specifically, the temperature field suppression component is converted into the unit time deduction of the heating power control quantity, the gas field suppression component is converted into the unit time deduction of the characteristic gas flow control quantity, and the voltage field suppression component is converted into the unit time deduction of the cell voltage drop rate control quantity.

[0092] Specifically, the constraint injection module 40 retrieves the overall propagation suppression effectiveness vector output in real time by the propagation suppression dynamic mapping model and performs an equivalent normalization transformation on the overall propagation suppression effectiveness vector. During the equivalent normalization transformation, the constraint injection module 40, according to the channel attributes of each component in the overall propagation suppression effectiveness vector, assigns the temperature field suppression component to the heating power control quantity deduction channel, the gas field suppression component to the characteristic gas flow control quantity deduction channel, and the voltage field suppression component to the cell voltage drop rate control quantity deduction channel; the component values ​​in each deduction channel are the corresponding control quantity deduction per unit time obtained in the weighting coefficient calibration stage. After completing the equivalent normalization transformation, the constraint injection module 40 integrates and accumulates the transformed overall propagation suppression effectiveness vector along the test time axis at each sampling time, calculating the total physical suppression deduction vector from the initial test time to the sampling time.

[0093] The heating power control reference sequence, the characteristic gas flow control reference sequence, and the cell voltage drop rate control reference sequence in the basic injection control sequence are used as reference inputs for the corresponding channels of the temperature field, gas field, and voltage field, respectively, to participate in the dynamic correction calculation.

[0094] The constraint injection module 40 subtracts the total physical suppression deduction vector from the basic injection control sequence at the corresponding sampling time, completing the negative feedback deduction of the control command to obtain the dynamically corrected actual injection control sequence. This actual injection control sequence is the net intervention control command set formed after considering the vehicle's own safety protection capabilities. The corresponding mathematical feedback formula is as follows: ; ; In the formula, Indicates at the sampling time The total physical suppression deduction vector is generated due to the accumulated response of the key actuators in the tested electric vehicle. This represents the historical sampling time from the start of the test to the sampling time, with values ​​ranging from 0, 1, 2, ... ; Indicates the time of historical sampling The overall propagation suppression effectiveness vector output by the propagation suppression dynamic mapping model; This indicates the sampling time step between two adjacent sampling moments. This sampling time step is consistent with the control cycle of the external test equipment, for example, it can be 10 milliseconds or 20 milliseconds. Indicates at the sampling time The basic injection control sequence; Indicates at the sampling time The generated actual injection control sequence.

[0095] S42 applies asymmetric unidirectional boundary constraints to the actual injected control sequence.

[0096] As the safety protection mechanisms of the tested electric vehicle continue to be involved, negative values ​​may appear in the actual injected control sequence at some sampling moments. External testing equipment only has unidirectional output characteristics in its physical structure. For example, external miniature heating sleeves can only actively output heat and cannot actively absorb heat or perform cooling actions. If negative value commands are directly issued, it may cause the equipment control program to malfunction or make the hardware unable to execute the command.

[0097] Therefore, the constraint injection module 40 applies asymmetric unidirectional boundary constraints to the actual injection control sequence. These asymmetric unidirectional boundary constraints are operational rules that differentiate truncation based on the positive and negative polarity of the control command. They are used to restrict external testing equipment to only output positive injection control quantities that promote thermal runaway environment simulation, and not to output reverse control quantities that are opposite to the direction of thermal runaway environment simulation.

[0098] In specific processing, the constraint injection module 40 reads the multidimensional array elements in the actual injection control sequence one by one. When the value of an element in the actual injection control sequence is greater than zero, it indicates that the destructive energy released by internal thermal runaway still exceeds the offsetting capacity of the current vehicle protection mechanism, and the constraint injection module 40 retains this positive calculation value.

[0099] When the value of an element in the actual injected control sequence is less than or equal to zero, it indicates that the fire prevention intervention mechanism of the tested electric vehicle has suppressed the thermal runaway evolution trend. The constraint injection module 40 truncates the corresponding element value and assigns it to zero. This processing method does not involve external testing equipment actively generating reverse cooling, reverse evacuation, or reverse pressurization actions, but rather stops applying the corresponding dimension of positive thermal runaway simulation excitation to simulate the natural fallback state after the actual fire deterioration process is blocked. The corresponding constraint formula is as follows: ; In the formula, Indicates at the sampling time The final output control sequence after processing with asymmetric unidirectional boundary constraints; This represents a mathematical comparison function that extracts the maximum value from each element; in the formula... Representative and The zero vector is of the same dimension.

[0100] S43 controls the output of multi-dimensional physical injection signals from external testing equipment.

[0101] After boundary constraints are completed, the constraint injection module 40 drives the relevant hardware to perform physical actions according to the final output control sequence. The injected signal is a real physical excitation parameter generated by the external test equipment based on the final output control sequence and directly applied to the induction probes inside the electric vehicle under test.

[0102] To achieve independent control of multiple physical quantities, the constraint injection module 40 decouples the final output control sequence into actual heating power command, actual characteristic gas flow command, and actual cell voltage drop rate command according to the type of physical quantity.

[0103] The constraint injection module 40 sends the actual heating power command to the external micro heating sleeve, driving the external micro heating sleeve to output the corresponding thermal radiation power to heat the temperature sensor inside the vehicle; it sends the actual characteristic gas flow command to the external micro gas flow valve, adjusting the valve opening to release the pre-stored characteristic mixed gas and stimulate the gas sensor inside the vehicle; it sends the actual cell voltage drop rate command to the external programmable voltage source, adjusting the equivalent voltage amplitude on the sampling pin side of the battery management unit of the electric vehicle under test according to the set drop rate.

[0104] Through the above-mentioned feedback-constrained multi-channel physical quantity injection process, this embodiment forms a closed-loop link that dynamically increases or decreases the physical test intensity based on the actual protection response of the vehicle.

[0105] Reference Figure 7 In this embodiment, step S5 is implemented through the following sub-steps: S51, obtain the actual action time and corresponding network timestamp of each execution node completing the real response action.

[0106] To establish a unified time reference, the source tracing and analysis module 50 directly retrieves the pre-calibrated thermal runaway trigger threshold time from step S12. This is then used as the starting point for the entire chain of subsequent delay calculations. Subsequently, the source tracing module 50 uses hardware sensors to acquire the actual action times of each execution node completing its real response. During execution, the source tracing module 50 continuously monitors the real-time operating status data uploaded by the data acquisition device.

[0107] For different execution nodes, the actual action time is determined according to the action type of the corresponding execution node, and the actual response action is determined by at least one of the following operating status data obtained by the data acquisition device: working current, pipeline pressure, opening displacement, auxiliary contact status, high voltage circuit voltage, window position or operation completion signal.

[0108] When the real-time operating status data of an execution node indicates that the execution node has completed the corresponding actual response action, the source tracing and analysis module 50 records the absolute time corresponding to the sampling point as the actual action time of the execution node. This actual action time is used to characterize the time it takes for the physical component to complete the actual response action.

[0109] Simultaneously, the traceability and analysis module 50 monitors the data stream of the vehicle controller local area network bus of the electric vehicle under test through the message reading interface of the test host computer, parses and captures specific message frames that control the action of the execution node, and records the transmission time of the specific message frame on the vehicle controller local area network bus of the electric vehicle under test as a network timestamp. This network timestamp is used to characterize the time when the vehicle electronic control unit issues control commands.

[0110] The parsing and timestamp capture of the vehicle controller local area network bus messages of the electric vehicle under test can be accomplished by those skilled in the art using a standard vehicle network protocol stack; the message listening and timestamp marking methods are well-known technologies in the field and will not be described in detail here.

[0111] S52, calculate the actual end-to-end delay and compare it with the fire safety linkage timing topology matrix for verification.

[0112] To evaluate the response speed of the tested electric vehicle after a thermal runaway alarm, the source analysis module 50 calculates the actual end-to-end delay. The actual end-to-end delay is the time elapsed from the thermal runaway trigger threshold to the physical actuator completing the corresponding actual response action. As a preferred method, the source analysis module 50 subtracts the thermal runaway trigger threshold from the actual action time to obtain the actual end-to-end delay of the corresponding execution node, calculated using the following formula: ; In the formula, Indicates the actual end-to-end latency; Indicates the actual moment of the action; This indicates the threshold time for thermal runaway to be triggered.

[0113] After obtaining the actual end-to-end latency of each execution node, the source tracing and analysis module 50 compares and verifies it with the previously constructed fire safety linkage time sequence topology matrix.

[0114] Specifically, the source tracing and analysis module 50 extracts the qualified theoretical delay time interval of the corresponding execution node under the current preset test conditions from the fire safety linkage timing topology matrix. If the actual delay of the entire link falls within the qualified theoretical delay time interval, the source tracing and analysis module 50 determines that the fire safety linkage test result of the execution node is qualified; if the actual delay of the entire link does not fall within the qualified theoretical delay time interval, such as when there are deviation characteristics such as severe response timeout or premature and disordered actions, the fire safety linkage test result of the execution node is determined to be unqualified, and the corresponding test fault record is triggered.

[0115] Through the above comparison and verification, it is possible to judge whether the timing of the vehicle's safe actions meets the requirements based on the engineering design boundaries of the tested electric vehicle.

[0116] S53 combines network timestamps to perform fault source analysis and determination.

[0117] When the fire safety linkage test result of a certain execution node is determined to be unqualified, specifically manifested as a response timeout, the source tracing and analysis module 50 combines the network timestamp to perform time difference analysis to determine whether the fault occurred in the communication control link or the physical execution link.

[0118] A complete vehicle physical response typically includes the control logic operation and bus communication issuance phase, and the physical actuator receiving instructions and generating actual actions phase. Based on this process, the source analysis module 50 decomposes the end-to-end actual delay into network communication delay and physical execution delay. The network communication delay is the difference between the network timestamp and the thermal runaway trigger threshold, and the physical execution delay is the difference between the actual action time and the network timestamp. The calculation formulas are as follows: ; ; In the formula, Indicates network communication delay; Represents a network timestamp; Indicates physical execution delay; Indicates the actual moment of action.

[0119] After obtaining the aforementioned differential time parameters, the source tracing and analysis module 50 performs independent and combined verifications on each delay parameter to reduce missed detections caused by a single judgment condition. Specifically, this includes the following situations: (1) If the network communication delay is greater than the preset network communication tolerance threshold and the physical execution delay is less than or equal to the preset physical execution tolerance threshold, it indicates that the fault is concentrated in the decision-making operation of the vehicle electronic control unit or the bus channel scheduling congestion. The fault source analysis module 50 outputs the fault source analysis judgment result as a fault in the electronic control communication link.

[0120] (2) If the physical execution delay is greater than the preset physical execution tolerance threshold and the network communication delay is less than or equal to the preset network communication tolerance threshold, it indicates that the control command has been issued on time, but the end execution component has failed to respond in time. The fault source analysis module 50 outputs the fault source analysis judgment result as physical execution layer lag fault.

[0121] (3) If the network communication delay and the physical execution delay are both greater than their corresponding tolerance thresholds, it indicates that there is a delay in both signal transmission and physical execution. The fault source analysis module 50 will output the fault source analysis judgment result as a full-link cascade timeout fault.

[0122] (4) If the sum of the preset network communication tolerance threshold and the physical execution tolerance threshold is greater than the maximum theoretical delay time, and if the network communication delay and the physical execution delay are both less than or equal to their respective tolerance thresholds, but the sum of the two, i.e. the actual delay of the entire link, exceeds the maximum theoretical delay time of the qualified theoretical delay time interval, it indicates that although the duration of the action of a single sub-link has not exceeded the corresponding tolerance threshold, the cumulative time of the entire link has exceeded the security requirements. The source tracing and analysis module 50 outputs the fault source tracing and analysis judgment result as the overall timing tolerance superimposed fault.

[0123] In this embodiment, the preset network communication tolerance threshold can be determined based on the maximum transmission period parameter of the corresponding control message specified in the communication matrix of the local area network bus of the vehicle controller of the electric vehicle under test, for example, a value of 50 milliseconds; the preset physical execution tolerance threshold can be determined based on the electromechanical setup response time parameter specified in the hardware specification of the corresponding execution node, for example, a value of 200 milliseconds.

[0124] Through the above time-difference analysis, the source tracing and analysis module 50 can distinguish communication control anomalies, physical execution delays, and cumulative timeouts in the vehicle safety linkage link, providing a basis for subsequent technical troubleshooting.

[0125] To further illustrate the implementation process and technical effects of this invention, the following description is provided in conjunction with specific application scenarios and experimental data. The specific numerical values, scenario parameters, and comparative experiments described below are only used to explain the implementation principle of this invention and do not limit the scope of protection of this invention.

[0126] Before conducting the test, clarify the specific configuration parameters of each module and control sequence: A fire safety linkage timing topology matrix is ​​constructed. This matrix is ​​a two-dimensional data table extracted from the vehicle controller local area network communication matrix file and the specifications of the actuators of the electric vehicle under test. The rows of this fire safety linkage timing topology matrix represent the various actuator nodes within the electric vehicle under test, while the columns contain the identifier of each actuator node, the corresponding actual response action type, and the extracted minimum and maximum theoretical delay times.

[0127] The host computer for testing is pre-installed with test control software and connects to external test equipment and data acquisition equipment via a communication bus. The external test equipment includes an external miniature heating sleeve, an external micro-gas flow valve, and an external programmable voltage source. The data acquisition equipment includes a current acquisition clamp and a pipeline pressure sensor. The current acquisition clamp is used to acquire the operating current of the thermal management cooling water pump, and the pipeline pressure sensor is used to acquire the pipeline pressure of the liquid cooling pipeline where the thermal management cooling water pump is located.

[0128] The external micro heating sleeve in the external testing equipment is formed by winding a flexible polyimide electrothermal film. This external micro heating sleeve wraps around the surface of the original temperature sensor probe inside the electric vehicle under test, and achieves local temperature increase through the Joule heating effect.

[0129] The basic injection control sequence is a multi-channel synchronous timing control file set, which includes a heating power control reference sequence, a characteristic gas flow control reference sequence, and a cell voltage drop rate control reference sequence. Specifically, the heating power control reference sequence is an array of instructions generated based on the actual thermal runaway temperature rise data of the target battery pack cells, indicating the output power value of the external micro-heating sleeve; the characteristic gas flow control reference sequence is an array of instructions generated based on the actual characteristic gas release concentration data, indicating the output characteristic gas flow value of the external micro-gas flow valve; and the cell voltage drop rate control reference sequence is an array of instructions generated based on the actual short-circuit voltage drop curve inside a single cell, indicating the voltage reduction rate of the external programmable voltage source.

[0130] The propagation suppression dynamic mapping model is a mapping function containing a preset weighted influence matrix. This preset weighted influence matrix is ​​generated through the aforementioned vehicle-level single-component response calibration test. Each weight coefficient in the matrix is ​​used to characterize the maximum unit-time deduction of the corresponding key actuator under rated operating conditions for the corresponding thermal runaway decay dimension.

[0131] Phase 1: Step S1: Establish a fire safety linkage time sequence topology matrix and select the thermal management cooling water pump in the tested electric vehicle as the target execution node. The actual response action of the thermal management cooling water pump. This is for pump start-up and pressure building. According to the specifications of the thermal management cooling water pump, the minimum theoretical delay time is determined to be 50 milliseconds, and according to the vehicle thermal runaway propagation prevention and control safety standards, the maximum theoretical delay time is determined to be 2000 milliseconds. Therefore, the qualified theoretical delay time range is determined to be [50, 2000] milliseconds.

[0132] Step S2: Generate the basic injection control sequence. Taking heating power control as an example, at the first sampling time... Extract the first sampling moment from the thermal runaway decay data of the target battery pack cells. True temperature change rate Degrees Celsius per second. Obtain the material physical parameters of the temperature sensor probe area to determine the equivalent specific heat capacity. Joules per kilogram per degree Celsius corresponds to the equivalent thermal mass of the temperature sensor probe area. Kilogram. The thermal conductivity coefficient is determined based on the material thermal conductivity of the external micro-heating sleeve. The static heat loss compensation value in the test connection channel is determined based on the air surface heat transfer coefficient under natural convection conditions. Watt. Calculated according to the formula at the first sampling time. Heating power control command value: watt.

[0133] Phase Two: Step S3: During the test run, the acquisition and mapping module 30 acquires real-time operating status data of the key actuators of the electric vehicle under test through the data acquisition device. At the target sampling time... The data acquisition equipment obtains that the operating current of the thermal management cooling water pump reaches 80% of the rated full-load parameter of the thermal management cooling water pump, and extracts the normalized operating intensity of the thermal management cooling water pump. The preset weighted influence matrix generated by the above calculation is retrieved, and the suppression conversion coefficient of the thermal management cooling water pump on the temperature field is extracted. Watts per second. Propagation suppression dynamic mapping model output at the target sampling time. Overall spread suppression efficiency vector (Here, the equivalent quantization verification is performed using the value of the channel corresponding to the suppressed equivalent power change rate in the temperature field as an example.) Watts per second.

[0134] Step S4: Calculate the dynamically corrected actual injection control sequence and apply constraints, selecting the sampling time step between two adjacent sampling times. Seconds. Set the target sampling time. When the cumulative effective suppression time of the thermal management cooling water pump after entering a stable output state reaches 10 seconds, the historical sampling times from the test start point to the target sampling time are analyzed. (Its value is) The overall spread suppression effectiveness vector Perform integral accumulation. Set the thermal management cooling water pump to maintain 80% output intensity during this period (i.e., at each historical sampling time). Overall spread suppression efficiency vector (Constantly 8 watts per second), with a cumulative sampling step of 500, the result at the target sampling time is calculated. The total physical suppression subtraction vector resulting from the accumulated responses of key actuators in the tested electric vehicle. for: 8 watts per second × 0.02 seconds × 500 = 80 watts.

[0135] At this point, the target sampling time is set. Basic injection control sequence The value is 100 watts (specifically referring to the heating power control reference sequence of the same dimension as the above deduction). This total physical suppression deduction vector is subtracted from the base injection control sequence at the corresponding sampling time to complete the negative feedback deduction of the control command, generating the value at the target sampling time. Actual injection control sequence : watt.

[0136] Subsequently, asymmetric unidirectional boundary constraints are applied to the actual injected control sequence. Since the value 20 of the actual injected control sequence is greater than 0, this forward calculated value is retained, yielding the final output control sequence. : Watt. The constraint injection module 40 controls the final output sequence. The system generates an actual heating power command of 20 watts and sends it to an external testing device. The external testing device then outputs the corresponding driving power to the external micro heating sleeve, causing the external micro heating sleeve to generate 20 watts of heat power.

[0137] Phase Three: Step S5: The source tracing and analysis module 50 retrieves the thermal runaway trigger threshold time from the initial record. The source tracing and analysis module 50 obtains the network timestamp of the thermal management cooling water pump start-up control message. .

[0138] The source analysis module 50 determines the actual moment when the thermal management cooling water pump completes its pump start-up and pressure build-up action by monitoring the operating current obtained from the data acquisition equipment and determining whether the operating current enters the stable operating range defined by the specifications of the actuator of the thermal management cooling water pump, or whether the pipeline pressure enters the stable pressure build-up range defined by the design pressure build-up parameters of the liquid cooling pipeline where the thermal management cooling water pump is located. .

[0139] Calculate the actual end-to-end latency: millisecond.

[0140] The actual end-to-end delay of 2250 milliseconds did not fall within the acceptable theoretical delay time range of [50, 2000] milliseconds. After comparing and verifying the actual end-to-end delay with the fire safety linkage timing topology matrix, the fire safety linkage test result of the thermal management cooling water pump was determined to be unqualified, and the fault tracing process was triggered.

[0141] Time-segment difference analysis based on the network timestamp: millisecond.

[0142] millisecond.

[0143] Based on the maximum transmission period parameter of the corresponding control message specified in the local area network bus communication matrix of the electric vehicle controller under test, the network communication tolerance value is determined to be 50 milliseconds; based on the electromechanical setup response time parameter specified in the hardware specification of the corresponding execution node, the physical execution tolerance value is determined to be 200 milliseconds. Since the network communication delay of 2100 milliseconds is greater than its corresponding network communication tolerance value of 50 milliseconds, and the physical execution delay of 150 milliseconds is less than or equal to its corresponding physical execution tolerance value of 200 milliseconds, the fault tracing and analysis module 50 outputs the fault tracing and analysis result as a fault in the electronic control communication link.

[0144] Experimental verification and effect comparison: A comparative experiment was conducted using four electric vehicles of the same batch and model. Two of the tested electric vehicles were designated as the control group, and the other two were designated as the experimental group. The test was conducted under uniform trigger boundary conditions.

[0145] The control group used a traditional static open-loop hardware-in-the-loop (HIL) testing method. The specific operational logic of this static open-loop HIL testing method is as follows: the external testing equipment only injects simulated physical quantity signals into the electric vehicle under test (EVD) through a non-destructive bypass harness, based on pre-recorded fixed temperature change curves, characteristic gas change curves, and cell voltage drop curves from the host computer. During the test, the external testing equipment does not receive real-time operating status data of the EVD's key actuators, and the injected signals are not dynamically corrected based on the EVD's protective actions.

[0146] The experimental group adopted the electric vehicle thermal runaway fire safety linkage test method of this embodiment.

[0147] The test results comparison indicators and data are as follows: (1) Thermal evolution curve fitting degree: The actual temperature rise curves measured by the internal temperature sensors of the two tested electric vehicles were obtained, and the measured temperature rise curves were compared with the thermal evolution baseline curve under the target safety response condition. The thermal evolution baseline curve was obtained by converting the actual thermal runaway decay data of the target battery pack cells into the design response parameters of the safety protection mechanism of the tested electric vehicle. The thermal evolution curve fitting degree was calculated by subtracting the curve deviation from 1. The control group maintained static forced injection because it did not accept the real-time operating status data feedback of the key actuators, and the curve deviation was 23%, with a corresponding thermal evolution curve fitting degree of 77%. The experimental group reduced the curve deviation to 3.5% due to the application of the deduction logic of the propagation suppression dynamic mapping model containing the preset weight influence matrix, with a corresponding thermal evolution curve fitting degree of 96.5%.

[0148] (2) Non-destructive testing success rate: The non-destructive testing success rate is calculated by dividing the number of tested electric vehicles that did not suffer hardware damage by the number of tested electric vehicles participating in the corresponding group test. In the control group, the temperature sensor of one tested electric vehicle was physically burned out because the external micro heating sleeve was continuously heated by the external testing equipment according to the open-loop heating power command that was not deducted. The non-destructive testing success rate was 50%. In the experimental group, under the control of asymmetric unidirectional boundary constraints, neither of the two tested electric vehicles suffered hardware damage. The non-destructive testing success rate was 100%.

[0149] (3) Fault source resolution rate: Before the experiment, the same communication congestion fault was injected into the controller local area network bus of the four tested electric vehicles by modifying the message sending period through software. The fault source resolution rate was calculated by dividing the number of tested electric vehicles that correctly output the fault source resolution result by the number of tested electric vehicles with injected faults. The static open-loop hardware-in-the-loop test method used by the control group only recorded the response timeout and could not distinguish between network communication delay and physical execution delay, so the fault source resolution rate was 0%. The experimental group calculated the difference between the network timestamp and the actual action time and output the correct fault source resolution result for the communication congestion fault injected into the two tested electric vehicles, that is, accurately determined to be a fault in the electronic control communication link, so the fault source resolution rate was 100%.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for testing the fire safety linkage of thermal runaway in an electric vehicle, characterized in that, Includes the following steps: Establish a fire safety linkage time-series topology matrix; Generate a basic injection control sequence based on the thermal runaway decay data of the target battery pack cells; Acquire real-time operating status data of the actuators of the tested electric vehicle, extract the actuator action intensity vector, input a propagation suppression dynamic mapping model containing a preset weight influence matrix to output the overall propagation suppression effectiveness vector; The actual injection control sequence is calculated based on the basic injection control sequence and the overall propagation suppression effectiveness vector. Asymmetric unidirectional boundary constraints are applied to the actual injection control sequence to control the external test equipment to output injection signals to the sensors of the electric vehicle under test. The actual action time of the execution node in the electric vehicle under test to complete the real response action is obtained, as well as the network timestamp of the controller local area network bus of the electric vehicle under test to issue the corresponding control command. The actual delay of the whole link is calculated and compared with the fire safety linkage timing topology matrix for verification. The fault source analysis is determined by combining the network timestamp.

2. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 1, characterized in that, The steps for establishing the fire safety linkage time-series topology matrix include: The external testing equipment is controlled to output a simulated physical quantity signal. When the simulated physical quantity signal reaches the alarm threshold value calibrated by the battery management unit inside the electric vehicle under test, the thermal runaway trigger threshold time is recorded. Read the controller local area network communication matrix file and the execution component specification of the electric vehicle under test, and extract the safety response design parameters corresponding to each execution node in the electric vehicle under test; The fire safety linkage timing topology matrix is ​​generated based on the safety response design parameters. The fire safety linkage timing topology matrix defines the qualified theoretical delay time interval for the execution node to complete the actual response action.

3. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 2, characterized in that, The qualified theoretical delay time interval is defined by the minimum theoretical delay time and the maximum theoretical delay time; The minimum theoretical delay time is set as the sum of the network instruction transmission time and the inherent response time required for the execution node to complete an effective action. The maximum theoretical delay time is set as the limit of the tolerance time allowed by the vehicle thermal runaway propagation prevention and control safety standard.

4. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 1, characterized in that, The step of generating the basic injection control sequence based on the thermal runaway decay data of the target battery pack cells includes: The thermal runaway decay data is time-axis aligned, and the temperature field data features, gas field data features, and voltage field data features that change with time are extracted according to the acquisition channels. The temperature field data features are converted into a heating power control reference sequence, the gas field data features are converted into a characteristic gas flow control reference sequence, and the voltage field data features are converted into a cell voltage drop rate control reference sequence. The heating power control reference sequence, the characteristic gas flow control reference sequence, and the cell voltage drop rate control reference sequence are packaged and spliced ​​in parallel according to a unified test timestamp to generate the basic injection control sequence.

5. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 1, characterized in that, The steps of acquiring real-time operating status data of key actuators in the tested electric vehicle, extracting actuator action intensity vectors, and inputting a propagation suppression dynamic mapping model containing a preset weighted influence matrix to output an overall propagation suppression effectiveness vector include: The real-time operating status data is filtered using a moving average filtering algorithm to eliminate electrical noise. The filtered real-time operating status data value is divided by the rated full load parameter value corresponding to the key actuator to calculate the percentage value. The percentage values ​​are arranged sequentially according to a preset node order to form the actuator action intensity vector; The overall propagation suppression effectiveness vector is obtained by multiplying the preset weight influence matrix with the actuator action intensity vector.

6. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 1, characterized in that, The step of calculating the actual injection control sequence based on the basic injection control sequence and the overall propagation suppression effectiveness vector includes: The overall propagation suppression effectiveness vector is transformed by equivalent normalization, and the transformed overall propagation suppression effectiveness vector is integrated and accumulated along the test time axis at each sampling time to calculate the total physical suppression deduction vector from the initial test time to the sampling time. The actual injection control sequence is obtained by subtracting the total physical suppression deduction vector from the basic injection control sequence at the corresponding sampling time to complete the negative feedback deduction of the control command.

7. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 6, characterized in that, The step of applying asymmetric unidirectional boundary constraints to the actual injection control sequence and controlling the external test equipment to output injection signals to the sensors of the electric vehicle under test includes: Read the multidimensional array elements in the actual injection control sequence one by one. When the element value of the actual injection control sequence is greater than zero, retain the positive calculation value. When the value of an element in the actual injected control sequence is less than or equal to zero, the corresponding element value is truncated and assigned the value of zero to obtain the final output control sequence. The final output control sequence is decoupled into actual heating power command, actual characteristic gas flow rate command and actual cell voltage drop rate command according to the type of physical quantity. Based on the actual heating power command, the actual characteristic gas flow command, and the actual cell voltage drop rate command, the external testing equipment is driven to output the injection signal to the sensors of the electric vehicle under test.

8. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 3, characterized in that, The steps of obtaining the actual action time of the execution node in the tested electric vehicle completing the real response action, and the network timestamp of the corresponding control command issued by the controller local area network bus of the tested electric vehicle, calculating the actual end-to-end delay, and comparing and verifying it with the fire safety linkage timing topology matrix include: When the real-time running status data of a certain execution node indicates that the execution node has completed a real response action, the corresponding absolute time is recorded as the actual action time; Monitor the data stream of the controller local area network bus of the electric vehicle under test, capture specific message frames that control the actions of the execution node, and record the transmission time of the specific message frames on the controller local area network bus as the network timestamp; Subtracting the thermal runaway trigger threshold time from the actual action time yields the actual end-to-end latency of the corresponding execution node. Extract the qualified theoretical delay time interval of the corresponding execution node from the fire safety linkage timing topology matrix, and determine whether the actual delay of the entire link falls within the qualified theoretical delay time interval.

9. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 8, characterized in that, The steps for fault tracing and analysis based on the network timestamp include: When it is determined that the actual delay of the entire link does not fall within the qualified theoretical delay time interval, the fault source analysis is performed in conjunction with the network timestamp. The actual end-to-end latency is decoupled and calculated as network communication latency and physical execution latency, wherein the network communication latency is the difference between the network timestamp and the thermal runaway trigger threshold time, and the physical execution latency is the difference between the actual action time and the network timestamp. The network communication delay and the physical execution delay are independently and jointly verified, and the corresponding fault tracing and analysis results are output.

10. The method for testing the thermal runaway fire safety linkage of an electric vehicle according to claim 9, characterized in that, The step of independently verifying and combining the network communication delay and the physical execution delay, and outputting the corresponding fault tracing and analysis result includes: If the network communication delay is greater than the preset network communication tolerance threshold, and the physical execution delay is less than or equal to the preset physical execution tolerance threshold, the fault tracing and analysis result is output as a fault in the electronic control communication link. If the physical execution delay is greater than the physical execution tolerance threshold, and the network communication delay is less than or equal to the network communication tolerance threshold, the fault tracing and analysis result is output as a physical execution layer lag fault. If both the network communication delay and the physical execution delay are greater than their respective tolerance thresholds, the fault tracing and analysis result is output as a full-link cascade timeout fault. If the sum of the network communication tolerance threshold and the physical execution tolerance threshold is greater than the maximum theoretical delay time, and if both the network communication delay and the physical execution delay are less than or equal to their respective tolerance thresholds, and the actual delay of the entire link exceeds the maximum theoretical delay time, the fault tracing and analysis judgment result is output as an overall timing tolerance superposition fault.

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