A method and device for early warning of thermal runaway of a lithium ion battery pack

Abstract

The present application belongs to the field of battery thermal runaway early warning, and particularly relates to a method and device for early warning of thermal runaway of a lithium ion battery pack. The method first collects cell voltage, temperature and current data, determines the current working condition level according to a preset rule, matches the corresponding AC excitation strategy according to the working condition level and applies excitation, synchronously collects the excitation current and voltage response of each cell after compensation processing through a sampling chip configured with a compensation strategy, calculates and generates a set of determination parameters including impedance deviation, thermal runaway risk, voltage and temperature deviation flag bits based on the response information and in combination with a multi-dimensional impedance abnormality determination strategy, and finally determines the thermal runaway risk level of the battery pack through an evaluation algorithm according to the parameter set and reports it through CAN communication packaging. The present application realizes online and accurate sensing and early warning of the internal state of the battery under various working conditions such as static and dynamic operation.

Description

Technical Field

[0001] This invention belongs to the field of battery thermal runaway early warning, and particularly relates to an early warning method and device for thermal runaway of lithium-ion battery packs. Background Technology

[0002] Currently, battery systems for new energy vehicles are required to provide occupants with at least a five-minute escape window after a single cell experiences thermal runaway. Therefore, developing technologies that can provide timely warnings before thermal runaway occurs is of urgent practical significance for meeting safety regulations and ensuring personal safety. Existing technologies generally employ battery management systems based on multi-sensor fusion for thermal runaway monitoring. This approach collects signals such as voltage, temperature, gas, and pressure to identify and alarm on typical late-stage characteristics of thermal runaway. However, these characteristics appear after thermal runaway has progressed rapidly, resulting in delayed warnings and insufficient time for active protection and evacuation. Electrochemical impedance spectroscopy (EIS) offers a new direction for overcoming this bottleneck. This technology injects a small alternating current excitation signal into the battery and measures its voltage response to obtain impedance spectrum information reflecting the internal electrochemical state of the battery. In the early stages of thermal runaway, before the external electrothermal parameters of the battery show obvious abnormalities, its internal interface structure has already begun to deteriorate, manifested in impedance changes at specific frequencies. Therefore, this technology possesses unique potential for capturing early-stage hazards and achieving pre-emptive warnings. However, transferring electrochemical impedance spectroscopy (EIS) technology from the laboratory environment to real vehicles for online early warning faces a core technical challenge: how to achieve a fast, accurate, and interference-resistant impedance detection method under complex operating conditions such as vehicle dynamic driving or charging. Traditional impedance measurement requires the battery to be in a stable state for a long period of time, and a complete frequency scan is time-consuming, which contradicts the need for real-time online monitoring of vehicle batteries. At the same time, inherent noise and load fluctuations in the vehicle's electrical system can interfere with weak excitation and response signals, affecting measurement accuracy. Therefore, solving the problems of real-time performance, operating condition adaptability, and signal robustness of EIS technology is the key to achieving online early warning functionality. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a method and device for early warning of thermal runaway in lithium-ion battery packs.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for early warning of thermal runaway in lithium-ion battery packs, comprising:

[0006] The configured sampling chip acquires cell operating condition data, including cell voltage, temperature and current data;

[0007] Based on the cell operating condition discrimination data and the operating condition judgment rules, a judgment result with an operating condition type level flag is obtained;

[0008] Based on the determination result with the working condition type level flag, the corresponding AC incentive strategy for the current working condition level is matched and obtained from the preset AC incentive strategy library.

[0009] In response to the AC excitation strategy, the AC excitation response information after compensation is synchronously collected through a sampling chip configured with an operating condition excitation compensation strategy; the operating condition excitation compensation strategy is determined by the operating condition type level flag bit;

[0010] Based on the compensated AC excitation response information and the preset multidimensional impedance anomaly judgment strategy, a multidimensional impedance judgment parameter set is obtained. At the same time, the battery pack thermal runaway risk level is obtained according to the multidimensional impedance judgment parameter set. The multidimensional impedance judgment parameter set and the battery pack thermal runaway risk level are encapsulated and reported back via CAN communication. The multidimensional impedance judgment parameter set includes cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag.

[0011] Specifically, the operating condition type level flag includes a first numerical operating condition type level flag, a second numerical operating condition type level flag, and a third numerical operating condition type level flag; the operating condition types include ideal static operating condition, quasi-static operating condition, and dynamic operating condition; the ideal static operating condition corresponds to the first numerical operating condition type level flag; the quasi-static operating condition corresponds to the second numerical operating condition type level flag; and the dynamic operating condition corresponds to the third numerical operating condition type level flag; the AC excitation strategy includes an excitation signal generation flag, AC excitation strategy control parameters, and signal compensation parameters; the AC excitation strategy control parameters include at least the preset test frequency points and execution order for applying excitation, the excitation current amplitude corresponding to each test frequency point, the duration of application of the excitation signal at each test frequency point, and the resting interval time between excitations at two adjacent test frequency points; the excitation signal generation flag includes a high level set to 1 and a low level set to 0.

[0012] Specifically, the AC incentive strategy corresponding to the current operating condition level is matched and obtained from a preset AC incentive strategy library, including:

[0013] A preset set of AC excitation strategy parameters for all operating conditions is provided; the set of AC excitation strategy parameters for all operating conditions includes excitation frequency parameters, excitation current amplitude parameters, excitation application time parameters, frequency interval rest time parameters, and excitation compensation parameters corresponding to each operating condition.

[0014] Based on the determination result with the working condition type level flag, the value of the working condition type level flag is extracted by the flag parsing algorithm to obtain the current working condition type determination information;

[0015] If the current operating condition type determination information is an ideal static operating condition, then the excitation parameters corresponding to the ideal static operating condition are extracted from the full operating condition AC excitation strategy parameter set to generate a first AC excitation strategy control parameter set; the first AC excitation strategy control parameter set includes a first excitation frequency parameter, a first excitation current amplitude parameter, a first excitation application time parameter, and a first frequency interval static time parameter.

[0016] Specifically, the process of matching and retrieving the corresponding AC incentive strategy from a pre-defined AC incentive strategy library also includes:

[0017] If the current operating condition type determination information is a quasi-static operating condition, then the second AC excitation strategy control parameter set corresponding to the quasi-static operating condition type is extracted from the full operating condition AC excitation strategy parameter set; the second AC excitation strategy control parameter set includes the same parameters as the first AC excitation strategy control parameter set and the first compensation flag bit;

[0018] If the current operating condition type determination information is a dynamic operating condition, then the third AC excitation strategy control parameter set corresponding to the dynamic operating condition is extracted from the full operating condition AC excitation strategy parameter set; the third AC excitation strategy control parameter set includes the same parameters as the second AC excitation strategy control parameter set and a second compensation flag bit.

[0019] Specifically, the compensated AC excitation response information is synchronously acquired through a sampling chip configured with a working condition excitation compensation strategy, including:

[0020] Based on the judgment result with the working condition type level flag and the matching obtained AC excitation strategy, in response to the working condition excitation compensation strategy of the sampling chip, the signal compensation parameters in the AC excitation strategy and the compensation logic of the corresponding working condition are loaded into the control unit of the sampling chip to complete the pre-configuration of the working condition excitation compensation strategy of the sampling chip.

[0021] Based on the pre-configured working condition incentive compensation strategy, a flag bit association algorithm is used to establish a mapping relationship between the working condition type level flag bit and the execution logic of the working condition incentive compensation strategy, and to obtain the compensation strategy triggering rules; the compensation strategy triggering rules are used to clarify the enabling status of the compensation algorithm and the parameter adaptation requirements corresponding to different working condition type level flag bits.

[0022] In response to the excitation signal generation flag in the AC excitation strategy, when the excitation signal generation flag is set to 1, the sampling chip starts the synchronous acquisition mechanism to synchronously acquire the original current signal of the battery pack circuit and the original voltage response signal of each cell to obtain the initial AC excitation response dataset.

[0023] Specifically, the method further includes synchronously acquiring compensated AC excitation response information through a sampling chip configured with a working condition excitation compensation strategy, and also includes:

[0024] Based on the operating condition type level flag and the compensation strategy triggering rules, the type of compensation strategy to be executed is determined. The specific determination and execution logic includes:

[0025] If the current operating condition type level flag is the first numerical operating condition type level flag, then the initial AC excitation response dataset is directly used as the compensated AC excitation response information to be output.

[0026] If the current working condition type level flag is the second numerical working condition type level flag, then based on the first compensation flag, the preset first working condition excitation compensation algorithm is called to perform the first hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information.

[0027] If the current working condition type level flag is the third numerical working condition type level flag, based on the compensation strategy triggering rules and the second compensation flag, the preset second working condition excitation compensation algorithm is activated to perform the second hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information.

[0028] Specifically, a multidimensional impedance determination parameter set is obtained, including:

[0029] Based on the compensated AC excitation response information and the excitation frequency list in the AC excitation strategy control parameters, a data segmentation extraction algorithm is adopted to separate the cell voltage response data segment and the battery pack loop current data segment at each test frequency point from the synchronously collected time series data according to the excitation application time window corresponding to each excitation frequency, so as to obtain the frequency segment cell voltage dataset and the frequency segment loop current dataset.

[0030] Based on the frequency-segmented cell voltage dataset and the frequency-segmented loop current dataset, the Fast Fourier Transform algorithm is used to perform frequency domain analysis on each data segment to obtain the cell voltage frequency domain parameters and loop current frequency domain parameters at each test frequency point.

[0031] Based on the frequency domain parameters of the cell voltage and the frequency domain parameters of the circuit current, the AC impedance parameters of each cell at each test frequency point are calculated using a complex impedance algorithm.

[0032] Specifically, obtaining the multidimensional impedance determination parameter set also includes:

[0033] Based on the AC impedance parameters of each cell at each test frequency point, for each test frequency point, the maximum and minimum values ​​of the impedance amplitude of all cells at the corresponding frequency are removed, and then the arithmetic average algorithm is used to calculate the remaining impedance amplitude to obtain the reference average value of the cell impedance at the corresponding test frequency point.

[0034] Based on the AC impedance amplitude of each cell and the reference average impedance of the cell at the corresponding test frequency point, calculate the relative impedance deviation ratio of each cell.

[0035] The impedance relative deviation ratio is compared with the preset impedance deviation allowable threshold. If the impedance relative deviation ratio is greater than the impedance deviation allowable threshold, the impedance deviation flag of the corresponding cell is set to an effective state; otherwise, it is set to an invalid state. The impedance deviation flag of each cell is obtained.

[0036] Based on the list of thermal runaway strongly correlated characteristic frequencies pre-stored in the main chip, the AC impedance amplitude of each cell at the corresponding characteristic frequency is extracted from the AC impedance parameters of each cell at each test frequency point, and used as the thermal runaway correlated impedance parameter of each cell.

[0037] The thermal runaway-related impedance parameter is compared with the preset characteristic frequency impedance alarm threshold. If the thermal runaway-related impedance parameter exceeds the characteristic frequency impedance alarm threshold, the thermal runaway risk flag position of the corresponding cell is set to an effective state; otherwise, it is set to an invalid state. The thermal runaway risk flag position of each cell is obtained.

[0038] Specifically, obtaining the multidimensional impedance determination parameter set also includes:

[0039] Based on the real-time voltage data of each cell obtained from the cell condition discrimination data, the voltage characteristic values ​​of all cells are sorted and eliminated using a sorting and elimination algorithm to remove the maximum and minimum values, and then the average value of the remaining values ​​is calculated to obtain the reference average value of cell voltage characteristics; the deviation ratio of each cell voltage characteristic value relative to the reference average value of voltage characteristics is calculated and compared with a preset voltage deviation allowable threshold. If the voltage deviation exceeds the voltage deviation allowable threshold, the voltage deviation flag of the corresponding cell is set to a valid state; otherwise, it is set to an invalid state, and the voltage deviation flag of each cell is obtained.

[0040] Based on the real-time temperature data of each cell obtained from the cell condition discrimination data, the temperature characteristic values ​​of all cells are sorted and eliminated by an algorithm to remove the maximum and minimum values, and then the average value of the remaining values ​​is calculated to obtain the reference average value of cell temperature characteristics.

[0041] Calculate the deviation ratio of each cell's temperature characteristic value relative to the average temperature characteristic reference value, and compare it with a preset temperature deviation allowable threshold. If the deviation exceeds the temperature deviation allowable threshold, set the temperature deviation flag of the corresponding cell to an effective state; otherwise, set it to an invalid state. Obtain the temperature deviation flag of each cell.

[0042] Based on the impedance deviation flag, thermal runaway risk flag, voltage deviation flag, and temperature deviation flag corresponding to each cell, a data fusion encapsulation algorithm is used to combine the four flags into a complete multidimensional state vector according to the cell's unique identifier. The state vectors of all cells are then summarized to obtain the multidimensional impedance judgment parameter set.

[0043] An early warning device for thermal runaway of a lithium-ion battery pack, comprising:

[0044] The data acquisition module obtains cell operating condition data, including cell voltage, temperature and current data, through a configured sampling chip.

[0045] The operating condition determination module obtains a determination result with an operating condition type level flag based on the cell operating condition discrimination data and operating condition determination rules; the operating condition types include ideal static operating condition, quasi-static operating condition and dynamic operating condition.

[0046] The incentive matching module, based on the judgment result with the working condition type level flag, matches and obtains the AC incentive strategy corresponding to the current working condition level from the preset AC incentive strategy library;

[0047] The excitation response module responds to the AC excitation strategy and synchronously collects the compensated AC excitation response information through a sampling chip configured with a working condition excitation compensation strategy; the working condition excitation compensation strategy is determined by the working condition type level flag bit.

[0048] The impedance determination module obtains a multi-dimensional impedance determination parameter set based on the compensated AC excitation response information and a preset multi-dimensional impedance anomaly determination strategy. At the same time, it obtains the battery pack thermal runaway risk level based on the multi-dimensional impedance determination parameter set and encapsulates and reports the multi-dimensional impedance determination parameter set and the battery pack thermal runaway risk level through CAN communication.

[0049] The multidimensional impedance judgment parameter set includes cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This invention addresses the shortcomings of existing technologies by employing electrochemical impedance spectroscopy (EIS) technology and innovatively proposing a multi-condition adaptive online measurement scheme. The system achieves early and sensitive detection of battery internal state deterioration, capturing latent thermal runaway precursors that traditional voltage and temperature monitoring cannot detect, thus significantly advancing the warning time and buying valuable time for personnel evacuation and proactive system protection. This method overcomes the dependence of traditional EIS on the battery's static state through dynamic matching of three-level operating conditions and corresponding excitation strategies, achieving reliable measurements under various actual operating conditions such as vehicle operation and charging, significantly improving the practicality and online monitoring capabilities of the technology. Simultaneously, the introduction of a layered and progressive signal compensation mechanism effectively suppresses interference from the complex electromagnetic environment of the vehicle, ensuring the measurement accuracy of weak impedance signals. By integrating multi-dimensional criteria such as impedance consistency deviation, characteristic frequency risk threshold, and voltage and temperature consistency for comprehensive decision-making, the system significantly reduces false alarm and false negative rates, achieving precise location of faulty cells and quantitative assessment of risk levels. Ultimately, real-time reporting via a standardized CAN communication interface fulfills the functional requirements for early warning of thermal runaway, providing a complete and effective technical solution for improving the active safety and reliability of power battery systems in new energy vehicles. Attached Figure Description

[0052] Figure 1 This is a flowchart of an early warning method for thermal runaway in a lithium-ion battery pack according to the present invention.

[0053] Figure 2 This is a module diagram of an early warning device for thermal runaway of a lithium-ion battery pack according to the present invention;

[0054] Figure 3 This is a logic structure diagram of an electronic device according to the present invention;

[0055] Figure 4 This is a signal and data transmission logic diagram of an AC generator circuit according to the present invention. Detailed Implementation

[0056] Example 1

[0057] Please see Figure 1 The present invention provides an embodiment of an early warning method for thermal runaway in lithium-ion battery packs, used to achieve real-time monitoring and early warning of thermal runaway events in lithium-ion battery packs, comprising the following steps:

[0058] S1. Obtain cell operating condition judgment data through the configured sampling chip, including cell voltage, temperature and current data;

[0059] S2. Based on the cell operating condition discrimination data and operating condition judgment rules, a judgment result with an operating condition type level flag is obtained; the operating condition type level flag includes a first numerical operating condition type level flag, a second numerical operating condition type level flag, and a third numerical operating condition type level flag; the operating condition types include ideal static operating condition, quasi-static operating condition, and dynamic operating condition; the ideal static operating condition corresponds to the first numerical operating condition type level flag; the quasi-static operating condition corresponds to the second numerical operating condition type level flag; the dynamic operating condition corresponds to the third numerical operating condition type level flag; it should be further noted that the flags in this embodiment are specifically set on-site by those skilled in the art according to the specific application in the actual scenario, and will not be described in detail here;

[0060] It should be further explained that the process of obtaining the determination result with the working condition type level flag bit in this embodiment includes:

[0061] S201. Based on the configuration of the sampling chip, the battery cell condition discrimination data is continuously collected. The built-in continuous time-series caching mechanism is started. The battery cell voltage data, battery cell temperature data and battery cell current data are associated and stored according to a fixed sampling period to obtain the continuous time-series original dataset of battery cell condition.

[0062] It should be further explained that the specific implementation steps for associating and storing cell voltage data, cell temperature data, and cell current data according to a fixed sampling period in this embodiment include:

[0063] Based on preset fixed sampling period parameters, a real-time clock or timer interrupt triggering mechanism is used to periodically start the multi-channel analog-to-digital conversion of the sampling chip, synchronously collect the voltage signal of each cell, the temperature signal of each cell, and the current signal of the battery pack's total circuit, and obtain the original sampled values ​​at discrete time points.

[0064] Based on the high-precision timestamp of each sampling time point, a data structure encapsulation algorithm with timestamp is adopted to bind and encapsulate the voltage data of all cells, the temperature data of all cells, and the circuit current data of the battery pack collected at the same time with the corresponding sampling timestamp to form a complete data frame.

[0065] Based on a preset continuous time buffer, a first-in-first-out queue management algorithm is used to store the timestamped data frames generated in the second step into the continuous time buffer in the order of sampling time, thereby realizing the time-series storage of the original sampled data.

[0066] Based on the capacity management mechanism of the cache, a circular queue index management algorithm is adopted to dynamically manage the read and write pointers of the cache. When the cache is full, the oldest historical data is automatically overwritten to ensure that the latest continuous time period of working condition data is always retained in the cache.

[0067] Based on preset data output or analysis trigger conditions, a data block extraction algorithm is used to extract a specified number of continuous data frames from the continuous time buffer according to the time range, and obtain a structured raw dataset of continuous time-series battery cell operating conditions containing voltage, temperature, current and time sequence information.

[0068] S202. Based on the original data set of battery cell operating conditions in continuous time series, the moving average filtering algorithm is used to perform noise suppression processing on the original data of battery cell voltage, battery cell temperature and battery cell current respectively, to obtain the noise-reduced continuous time series battery cell voltage data, noise-reduced continuous time series battery cell temperature data and noise-reduced continuous time series battery cell current data.

[0069] S203. Based on the noise-reduced continuous time-series cell current data, the steady-state amplitude feature of the current signal is extracted using a current steady-state amplitude extraction algorithm to obtain the cell current feature value.

[0070] It should be further explained that this embodiment uses a current steady-state amplitude extraction algorithm to extract the steady-state amplitude characteristics of the current signal. The specific implementation steps include:

[0071] Based on the preset steady-state analysis time window length, a sliding window management algorithm is used to divide the continuous time-series cell current data into a series of continuous and partially overlapping time-series data segments to obtain the current data sub-segment sequence to be analyzed.

[0072] For each current data segment, the root mean square (RMS) value is calculated using a root mean square (RMS) value algorithm to obtain the RMS value of the current signal within that segment, which is then used as the current intensity characterization value for that time window.

[0073] Based on a preset steady-state discrimination threshold, an algorithm for determining the rate of change of current intensity in adjacent windows is used to identify the current in a steady-state interval. Specifically, the rate of change of the current intensity characterization value in adjacent windows in the sliding window sequence is calculated. If the absolute value of the rate of change is continuously lower than the steady-state discrimination threshold and the duration exceeds the preset minimum steady-state duration, then the interval is determined to be a current steady-state interval.

[0074] Based on all identified steady-state current ranges, the RANSAC algorithm or the arithmetic mean algorithm after removing outliers is used to extract the steady-state amplitude from the original current data corresponding to each steady-state range, and calculate its statistical average value. This average value is then used as the final cell current characteristic value output.

[0075] S204. Based on the noise-reduced continuous time-series cell voltage data, calculate the difference between the maximum and minimum voltage values ​​within the continuous time sequence, and obtain the cell voltage characteristic values ​​corresponding to all cells.

[0076] S205. Based on the noise-reduced continuous time-series cell temperature data, calculate the difference between the maximum and minimum temperature values ​​within the continuous time sequence, and obtain the cell temperature characteristic values ​​corresponding to all cells.

[0077] S206. Based on the cell current characteristic value, the cell voltage characteristic value of all cells, and the cell temperature characteristic value of all cells, combined with the preset ideal static working condition judgment rules, determine whether the following conditions are met simultaneously: the cell current characteristic value is less than or equal to the first current threshold, the cell voltage characteristic value of all cells is less than or equal to the first voltage threshold, and the cell temperature characteristic value of all cells is less than or equal to the first temperature threshold. If all conditions are met, the current working condition is determined to be an ideal static working condition, and a judgment result with the first numerical working condition type level flag is obtained.

[0078] S207. If not all the ideal static condition determination conditions are met, then determine whether the following conditions are simultaneously met: the cell current characteristic value is less than or equal to the second current threshold and greater than the first current threshold; the cell voltage characteristic value of all cells is less than or equal to the second voltage threshold; and the cell temperature characteristic value of all cells is less than or equal to the second temperature threshold. If all conditions are met, then the current condition is determined to be a quasi-static condition, and a determination result with the second numerical condition type level flag is obtained. It should be further explained that the setting process of the first current threshold, the first voltage threshold, and the first temperature threshold in this embodiment includes:

[0079] Based on the nominal type and rated parameters of the battery cell under test, a constant temperature static test method was adopted under standard laboratory ambient temperature to keep the battery cell in a no-load state for a preset time. At the same time, high-precision data acquisition equipment was used to record the current data, voltage data and temperature data of multiple battery cells simultaneously to obtain the original sample dataset under standard static conditions.

[0080] Based on the acquired original sample dataset, the sliding window statistical analysis method was used to calculate the absolute average current, the difference between the maximum and minimum voltage values, and the difference between the maximum and minimum temperature values ​​for each group of data within a continuous time window, thereby obtaining statistical sequences of multiple groups of steady-state current values, voltage fluctuation values, and temperature fluctuation values.

[0081] Based on the statistical sequence of the steady-state current value, voltage fluctuation value, and temperature fluctuation value, a percentile estimation algorithm after removing extreme values ​​is used, such as calculating the 95th percentile, to determine their statistical upper limit values ​​as preliminary current reference thresholds, voltage reference thresholds, and temperature reference thresholds.

[0082] Based on the preliminary current reference threshold, voltage reference threshold, and temperature reference threshold, and combined with a preset safety redundancy coefficient, wherein the safety redundancy coefficient ranges from 1.1 to 1.5, the preliminary thresholds are amplified by multiplication to obtain initial current threshold, initial voltage threshold, and initial temperature threshold that include safety margins.

[0083] Based on the initial current threshold, initial voltage threshold, and initial temperature threshold, multiple rounds of testing were conducted under various preset environmental conditions (including at least combinations of high and low temperatures and different SOC states) using a threshold validity verification experiment method. By statistically analyzing the proportion of misjudgment cases where the current, voltage, and temperature parameters exceeded the initial thresholds in actual working conditions that were determined to be ideal static conditions, the misjudgment rate data for each threshold was obtained.

[0084] Based on the false positive rate data and the preset allowable false positive rate target, the safety redundancy coefficient or the threshold value is dynamically adjusted using the gradient descent method or the binary search method, and the partial verification experiment is repeated until the measured false positive rate of each parameter meets the judgment accuracy requirements. Finally, the preset first current threshold, first voltage threshold and first temperature threshold are obtained and fixed.

[0085] S208. If neither the ideal static condition determination condition nor the quasi-static condition determination condition is met, based on the cell current characteristic value, the cell voltage characteristic value of all cells, and the cell temperature characteristic value of all cells, the current condition is determined to be a dynamic condition, and a determination result with a third numerical condition type level flag is obtained.

[0086] For example, this embodiment configures a sampling chip to synchronously collect cell voltage, temperature, and total circuit current data at a fixed period of 10 milliseconds. Each sampling point is timestamped and encapsulated into a data frame, which is then sequentially stored in a circular buffer queue with a capacity of 5000 frames to achieve time-series storage. Subsequently, the raw data undergoes moving average filtering, with noise suppression achieved using a 5-point window for current, a 10-point window for voltage, and a 20-point window for temperature. Steady-state features are extracted from the filtered current data. Data segments are divided using a 2-second analysis window and 50% overlap. The root mean square (RMS) value of the current in each window is calculated. Steady-state intervals are identified by determining whether the rate of change of the RMS value between adjacent windows is consistently below 0.1 amperes per second for more than 5 seconds. The RANSAC algorithm is used to extract the amplitude from the steady-state intervals and calculate the average value as the current feature value. Simultaneously, the difference between the maximum and minimum values ​​of voltage and temperature within the 2-second window is calculated as their respective feature values. Finally, the operating condition level is determined based on the preset dual-layer threshold. The ideal static operating condition requires the current characteristic value not to exceed 0.05 Amperes, the voltage difference of each cell not to exceed 0.005 V, and the temperature difference of each cell not to exceed 0.2 degrees Celsius. The quasi-static operating condition requires the current characteristic value not to exceed 1.0 Amperes, the voltage difference of each cell not to exceed 0.02 V, and the temperature difference of each cell not to exceed 1.0 degrees Celsius. If the above conditions are not met, it is determined to be a dynamic operating condition, providing a basis for graded decision-making for battery management.

[0087] S3. Based on the determination result with the working condition type level flag, match and obtain the AC incentive strategy corresponding to the current working condition level from the preset AC incentive strategy library.

[0088] It should be further explained that this embodiment matches and obtains the AC excitation strategy corresponding to the current operating condition level from a preset AC excitation strategy library, including:

[0089] S301. A preset set of AC excitation strategy parameters for all operating conditions; the set of AC excitation strategy parameters for all operating conditions includes excitation frequency parameters, excitation current amplitude parameters, excitation application time parameters, frequency interval rest time parameters, and excitation compensation parameters corresponding to each operating condition. It should be further explained that the parameters included in the set of AC excitation strategy parameters for all operating conditions in this embodiment are used to accurately control and adapt the electrochemical impedance spectroscopy measurement process under different operating conditions. Among them, the excitation frequency parameter defines the test frequency points for applying AC excitation and their scanning order, used to obtain the impedance spectrum characteristics of the battery at different frequencies. The excitation current amplitude parameter determines the intensity of the excitation signal, aiming to ensure that the excitation signal has a sufficient signal-to-noise ratio. The excitation application time parameter sets the duration of the excitation signal at each frequency point, used to ensure that the response signal reaches stability. The frequency interval rest time parameter specifies the no-excitation interval between adjacent frequency excitations, used to eliminate the transient influence of the previous frequency excitation. The excitation compensation parameter defines the type and intensity of the signal processing and compensation algorithms to be enabled under different operating conditions (especially non-resting operating conditions), used to resist or eliminate external operating condition interference, ensuring the accuracy and reliability of the impedance measurement results.

[0090] S302. Based on the judgment result with the working condition type level flag, extract the value of the working condition type level flag through the flag parsing algorithm to obtain the current working condition type judgment information.

[0091] S303. If the current operating condition type determination information is an ideal static operating condition, then extract the excitation parameters corresponding to the ideal static operating condition from the full operating condition AC excitation strategy parameter set to generate a first AC excitation strategy control parameter set; the first AC excitation strategy control parameter set includes a first excitation frequency parameter, a first excitation current amplitude parameter, a first excitation application time parameter, and a first frequency interval static time parameter.

[0092] In this embodiment, the first AC excitation strategy control parameter set is used to precisely control a complete battery electrochemical impedance spectroscopy scan test: the first excitation frequency parameter specifies the specific AC signal frequency used in this scan test; the first excitation current amplitude parameter sets the peak value of the sinusoidal AC current injected into the battery at this frequency; the first excitation application time parameter determines the duration for which the excitation signal is continuously applied at this frequency point to ensure that the system reaches a steady-state response; and the first frequency interval rest time parameter specifies the waiting time for the system to stop excitation and remain resting before switching from one frequency test point to the next, in order to allow the polarization state inside the battery to fully relax, thereby avoiding mutual interference between different frequency test points and ensuring the accuracy and independence of the impedance measurement results.

[0093] S304. If the current operating condition type determination information is a quasi-static operating condition, then extract the second AC excitation strategy control parameter set corresponding to the quasi-static operating condition type from the full operating condition AC excitation strategy parameter set; the second AC excitation strategy control parameter set includes the same parameters as the first AC excitation strategy control parameter set and the first compensation flag bit;

[0094] S305. If the current working condition type determination information is a dynamic working condition, then extract the third AC excitation strategy control parameter set corresponding to the dynamic working condition from the full working condition AC excitation strategy parameter set; it should be further explained that the third AC excitation strategy control parameter set in this embodiment includes the same parameters as the second AC excitation strategy control parameter set and the second compensation flag bit.

[0095] For example, in the quasi-static condition, the specific settings of the second AC excitation strategy control parameter set in this embodiment are as follows: the excitation frequency parameter is set to a sequence of m discrete frequency points from 1000 Hz to 0.01 Hz in logarithmic coordinates; the excitation current amplitude parameter is fixed to the current value corresponding to 0.05 times the rated capacity of the battery, for example, for a 100 Ah battery pack, this value is set to a peak value of 5 Amperes; the excitation application time parameter is specified as the application time for each frequency point is 10 times the corresponding period of that frequency, but the upper limit is set to 300 seconds and the lower limit is 0.3 seconds, that is, for a 100-second period of 0.01 Hz, the application time is set to the upper limit of 300 seconds, and for a 0.001-second period of 1000 Hz, the application time is set to the lower limit of 0.3 seconds; the frequency interval static time parameter is uniformly set to half of the excitation application time; the first compensation flag is set to the enabled mode and is specifically associated with the DC bias compensation algorithm, and its compensation intensity coefficient is set to 0.2.

[0096] S4. The AC excitation strategy is responded to on the configured AC signal generation circuit, and the compensated AC excitation response information is synchronously collected through the sampling chip configured with the operating condition excitation compensation strategy. The compensated AC excitation response information includes the current of the battery pack circuit and the voltage response of each configured cell, which is sent to the configured main chip. It should be further noted that the operating condition excitation compensation strategy in this embodiment is determined by the operating condition type level flag. The AC excitation strategy includes the excitation signal generation flag, amplitude-frequency parameter information, and signal compensation parameters. It should be further noted that the amplitude-frequency parameter information in this embodiment includes at least the preset test frequency points and execution order of the excitation, the excitation current amplitude corresponding to each test frequency point, the continuous application time of the excitation signal at each test frequency point, and the resting interval time between the excitations of two adjacent test frequency points. The multi-dimensional impedance judgment parameter set includes the cell impedance deviation flag, the cell thermal runaway risk flag, the cell voltage deviation flag, and the cell temperature deviation flag. For example, in this embodiment, the test frequency points are applied with an AC current excitation signal of 0.2 A in the following frequency order, with each frequency signal applied for 2 seconds and a rest period of 2 seconds between two frequency excitations; the frequency order includes: 10kHz, 8kHz, 5kHz, 1kHz, 500Hz, 200Hz, 100Hz, 50Hz, 10Hz, 1Hz, 0.1Hz.

[0097] It should be further explained that this embodiment synchronously collects the compensated AC excitation response information through a sampling chip configured with a working condition excitation compensation strategy, including:

[0098] S401. Based on the judgment result with the working condition type level flag and the matching obtained AC excitation strategy, in response to the working condition excitation compensation strategy of the sampling chip, the signal compensation parameters in the AC excitation strategy and the compensation logic of the corresponding working condition are loaded into the control unit of the sampling chip to complete the pre-configuration of the working condition excitation compensation strategy of the sampling chip.

[0099] S402. Based on the pre-configured operating condition excitation compensation strategy, a flag bit association algorithm is used to establish a mapping relationship between the operating condition type level flag bit and the execution logic of the operating condition excitation compensation strategy, and to obtain the compensation strategy triggering rules; the compensation strategy triggering rules are used to clarify the enabling status of the compensation algorithm and the parameter adaptation requirements corresponding to different operating condition type level flag bits, specifically:

[0100] Based on the operating condition level flag and the preset compensation strategy mapping table, a condition matching algorithm is used to establish the correspondence between the values ​​of different operating condition level flags and the specific compensation algorithm activation instructions, so as to obtain the compensation algorithm activation status. At the same time, based on the operating condition level flag and the preset compensation parameter adaptation rule library, a parameter query algorithm is used to match and obtain the specific parameter set required for the operation of the compensation algorithm under the current operating condition, so as to obtain the parameter adaptation requirements. Finally, the compensation algorithm activation status and the parameter adaptation requirements are integrated to generate a complete compensation strategy triggering rule for guiding the specific execution of the signal compensation link.

[0101] S403. In response to the excitation signal generation flag bit in the AC excitation strategy, when the excitation signal generation flag bit is set to 1, the sampling chip starts the synchronous acquisition mechanism to synchronously acquire the original current signal of the battery pack circuit and the original voltage response signal of each cell to obtain the initial AC excitation response dataset.

[0102] S404. Based on the operating condition type level flag and compensation strategy triggering rules, determine the type of compensation strategy to be executed. The specific determination and execution logic includes:

[0103] S4041. If the current working condition type level flag is the first numerical working condition type level flag, then the initial AC excitation response dataset is directly used as the compensated AC excitation response information to be output.

[0104] S4042. If the current working condition type level flag is the second numerical working condition type level flag, then based on the first compensation flag, the preset first working condition excitation compensation algorithm is called to perform the first hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information.

[0105] It should be further explained that this embodiment performs a first hierarchical progressive compensation process on the initial AC stimulus response dataset, including:

[0106] A101. Based on the preset DC component cutoff frequency in the signal compensation parameters, Butterworth high-pass digital filter or Chebyshev high-pass digital filter are used to filter the original current signal and the original voltage response signal respectively to obtain the initial AC current component and the initial AC voltage component.

[0107] A102. Based on a preset DC bias allowable threshold, the DC bias is verified for the initial AC current component and the initial AC voltage component by calculating the arithmetic mean of the signal.

[0108] A103. If the absolute value of the DC bias obtained by the verification calculation is less than or equal to the DC bias allowable threshold, then the initial AC current component is determined to be a valid AC current component, and the initial AC voltage component is determined to be a valid AC voltage component.

[0109] A104. If the absolute value of the DC bias obtained by the verification calculation is greater than the allowable threshold of the DC bias, then the cutoff frequency of the Butterworth high-pass digital filter or the Chebyshev high-pass digital filter is adjusted according to the absolute value of the DC bias, and the filtering process and DC bias verification steps are re-executed based on the adjusted cutoff frequency until the effective AC current component and the effective AC voltage component are obtained.

[0110] A105. Based on a preset first-order or second-order equivalent circuit model of the battery, the pre-stored model parameters are called, and combined with the time-varying cell operating current data, the dynamic polarization voltage time-series curve is calculated using the Romberg numerical integration method or the trapezoidal numerical integration method. The model parameters include ohmic resistance parameters, polarization resistance parameters, and polarization capacitance parameters. The motivation for this step is that when the battery is in dynamic operating conditions, its internal polarization process will superimpose a slowly changing "dynamic polarization voltage" caused by the operating current. This voltage component will seriously mix in and interfere with the small AC excitation voltage response signal, leading to distortion of the subsequently extracted impedance information. The principle is that the polarization behavior of the battery can be mathematically described using a first-order or second-order equivalent circuit model. By calling pre-stored model parameters and combining them with real-time acquired time-varying operating current data, the time-domain response equation of the model can be solved using the Runge-Kutta method or the trapezoidal method. This allows for the simulation and calculation of the precise curve of the dynamic polarization voltage changing over time due to the operating current history. This provides a quantitative basis for accurately subtracting the interference component from the total voltage response, ensuring the accuracy of impedance measurements under non-static conditions. It should be further noted that the first-order equivalent circuit model of the battery in this embodiment is preferably the Rint model or the Thevenin model, and the second-order equivalent circuit model is preferably a dual RC or PNGV model. First-order or second-order equivalent circuit models of batteries abstract the complex electrochemical processes inside the battery into a combination of ideal circuit elements such as resistors and capacitors. For example, in the first-order model, the ohmic internal resistance and a single RC parallel circuit are used to characterize the instantaneous voltage drop and polarization effect, respectively. In the second-order model, two RC parallel circuits are used to further distinguish between electrochemical polarization and concentration polarization, thereby describing the dynamic external characteristics of the battery (such as terminal voltage response) in a calculable way. They have become the core basic models for battery state estimation (such as SOC, SOH), power prediction and system simulation.

[0111] A106. Based on the timestamp of the effective AC voltage component signal, a linear interpolation algorithm is used to synchronize the dynamic polarization voltage time series curve with the effective AC voltage component on the time axis, and the instantaneous value of the synchronized dynamic polarization voltage is subtracted point by point from the instantaneous value of the effective AC voltage component to obtain the voltage response signal after initial compensation.

[0112] A107. Based on a preset compensation verification threshold, the fluctuation of the initially compensated voltage response signal within a complete excitation cycle is quantitatively verified using either the signal standard deviation calculation method or the signal variance calculation method. The setting of the compensation verification threshold in this embodiment needs to be determined based on the system's minimum requirements for the stability of the compensated signal and the baseline fluctuation level under ideal static conditions. Specifically, the method is as follows: First, under ideal static conditions, a standard AC excitation is applied to a battery in known good condition, and a large number of compensated voltage response signal samples are collected. Second, the fluctuation of all samples within a complete excitation cycle is statistically analyzed using the signal standard deviation calculation method, and its upper limit is determined through percentile statistics (e.g., using the 95th percentile) as the baseline fluctuation threshold. Finally, based on a preset system safety factor, this baseline fluctuation threshold is appropriately amplified to accommodate individual battery differences and inherent noise in the measurement system, thereby ultimately setting it as the compensation verification threshold. This threshold is used to determine whether the compensation effect under dynamic or quasi-static conditions reaches a signal quality level comparable to that under ideal static conditions.

[0113] A108. If the fluctuation metric value obtained by the verification calculation is less than or equal to the compensation verification threshold, then the initial compensated voltage response signal is determined to be the final compensated voltage response signal.

[0114] A109. If the fluctuation metric value obtained by the verification calculation is greater than the compensation verification threshold, the parameters of the battery first-order equivalent circuit model or the battery second-order equivalent circuit model are re-identified and optimized using the recursive least squares method or the extended Kalman filter algorithm. Based on the optimized model parameters, the calculation of the dynamic polarization voltage timing curve, the time axis synchronization matching and subtraction steps are re-executed until the final compensated voltage response signal is obtained.

[0115] A110. Integrate the final compensated voltage response signal with the effective AC current component into a synchronous data sequence with the same sampling time point to obtain the compensated AC excitation response information for subsequent Fourier transform calculation and impedance spectrum calculation.

[0116] In a specific example of a quasi-static operating condition, the first layered progressive compensation process is executed as follows: The system first preprocesses the original current and voltage signals, using a fourth-order Butterworth high-pass digital filter with a cutoff frequency of 0.1 Hz to filter out the DC component, obtaining the initial AC components. Subsequently, the algorithm calculates the arithmetic mean of these initial AC components for DC bias verification, with the allowable threshold strictly set at 0.005 amperes for current and 0.005 volts for voltage. If the verification fails, the filter's cutoff frequency is increased in 0.01 Hz steps, and the filtering and verification are repeated until the threshold requirements are met, thereby obtaining pure and effective AC current and voltage components.

[0117] Secondly, the core compensation step is initiated. The system calls the pre-stored parameters of the battery's first-order equivalent circuit model, including an internal resistance of 0.8 milliohms, a polarization resistance of 2.5 milliohms, and a polarization capacitance of 1800 farads. Combining the real-time acquired cell operating current time-series data, the trapezoidal numerical integration method is used to solve the time-domain response of the model, accurately calculating the dynamic polarization voltage time-series curve caused by the operating current history. Then, a linear interpolation algorithm is used to align this dynamic polarization voltage curve with the effective AC voltage signal on the time axis, and the corresponding dynamic polarization voltage value is subtracted point by point from the voltage signal to obtain the initial compensated voltage response signal.

[0118] Third, to verify the compensation effect, the system calculates the standard deviation of the initially compensated voltage signal over a complete excitation cycle and compares it with a preset compensation verification threshold of 0.002 volts. If the fluctuation standard deviation exceeds this threshold, the compensation is deemed insufficient, and a recursive least squares parameter identification algorithm is initiated. With a forgetting factor of 0.1, the ohmic internal resistance, polarization resistance, and polarization capacitance parameters of the battery model are re-identified and optimized online. Using the optimized new model parameters, the system recalculates the dynamic polarization voltage curve and performs a subtraction operation. This process is iterated until the standard deviation of the compensated voltage signal drops below 0.002 volts, ultimately obtaining a stable final compensated voltage response signal.

[0119] Finally, the system synchronizes and integrates the verified final compensated voltage response signal with the previously acquired effective AC current component according to the sampling timestamp with millisecond precision, and encapsulates it into a data frame that conforms to the predetermined format, as the output of compensated AC excitation response information that can be used for high-precision impedance spectrum calculation.

[0120] S4043. If the current working condition type level flag is the third numerical working condition type level flag, based on the compensation strategy triggering rule and the second compensation flag, the preset second working condition excitation compensation algorithm is activated to perform the second hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information.

[0121] It should be further explained that this embodiment performs a second hierarchical progressive compensation process on the initial AC stimulus-response dataset, including:

[0122] B101. Based on the high-frequency band filtering threshold in the signal compensation parameters, a fast Fourier transform spectrum analysis algorithm is used to perform real-time spectrum analysis on the original current signal and the original voltage response signal, converting the time-domain signal into a frequency-domain signal to obtain the frequency distribution characteristics of the original signal, including all frequency components contained in the signal and the amplitude and energy ratio corresponding to each frequency component. In this embodiment, the setting of the high-frequency band filtering threshold needs to be determined based on the battery impedance spectrum characteristics and the spectrum distribution characteristics of interference noise under vehicle dynamic conditions, through experimental calibration and signal-to-noise ratio analysis. The specific method is as follows: First, under typical dynamic conditions (such as vehicle uniform motion), Under conditions of high-speed driving or charging, the battery pack's base voltage noise signal is collected when there is no excitation, and its power spectral density distribution is obtained using spectral analysis. Secondly, under static conditions, the battery's reference impedance spectrum in a wide frequency range is measured to identify the typical low-frequency bands distributed in the characteristic frequencies sensitive to thermal runaway. Finally, by comparing and analyzing the noise spectrum and the reference impedance spectrum, a specific frequency point that can effectively avoid the strong noise low-frequency band while being as close as possible to the boundary of the sensitive frequency band is selected and set as the high-frequency band screening threshold to ensure that the selected high-frequency band signal has sufficient signal-to-noise ratio under dynamic conditions and can indirectly reflect some internal state changes.

[0123] B102. Based on the frequency distribution characteristics of the original signal and the high-frequency band filtering threshold, the bandpass filter parameters are configured using the infinite impulse response adaptive frequency band filtering algorithm. The specific configuration process is as follows: based on the high-frequency band filtering threshold, the lower passband frequency and the upper passband frequency of the infinite impulse response bandpass filter are determined.

[0124] B103. Based on the frequency distribution characteristics, calculate the proportion of the amplitude of the high-frequency components above the lower passband frequency to the total signal amplitude, and obtain the high-frequency amplitude proportion.

[0125] B104. Based on a preset filter order adjustment rule, determine the filter order of the infinite impulse response bandpass filter according to the proportion of the high-frequency amplitude; the filter order adjustment rule is as follows:

[0126] When the proportion of high-frequency amplitude is lower than the first preset proportion threshold, the first high filtering order is used; when the proportion of high-frequency amplitude is higher than the first preset proportion threshold but lower than the second preset proportion threshold, the second medium filtering order is used; when the proportion of high-frequency amplitude is higher than the second preset proportion threshold, the third low filtering order is used; wherein, the first high filtering order is higher than the second medium filtering order, and the second medium filtering order is higher than the third low filtering order.

[0127] Based on the determined lower passband frequency, upper passband frequency, and filter order, configure and generate the corresponding infinite impulse response bandpass filter parameters;

[0128] Based on the configured parameters of the infinite impulse response bandpass filter, the bandpass filter is instantiated using the Butterworth or Chebyshev filter design method.

[0129] Based on the instantiated infinite impulse response bandpass filter, the original current signal and the original voltage response signal are digitally filtered to filter out low-frequency interference signals below the lower passband frequency and high-frequency noise above the upper passband frequency, while retaining the target high-frequency signal components between the lower passband frequency and the upper passband frequency.

[0130] Based on the results of the digital filtering process, an initial high-frequency current signal and an initial high-frequency voltage signal containing the target high-frequency band signal components are obtained.

[0131] B105. Based on a preset low-frequency residual judgment threshold, a power spectral density analysis algorithm is used to perform low-frequency residual verification on the initial high-frequency current signal and the initial high-frequency voltage signal; the specific implementation process is as follows:

[0132] Based on the initial high-frequency current signal and the initial high-frequency voltage signal, a power spectral density analysis algorithm is used to calculate the ratio of the energy of the low-frequency components below the high-frequency band screening threshold in the initial high-frequency current signal to the total energy of the signal, thereby obtaining the proportion of low-frequency energy in the current. Similarly, the ratio of the energy of the low-frequency components below the high-frequency band screening threshold in the initial high-frequency voltage signal to the total energy of the signal is calculated, thereby obtaining the proportion of low-frequency energy in the voltage.

[0133] Based on a preset low-frequency residual determination threshold, the proportion of low-frequency energy of current and the proportion of low-frequency energy of voltage are compared with the low-frequency residual determination threshold respectively.

[0134] If the proportion of low-frequency energy of the current is less than or equal to the low-frequency residual judgment threshold, and the proportion of low-frequency energy of the voltage is less than or equal to the low-frequency residual judgment threshold, then the frequency band filtering result meets the standard, and the initial high-frequency current signal is directly obtained as a valid high-frequency current signal, and the initial high-frequency voltage signal is obtained as a valid high-frequency voltage signal.

[0135] If the proportion of low-frequency energy in the current is greater than the low-frequency residual judgment threshold, or the proportion of low-frequency energy in the voltage is greater than the low-frequency residual judgment threshold, then the parameters of the infinite impulse response bandpass filter are re-optimized based on the excess ratio; the excess ratio is the relative magnitude by which the current low-frequency energy proportion exceeds the low-frequency residual judgment threshold.

[0136] Based on the re-optimized parameters of the infinite impulse response bandpass filter, the filter state is updated, and the digital filtering process, power spectral density analysis calculation, and low-frequency energy ratio comparison steps are re-executed until both the low-frequency energy ratio of the current and the low-frequency energy ratio of the voltage are less than or equal to the low-frequency residual judgment threshold. Finally, the effective high-frequency current signal and the effective high-frequency voltage signal are obtained.

[0137] B106. Based on the amplitude attenuation characteristics of the excitation signal under dynamic operating conditions, the root mean square error algorithm is used to analyze the amplitude fluctuation of the effective high-frequency current signal and the effective high-frequency voltage signal to obtain real-time amplitude fluctuation parameters; specifically:

[0138] Based on the effective high-frequency current signal and the effective high-frequency voltage signal, a sliding time window of preset length is used to segment and extract the time-series amplitude data of the effective high-frequency current signal and the effective high-frequency voltage signal respectively, so as to obtain the current amplitude data segment and the voltage amplitude data segment within the current analysis period.

[0139] Based on the current amplitude data segment and the voltage amplitude data segment, the root mean square error algorithm is used to calculate the dispersion of amplitude data points in each data segment relative to the average amplitude within the segment, so as to obtain the root mean square error value of the current amplitude of the effective high-frequency current signal in the current window, and the root mean square error value of the voltage amplitude of the effective high-frequency voltage signal in the current window.

[0140] Based on the root mean square error of the current amplitude and the root mean square error of the voltage amplitude, a comprehensive fluctuation metric is synthesized by weighted averaging or taking the maximum value, which represents the overall signal amplitude fluctuation level. This comprehensive fluctuation metric is then output as a real-time amplitude fluctuation parameter.

[0141] B107. Based on real-time amplitude fluctuation parameters and preset initial values ​​of amplitude correction coefficients, the optimal amplitude correction coefficient under the current operating condition is calculated using a proportional-integral adjustment algorithm; the specific adjustment logic is as follows:

[0142] The target input value of the proportional-integral (PI) control algorithm is set to zero, and the real-time amplitude fluctuation parameter is used as the process feedback value of the PI control algorithm.

[0143] Based on the target input value and the process feedback value, the amplitude fluctuation error value within the current control cycle is calculated;

[0144] The amplitude fluctuation error value is input to a proportional-integral controller with a preset proportional coefficient and integral coefficient, and proportional and integral operations are performed to obtain the corresponding proportional output term and integral output term.

[0145] The proportional output term and the integral output term are summed, and the summation result is superimposed on the initial value of the amplitude correction coefficient to generate the amplitude correction coefficient updated in the current iteration cycle.

[0146] Based on the updated amplitude correction coefficient, the aforementioned signal amplitude calibration, amplitude fluctuation parameter calculation, and proportional-integral adjustment process are repeated to form a closed-loop online optimization process.

[0147] The closed-loop optimization process continues until the calculated value of the real-time amplitude fluctuation parameter stabilizes within the preset zero value allowable deviation range, and the amplitude correction coefficient corresponding to this point is determined as the optimal amplitude correction coefficient that minimizes amplitude fluctuation.

[0148] B108. Based on the optimal amplitude correction coefficient, a gain calibration algorithm is used to perform amplitude calibration on the effective high-frequency current signal and the effective high-frequency voltage signal respectively; specifically:

[0149] First, based on the sequence of time-series amplitude data points contained in the effective high-frequency current signal, a scalar multiplication algorithm is used to multiply each data point in the sequence with the optimal amplitude correction coefficient in turn to obtain the amplitude-compensated time-series current data point sequence, and the initial corrected AC current signal is obtained based on this sequence.

[0150] Meanwhile, based on the time-series amplitude data point sequence contained in the effective high-frequency voltage signal, the same scalar multiplication algorithm is used to multiply each data point in the sequence with the optimal amplitude correction coefficient in turn to obtain the amplitude-compensated time-series voltage data point sequence, and the initial corrected voltage response signal is obtained based on the sequence.

[0151] B109. Based on a preset allowable threshold for amplitude calibration error, a relative error analysis algorithm is used to verify the calibration effect of the initially corrected AC current signal and the initially corrected voltage response signal; the specific implementation process is as follows:

[0152] First, based on the amplitude of the standard high-frequency excitation signal, the theoretical current amplitude and theoretical voltage amplitude of the standard high-frequency excitation signal are obtained by using the root mean square value calculation method.

[0153] Meanwhile, based on the initial corrected AC current signal and the initial corrected voltage response signal, the corresponding measured current amplitude and measured voltage amplitude are obtained respectively using the same root mean square value calculation method.

[0154] Based on the theoretical current amplitude and the measured current amplitude, the relative error value of the current amplitude is obtained using the first relative error calculation formula. The first relative error calculation formula is: the relative error value of the current amplitude is equal to the absolute value of the difference between the measured current amplitude and the theoretical current amplitude, divided by the theoretical current amplitude.

[0155] Based on the theoretical voltage amplitude and the measured voltage amplitude, the voltage amplitude relative error value is obtained using the second relative error calculation formula. The second relative error calculation formula is: the voltage amplitude relative error value is equal to the absolute value of the difference between the measured voltage amplitude and the theoretical voltage amplitude, divided by the theoretical voltage amplitude.

[0156] Based on the preset amplitude calibration error allowable threshold, the relative error value of the current amplitude and the relative error value of the voltage amplitude are compared with the amplitude calibration error allowable threshold respectively.

[0157] If the relative error value of the current amplitude is less than or equal to the allowable threshold value of the amplitude calibration error, and the relative error value of the voltage amplitude is less than or equal to the allowable threshold value of the amplitude calibration error, then the calibration effect is determined to be satisfactory, the initial corrected AC current signal is obtained as the final corrected AC current signal, and the initial corrected voltage response signal is obtained as the final corrected voltage response signal.

[0158] If the relative error of the current amplitude is greater than the allowable threshold of the amplitude calibration error, or the relative error of the voltage amplitude is greater than the allowable threshold of the amplitude calibration error, the control parameters of the proportional-integral adjustment algorithm are adjusted based on the degree of deviation, and the optimal amplitude correction coefficient is recalculated.

[0159] The degree of deviation is quantified by the deviation ratio, which is equal to the ratio of the portion of the current relative error value that exceeds the allowable threshold of amplitude calibration error to the allowable threshold of amplitude calibration error.

[0160] Based on the aforementioned deviation ratio, the proportional coefficient and integral coefficient in the proportional-integral adjustment algorithm are dynamically increased using preset parameter adjustment rules.

[0161] Finally, based on the optimal amplitude correction coefficient recalculated after adjusting the control parameters, the amplitude gain processing, relative error calculation and verification comparison steps are re-executed to form an amplitude calibration closed-loop optimization process until the relative error values ​​of the current amplitude and the voltage amplitude are both less than or equal to the allowable threshold of the amplitude calibration error, and finally the final corrected AC current signal and the final corrected voltage response signal are obtained.

[0162] B110. Based on the signal timing synchronization criterion, a timestamp alignment algorithm is adopted to integrate the final corrected AC current signal and the final corrected voltage response signal according to the sampling timestamp, and to complete the encapsulation according to the data frame format preset by the main chip, so as to obtain the compensated AC excitation response information that meets the impedance calculation requirements of the main chip.

[0163] For example, in this embodiment, when performing the second-level progressive compensation under dynamic operating conditions (such as a vehicle traveling at a constant speed of 60 km / h), a specific and complete parameter setting example is as follows: The high-frequency band screening threshold is set to 50 Hz based on experimental calibration. The bandpass filter parameters are set accordingly: the lower passband frequency is 50 Hz, and the upper passband frequency is 500 Hz. The specific values ​​of the filter order adjustment rule are as follows: when the calculated amplitude proportion of signal components above 50 Hz is less than 5%, a 12th-order Butterworth filter is used; when the proportion is between 5% and 15%, an 8th-order filter is used; when the proportion is higher than 15%, a 4th-order filter is used. The low-frequency residual judgment threshold is set to 2%. The sliding time window length used for amplitude fluctuation analysis is fixed at 10 seconds. The initial value of the amplitude correction coefficient is set to 1.000. The proportional coefficient of the proportional-integral regulator is set to 0.1, the integral coefficient is set to 0.01, and the adjustment control cycle is 100 milliseconds. The allowable threshold for amplitude calibration error is set to 1.5%. If the relative error between the measured signal amplitude and the theoretical value after calibration exceeds this threshold, the proportional and integral coefficients will be dynamically increased in steps of 0.02 for re-optimization. The entire process forms a closed loop: if the low-frequency energy proportion of the filtered signal exceeds 2%, the design parameters and internal state of the bandpass filter are recalculated and updated according to the out-of-tolerance ratio (e.g., increasing the frequency by 5 Hz if it exceeds 10%), and iterated until all verifications meet the standards. Finally, the current and voltage signals that meet the threshold are aligned with timestamps with 1 millisecond precision and encapsulated, outputting a structured compensated AC excitation response data frame.

[0164] S405. Based on the acquired compensated AC excitation response information, a hierarchical progressive data verification algorithm is used to perform dual verification of integrity and validity. The verification results are then combined to perform differential processing. The specific process includes:

[0165] S4051. Based on the compensated AC excitation response information and the preset information integrity verification rules, perform hierarchical integrity verification and obtain the integrity verification result; the hierarchical integrity verification includes:

[0166] S4052. Based on the total number of battery pack cells pre-stored in the main chip, a data quantity statistics and comparison algorithm is used to count the actual number of cell identifiers contained in the compensated voltage response signal, and the actual number obtained is compared with the total number of cells to obtain the cell coverage quantity verification result.

[0167] S4053. Based on the preset excitation duration parameter and preset sampling period parameter from the AC excitation strategy, the theoretical time-series data length of the current signal and voltage signal is calculated using the sampling point conversion algorithm. The theoretical time-series data length is then compared one by one with the actual time-series data length of the compensated current signal and the compensated voltage response signal to obtain the time-series data length verification result.

[0168] S4054. Based on the data bit width, check bit rules and byte order format specifications specified in the preset data communication protocol, the cyclic redundancy check algorithm is used to perform check calculations on the original encoded data of the compensated AC excitation response information to obtain the data encoding integrity check result.

[0169] S4055. Based on the cell coverage quantity verification sub-result, the timing data length verification sub-result, and the data encoding integrity verification sub-result, a logical AND operation is used to integrate and determine the result. If all sub-results pass, the integrity verification result is determined to be qualified; if any sub-result fails, the integrity verification result is determined to be unqualified.

[0170] S4056. If the integrity verification result is unqualified, a first-level acquisition compensation abnormal flag is generated and transmitted to the main chip to trigger the first re-acquisition process.

[0171] S4057. If the integrity verification result is qualified, then based on the preset information validity verification rules, perform double validity verification to obtain the validity verification result;

[0172] For example, in the layered integrity verification stage of this embodiment, the specific parameter settings and examples are as follows: For a battery pack composed of 96 ternary lithium battery cells connected in series, the preset total number of cells is 96. Based on a standard excitation with a duration of 2 seconds and a sampling period of 10 milliseconds, the theoretical time-series data length is calculated to be 201 sampling points. The data communication protocol specifies the use of a cyclic redundancy check algorithm, specifically the CRC-16-CCITT standard, with a generator polynomial of 0x1021 and an initial value of 0xFFFF. The integrity verification process is strictly executed according to the above parameters: First, the actual number of cell identifiers included in the voltage data must be equal to 96; second, the actual data lengths of the current and voltage signals are checked separately, and both must be 201 points; finally, the CRC calculation is performed on the original encoding of the entire data packet, and the result must match the attached check code. Only when all three checks pass is the integrity check result deemed qualified; if any one fails, for example, only 95 cell voltages are counted or the data length is incorrect, a first-level acquisition compensation abnormality flag bit with the code 0xE1 is immediately generated and transmitted to the main chip to trigger the first re-acquisition process.

[0173] The dual validity check includes:

[0174] S40571. Based on the signal timing synchronization criterion, the timestamp deviation analysis algorithm is used to extract the timestamp information of the corresponding data points in the compensated current signal and each voltage response signal, calculate the timing deviation value within the same data frame, and compare the timing deviation value with the preset timing alignment accuracy allowable threshold to obtain the timing alignment accuracy verification result.

[0175] S40572. Based on the threshold of reasonable range of signal amplitude calibrated according to cell type, a threshold interval comparison algorithm is adopted to compare each time-series amplitude point of the compensated current signal with the preset current amplitude range, and to compare each time-series amplitude point of the compensated voltage response signal of each cell with the preset voltage amplitude range, so as to obtain the signal amplitude reasonableness verification result.

[0176] S40573. Based on the timing alignment accuracy verification sub-result and the signal amplitude rationality verification sub-result, a logical AND operation is used to integrate and judge. If all sub-results pass, the validity verification result is judged to be qualified; if any sub-result fails, the validity verification result is judged to be unqualified.

[0177] S40574. If the validity verification result is unqualified, a second-level acquisition compensation abnormal flag bit is generated, and the second-level acquisition compensation abnormal flag bit and the current verification data are synchronously transmitted to the main chip to trigger the re-acquisition process, and the cumulative count of the number of re-acquisitions is started in the main chip.

[0178] S40575. If the validity verification result is qualified, it is determined that the compensated AC excitation response information meets the impedance calculation requirements of the main chip, and based on the preset data transmission protocol, the compensated AC excitation response information is encapsulated into a data frame that conforms to the main chip interface specification and transmitted to the main chip.

[0179] S40576. Based on a preset re-acquisition count threshold, determine the cumulative number of times the re-acquisition process is executed in the main chip; if the cumulative number of executions is less than or equal to the re-acquisition count threshold, continue executing the re-acquisition process; if the cumulative number of executions is greater than the re-acquisition count threshold, generate a third-level acquisition compensation abnormality flag, and transmit the third-level acquisition compensation abnormality flag, the cumulative number of executions, and the fault type information to the main chip to trigger the system fault alarm process, and terminate the current acquisition compensation task.

[0180] For example, in this embodiment, the specific parameter settings and examples for the dual validity verification stage are as follows: the allowable threshold for timing alignment accuracy is set to 1 millisecond. The threshold for the reasonable range of signal amplitude is set according to the cell type. For ternary lithium batteries, the reasonable range of the compensated voltage response signal is set to 2.5 volts to 4.2 volts, and the reasonable range of the compensated current signal is set to -5 amps to +5 amps. The threshold for the number of re-acquisitions is set to 3 times. The verification process is as follows: the system first extracts the timestamps of the corresponding data points of the current and the voltage signals of each cell, calculates their deviations, and if all deviations are less than 1 millisecond, the timing alignment verification passes; at the same time, the system compares all current values ​​and voltage values ​​point by point. If they all fall within the preset ranges of [-5A, +5A] and [2.5V, 4.2V], the amplitude reasonableness verification passes. Only when both of the above verifications pass is the data determined to be valid and encapsulated and sent to the main chip. If any verification fails, a second-level exception flag with the code 0xE2 is generated, triggering a re-acquisition process and incrementing the retry count by 1; if the verification still fails after a cumulative total of 3 re-acquisitions, a third-level exception flag with the code 0xE3 is generated, a system fault is reported, and the task is terminated.

[0181] S5. Based on the compensated AC excitation response information and the preset multi-dimensional impedance anomaly judgment strategy, a multi-dimensional impedance judgment parameter set is obtained. At the same time, based on the multi-dimensional impedance judgment parameter set and the evaluation algorithm, the thermal runaway risk level of the battery pack is obtained. The multi-dimensional impedance judgment parameter set and the thermal runaway risk level of the battery pack are encapsulated and reported back via CAN communication.

[0182] It should be further explained that the process of obtaining the multidimensional impedance determination parameter set in this embodiment includes:

[0183] S501. Based on the compensated AC excitation response information and the excitation frequency list in the AC excitation strategy control parameters, a data segmentation extraction algorithm is adopted to separate the cell voltage response data segment and the battery pack circuit current data segment at each test frequency point from the synchronously collected time series data according to the excitation application time window corresponding to each excitation frequency, and obtain the frequency segment cell voltage dataset and the frequency segment circuit current dataset.

[0184] It should be further explained that this embodiment uses a time window matching and data slicing algorithm to obtain the frequency band dataset. The specific steps include:

[0185] Based on the control parameters of the AC excitation strategy, the theoretical excitation start time and theoretical excitation end time are calculated for each test frequency point, thereby obtaining the theoretical data acquisition time window corresponding to each frequency point.

[0186] Based on the high-precision synchronization timestamps carried by the current and voltage signals in the compensated AC excitation response information, each data point in the original time-series data stream is mapped to the corresponding theoretical time axis.

[0187] Based on the mapped time axis and the acquired theoretical data acquisition time window, array indexing or pointer positioning methods are used to accurately extract the current data point subsets and cell voltage data point subsets that fall completely within each theoretical time window from the original time series data stream.

[0188] Based on the extracted subsets of data points at each frequency point, the data points are categorized and reorganized according to the test frequency points to form a structured data set indexed by frequency points, thereby ultimately obtaining the cell voltage dataset and the circuit current dataset of the frequency segment.

[0189] S502. Based on the frequency segment cell voltage dataset and the frequency segment loop current dataset, the fast Fourier transform algorithm is used to perform frequency domain analysis on each data segment, extract the fundamental frequency component that is the same as the excitation frequency, obtain the fundamental voltage amplitude and fundamental voltage phase angle of each cell at each test frequency point, as well as the fundamental current amplitude and fundamental current phase angle of the loop current, thereby obtaining the cell voltage frequency domain parameters and loop current frequency domain parameters at each test frequency point;

[0190] S503. Based on the cell voltage frequency domain parameters and the circuit current frequency domain parameters, a complex impedance calculation algorithm is used to calculate the AC impedance parameters of each cell at each test frequency point; specifically:

[0191] S5031. Based on the cell voltage frequency domain parameters at each test frequency point, a conversion algorithm based on Euler's formula is used to convert the voltage fundamental amplitude and voltage fundamental phase angle contained therein into complex voltage phasors to obtain voltage phasor data at each frequency point.

[0192] S5032. Based on the loop current frequency domain parameters at each test frequency point, the same Euler formula-based conversion algorithm is used to convert the current fundamental amplitude and current fundamental phase angle contained therein into complex current phasors to obtain current phasor data at each frequency point.

[0193] S5033. Based on the acquired voltage phasor data and current phasor data, a division operation in the complex domain is used to divide the voltage phasor by the current phasor at the same frequency point to calculate the complex impedance value at the test frequency point.

[0194] S5034. Based on the calculated complex impedance value, the algorithm for calculating the complex modulus is used to obtain the modulus of the complex impedance, which is used as the AC impedance amplitude at the corresponding frequency; at the same time, the algorithm for calculating the complex argument is used to obtain the argument of the complex impedance, which is used as the AC impedance phase angle at the corresponding frequency.

[0195] S5035. Iterate through all preset test frequency points in sequence and repeatedly execute phasor transformation based on voltage and current frequency domain parameters, impedance calculation based on complex division, and amplitude and phase angle extraction based on complex operations to finally obtain and integrate an AC impedance dataset covering the entire test frequency band for each cell.

[0196] S504. Based on the AC impedance parameters of each cell at each test frequency point, for each test frequency point, a sorting and elimination algorithm is used to remove the maximum and minimum values ​​of the impedance amplitude of all cells at that frequency, and then the arithmetic average algorithm is used to calculate the remaining impedance amplitude to obtain the reference average value of the cell impedance at the corresponding test frequency point.

[0197] S505. Based on the AC impedance amplitude of each cell and the reference average value of the cell impedance at the corresponding test frequency point, calculate the relative impedance deviation ratio of each cell; compare the relative impedance deviation ratio with a preset impedance deviation allowable threshold; if the relative impedance deviation ratio is greater than the impedance deviation allowable threshold, set the impedance deviation flag of the cell to an effective state; otherwise, set it to an invalid state, thereby obtaining the impedance deviation flag of each cell.

[0198] S506. Based on the thermal runaway strongly correlated characteristic frequency list pre-stored in the main chip, extract the AC impedance amplitude of each cell at the corresponding characteristic frequency from the AC impedance parameters of each cell at each test frequency point, and use it as the thermal runaway associated impedance parameter of each cell; compare the thermal runaway associated impedance parameter with the preset characteristic frequency impedance alarm threshold; if the thermal runaway associated impedance parameter exceeds the characteristic frequency impedance alarm threshold, set the thermal runaway risk flag position of the cell to an effective state, otherwise set it to an invalid state, thereby obtaining the thermal runaway risk flag position of each cell;

[0199] S507. Based on the real-time voltage data of each cell obtained from the cell operating condition discrimination data, a sliding window statistical method is used to calculate the voltage characteristic value of each cell during the analysis period; after removing the maximum and minimum values ​​of the voltage characteristic values ​​of all cells using a sorting and elimination algorithm, the average value of the remaining values ​​is calculated to obtain the reference average value of the cell voltage characteristics; the deviation ratio of each cell voltage characteristic value relative to the reference average value of the voltage characteristics is calculated and compared with a preset voltage deviation allowable threshold. If the voltage deviation exceeds the voltage deviation allowable threshold, the voltage deviation flag of the corresponding cell is set to a valid state; otherwise, it is set to an invalid state, thereby obtaining the voltage deviation flag of each cell.

[0200] S508. Based on the real-time temperature data of each cell obtained from the cell operating condition discrimination data, a sliding window statistical method is used to calculate the temperature characteristic value of each cell during the analysis period; after removing the maximum and minimum values ​​of the temperature characteristic values ​​of all cells using a sorting and elimination algorithm, the average value of the remaining values ​​is calculated to obtain the reference average value of the cell temperature characteristics; the deviation ratio of each cell temperature characteristic value relative to the reference average value of the temperature characteristics is calculated and compared with a preset temperature deviation allowable threshold. If the deviation exceeds the temperature deviation allowable threshold, the temperature deviation flag of the corresponding cell is set to a valid state; otherwise, it is set to an invalid state, thereby obtaining the temperature deviation flag of each cell.

[0201] S509. Based on the impedance deviation flag, thermal runaway risk flag, voltage deviation flag, and temperature deviation flag corresponding to each cell, a data fusion encapsulation algorithm is used to combine the four flags into a complete multidimensional state vector according to the cell's unique identifier. The state vectors of all cells are summarized to finally obtain the multidimensional impedance judgment parameter set.

[0202] For example, this embodiment uses a battery pack containing 96 ternary lithium batteries as an example. The specific parameter settings for the impedance calculation and state assessment process are as follows: The excitation frequency list is preset to 31 frequency points distributed logarithmically at equal intervals from 1000 Hz to 0.01 Hz. Data segmentation is based on a sampling period of 10 milliseconds and the excitation duration of each frequency point (0.5 seconds for high frequency and 300 seconds for low frequency) to determine the theoretical time window. The Fast Fourier Transform is calculated using 4096 points and a Hanning window is added. After complex impedance calculation, the impedance amplitude of all cells at each frequency point is divided by a maximum and a minimum value, and then the arithmetic mean is calculated to obtain the reference average impedance value for that frequency. The deviation judgment thresholds are set as follows: the allowable threshold for relative impedance deviation is 5%, the allowable threshold for voltage deviation is 2%, and the allowable threshold for temperature deviation is 3%. The list of characteristic frequencies strongly correlated with thermal runaway is set to [0.1 Hz, 0.316 Hz, 1 Hz], and the corresponding characteristic frequency impedance alarm threshold is set to 20 milliohms. Sliding window statistics are used to calculate voltage and temperature characteristic values. The window length is set to 10 seconds and the sliding step size is 5 seconds. Finally, the multidimensional state vector of each cell is composed of its impedance deviation flag, thermal runaway risk flag, voltage deviation flag and temperature deviation flag in sequence.

[0203] It should be further explained that the method for obtaining the thermal runaway risk level of the battery pack in this embodiment specifically includes:

[0204] S510. First, based on the cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag corresponding to each cell in the multi-dimensional impedance judgment parameter set, a state statistics and weighted fusion algorithm is used to calculate the overall risk quantification value of the battery pack; specifically, the number of each of the cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag in the effective state within the battery pack is counted, and each is multiplied by a preset weighting coefficient and then summed to obtain the overall risk quantification value of the battery pack;

[0205] It should be further explained that this embodiment uses a weighted fusion algorithm to obtain the overall risk quantification value of the battery pack. The specific steps are as follows:

[0206] Based on the multidimensional impedance judgment parameter set, the number of cells in the battery pack with the cell impedance deviation flag bit in a valid state, the number of cells with the cell thermal runaway risk flag bit in a valid state, the number of cells with the cell voltage deviation flag bit in a valid state, and the number of cells with the cell temperature deviation flag bit in a valid state are counted respectively to obtain the statistical count of four independent valid flag bits.

[0207] Based on a preset weighting coefficient configuration table, obtain the first weighting coefficient corresponding to the cell impedance deviation flag, the second weighting coefficient corresponding to the cell thermal runaway risk flag, the third weighting coefficient corresponding to the cell voltage deviation flag, and the fourth weighting coefficient corresponding to the cell temperature deviation flag.

[0208] Based on the statistical count of four valid flag bits and their corresponding weighting coefficients, a linear weighted summation algorithm is used to calculate the overall risk quantification value of the battery pack. Specifically, the number of valid flag bits for cell impedance deviation is multiplied by the first weighting coefficient, the number of valid flag bits for cell thermal runaway risk is multiplied by the second weighting coefficient, the number of valid flag bits for cell voltage deviation is multiplied by the third weighting coefficient, and the number of valid flag bits for cell temperature deviation is multiplied by the fourth weighting coefficient. Then, the four products are summed to obtain the final overall risk quantification value of the battery pack. For example, in this embodiment, the preset weighting coefficient configuration table is set based on the severity and urgency of battery safety risks. In a typical example, the four weighting coefficients are configured as follows: the second weighting coefficient (thermal runaway risk) is set to 10, because it is directly related to thermal runaway, the most serious safety fault; the first weighting coefficient (impedance deviation) and the fourth weighting coefficient (temperature deviation) are both set to 3, which respectively reflect the abnormality of the electrochemical state and the thermal state, and are important risk precursors; the third weighting coefficient (voltage deviation) is set to 1, because it usually represents consistent fluctuations in the short term, and the direct risk is relatively low. This configuration ensures that when any cell in the battery pack exhibits a thermal runaway risk indicator, its contribution value will far exceed that of other types of anomalies, thus enabling the overall risk quantification value to sensitively and accurately reflect the most dangerous safety conditions.

[0209] S511. Based on the pre-stored battery pack thermal runaway risk level determination rules, the overall risk quantification value of the battery pack is compared with the preset multi-level risk thresholds to determine the final risk level. The risk level determination rules are as follows: if the overall risk quantification value is less than or equal to the first risk threshold, the risk level is determined to be level 0, indicating no risk; if the overall risk quantification value is greater than the first risk threshold but less than or equal to the second risk threshold, the risk level is determined to be level 1, indicating low risk; if the overall risk quantification value is greater than the second risk threshold but less than or equal to the third risk threshold, the risk level is determined to be level 2, indicating medium risk; if the overall risk quantification value is greater than the third risk threshold but less than or equal to the fourth risk threshold, the risk level is determined to be level 3, indicating high risk; if the overall risk quantification value is greater than the fourth risk threshold, the risk level is determined to be level 4, indicating emergency risk.

[0210] S512. Based on the determined risk level of the battery pack thermal runaway, generate a corresponding risk level code, and associate and encapsulate it with the specific abnormal flag information and associated cell identifier that triggered the risk level, so as to obtain a complete battery pack thermal runaway risk level determination result that can be used for reporting.

[0211] For example, in the overall risk assessment of the battery pack in this embodiment, the specific parameter settings are as follows: The preset weighting coefficients are configured as follows: the second weighting coefficient corresponding to the cell thermal runaway risk flag is set to 10, the first weighting coefficient corresponding to the cell impedance deviation flag and the fourth weighting coefficient corresponding to the cell temperature deviation flag are both set to 3, and the third weighting coefficient corresponding to the cell voltage deviation flag is set to 1. The multi-level risk thresholds are set as follows: the first risk threshold is 5, the second risk threshold is 20, the third risk threshold is 50, and the fourth risk threshold is 100. Taking a 96-cell battery pack as an example, if it is found that the thermal runaway risk flags of 2 cells are valid, the impedance deviation flags of 5 cells are valid, the temperature deviation flags of 10 cells are valid, and the voltage deviation flags of 15 cells are valid, then the overall risk quantification value is calculated as 2×10+5×3+10×3+15×1=80. This value is greater than the third risk threshold of 50 and less than or equal to the fourth risk threshold of 100, so the thermal runaway risk level of the battery pack is determined to be level 3 (high risk). The final risk level determination result includes risk level code 0x03, anomaly flag statistics, and a list of associated specific cell identifiers.

[0212] Example 2

[0213] Please see Figure 2 Another embodiment of the present invention provides: an early warning device for thermal runaway of a lithium-ion battery pack, comprising:

[0214] The data acquisition module obtains cell operating condition data, including cell voltage, temperature and current data, through a configured sampling chip.

[0215] The operating condition determination module obtains a determination result with an operating condition type level flag based on the cell operating condition discrimination data and operating condition determination rules; the operating condition types include ideal static operating condition, quasi-static operating condition and dynamic operating condition.

[0216] The incentive matching module, based on the judgment result with the working condition type level flag, matches and obtains the AC incentive strategy corresponding to the current working condition level from the preset AC incentive strategy library;

[0217] The excitation response module responds to the AC excitation strategy and synchronously collects the compensated AC excitation response information through a sampling chip configured with an operating condition excitation compensation strategy. The compensated AC excitation response information includes the current of the battery pack circuit and the voltage response of each configured cell, and is sent to the configured main chip. The operating condition excitation compensation strategy is determined by the operating condition type level flag bit.

[0218] The impedance determination module obtains a multi-dimensional impedance determination parameter set based on the compensated AC excitation response information and a preset multi-dimensional impedance anomaly determination strategy. At the same time, it obtains the battery pack thermal runaway risk level based on the multi-dimensional impedance determination parameter set and the evaluation algorithm. The multi-dimensional impedance determination parameter set and the battery pack thermal runaway risk level are encapsulated and reported back via CAN communication.

[0219] The multidimensional impedance judgment parameter set includes cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag.

[0220] Example 3

[0221] Please see Figure 3 This application also discloses an electronic device for performing a warning method for thermal runaway of a lithium-ion battery pack, including a printed circuit board and a set of low-voltage interfaces disposed thereon;

[0222] The printed circuit board integrates:

[0223] An AC signal generation circuit is used to generate a wideband sinusoidal current signal with adjustable frequency and amplitude according to the control instructions issued by the main chip, and inject the current signal into the main circuit of the battery pack through the excitation signal injection interface.

[0224] The high-precision sampling chip has its input terminal connected to the current sampling point of the battery pack's main circuit and the voltage sampling point of each cell via an analog sampling path, and its output terminal connected to the main chip via a digital data transmission path. It is used to synchronize, perform high-precision analog-to-digital conversion and data upload of the sinusoidal excitation current signal and the AC voltage response signal of each cell.

[0225] The power chip has its input terminal connected to the device power supply interface in the low-voltage interface set, which is used to convert the external input power into a stable 5V DC voltage, and to power the AC signal generation circuit, the high-precision sampling chip and the main chip through independent 5V power supply paths.

[0226] The communication transceiver chip is connected to the main chip through a data exchange path. It is used to convert the risk information generated by the main chip into a message that conforms to the preset vehicle network protocol and send it to the outside through the communication interface.

[0227] The low-voltage interface set also includes:

[0228] A reference ground interface that provides a common potential reference for all analog and digital circuits on the printed circuit board;

[0229] The high-precision sampling chip is connected to the cell sampling interface of each voltage and temperature sensor in the battery pack;

[0230] The functional chips and circuits on the printed circuit board are electrically connected to the low-voltage interface to form a closed-loop online monitoring system that completes excitation signal injection, synchronization signal acquisition, impedance calculation, risk analysis, and result reporting.

[0231] Please see Figure 4 The AC signal generation circuit includes an AC signal generator and other supporting devices, capable of outputting a sinusoidal current excitation signal with a wide frequency range. The frequency and amplitude characteristics of the sinusoidal signal can be controlled via a software interface. When the AC signal generation circuit receives a generation command from the main chip, it injects a sinusoidal current signal of a specified frequency into the battery pack circuit through the AC excitation injection interface. When the frequency command signal issued by the main chip switches rapidly, frequency sweep impedance measurement can be achieved. The sinusoidal current excitation signal actually applied to the battery pack circuit and the AC voltage response signal of each cell are synchronously acquired by a high-precision sampling chip and sent to the main chip to calculate the AC impedance information at the current frequency.

[0232] This application also discloses a computer-readable storage medium storing computer instructions that, when executed, provide an early warning method for thermal runaway of a lithium-ion battery pack.

[0233] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of the present invention without departing from the spirit and scope of the claims. All of these variations are within the protection scope of the present invention.

Claims

1. A method for early warning of thermal runaway in lithium-ion battery packs, characterized in that, include: The configured sampling chip acquires cell operating condition data, including cell voltage, temperature and current data; Based on the cell operating condition discrimination data and the operating condition judgment rules, a judgment result with an operating condition type level flag is obtained; Based on the determination result with the working condition type level flag, the corresponding AC incentive strategy for the current working condition level is matched and obtained from the preset AC incentive strategy library. In response to the AC excitation strategy, the AC excitation response information after compensation is synchronously collected through a sampling chip configured with an operating condition excitation compensation strategy; the operating condition excitation compensation strategy is determined by the operating condition type level flag bit; Based on the compensated AC excitation response information and the preset multidimensional impedance anomaly judgment strategy, a multidimensional impedance judgment parameter set is obtained. At the same time, based on the multidimensional impedance judgment parameter set, the thermal runaway risk level of the battery pack is obtained. The multidimensional impedance judgment parameter set and the thermal runaway risk level of the battery pack are encapsulated and reported back via CAN communication. The multidimensional impedance judgment parameter set includes cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag. The process of synchronously acquiring compensated AC excitation response information through a sampling chip configured with a working condition excitation compensation strategy includes: Based on the judgment result with the working condition type level flag and the matching obtained AC excitation strategy, in response to the working condition excitation compensation strategy of the sampling chip, the signal compensation parameters in the AC excitation strategy and the compensation logic of the corresponding working condition are loaded into the control unit of the sampling chip to complete the pre-configuration of the working condition excitation compensation strategy of the sampling chip. Based on the pre-configured working condition incentive compensation strategy, a flag bit association algorithm is used to establish a mapping relationship between the working condition type level flag bit and the execution logic of the working condition incentive compensation strategy, and to obtain the compensation strategy triggering rules; the compensation strategy triggering rules are used to clarify the enabling status of the compensation algorithm and the parameter adaptation requirements corresponding to different working condition type level flag bits. In response to the excitation signal generation flag in the AC excitation strategy, when the excitation signal generation flag is set to 1, the sampling chip starts the synchronous acquisition mechanism to synchronously acquire the original current signal of the battery pack circuit and the original voltage response signal of each cell to obtain the initial AC excitation response dataset.

2. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 1, characterized in that, The operating condition type level flag includes a first numerical operating condition type level flag, a second numerical operating condition type level flag, and a third numerical operating condition type level flag; the operating condition types include ideal static operating condition, quasi-static operating condition, and dynamic operating condition; the ideal static operating condition corresponds to the first numerical operating condition type level flag; the quasi-static operating condition corresponds to the second numerical operating condition type level flag; and the dynamic operating condition corresponds to the third numerical operating condition type level flag; the AC excitation strategy includes an excitation signal generation flag, AC excitation strategy control parameters, and signal compensation parameters; the AC excitation strategy control parameters include at least the preset test frequency points and execution order for applying excitation, the excitation current amplitude corresponding to each test frequency point, the duration of excitation signal application at each test frequency point, and the resting interval time between excitations at two adjacent test frequency points; the excitation signal generation flag includes a high level set to 1 and a low level set to 0.

3. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 2, characterized in that, The AC incentive strategy corresponding to the current operating condition level is retrieved from the preset AC incentive strategy library, including: A preset set of AC excitation strategy parameters for all operating conditions is provided; the set of AC excitation strategy parameters for all operating conditions includes excitation frequency parameters, excitation current amplitude parameters, excitation application time parameters, frequency interval rest time parameters, and excitation compensation parameters corresponding to each operating condition. Based on the determination result with the working condition type level flag, the value of the working condition type level flag is extracted by the flag parsing algorithm to obtain the current working condition type determination information; If the current operating condition type determination information is an ideal static operating condition, then the excitation parameters corresponding to the ideal static operating condition are extracted from the full operating condition AC excitation strategy parameter set to generate a first AC excitation strategy control parameter set; the first AC excitation strategy control parameter set includes a first excitation frequency parameter, a first excitation current amplitude parameter, a first excitation application time parameter, and a first frequency interval static time parameter.

4. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 3, characterized in that, The system retrieves the corresponding AC incentive strategy from a pre-defined AC incentive strategy library, including: If the current operating condition type determination information is a quasi-static operating condition, then the second AC excitation strategy control parameter set corresponding to the quasi-static operating condition type is extracted from the full operating condition AC excitation strategy parameter set; the second AC excitation strategy control parameter set includes the same parameters as the first AC excitation strategy control parameter set and the first compensation flag bit; If the current operating condition type determination information is a dynamic operating condition, then the third AC excitation strategy control parameter set corresponding to the dynamic operating condition is extracted from the full operating condition AC excitation strategy parameter set; the third AC excitation strategy control parameter set includes the same parameters as the second AC excitation strategy control parameter set and a second compensation flag bit.

5. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 4, characterized in that, The system also includes synchronously acquiring compensated AC excitation response information via a sampling chip configured with a working condition excitation compensation strategy, and further includes: Based on the operating condition type level flag and the compensation strategy triggering rules, the type of compensation strategy to be executed is determined. The specific determination and execution logic includes: If the current operating condition type level flag is the first numerical operating condition type level flag, then the initial AC excitation response dataset is directly used as the compensated AC excitation response information to be output. If the current working condition type level flag is the second numerical working condition type level flag, then based on the first compensation flag, the preset first working condition excitation compensation algorithm is called to perform the first hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information. If the current working condition type level flag is the third numerical working condition type level flag, based on the compensation strategy triggering rules and the second compensation flag, the preset second working condition excitation compensation algorithm is activated to perform the second hierarchical progressive compensation processing on the initial AC excitation response dataset to obtain the compensated AC excitation response information.

6. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 5, characterized in that, The obtained multidimensional impedance determination parameter set includes: Based on the compensated AC excitation response information and the excitation frequency list in the AC excitation strategy control parameters, a data segmentation extraction algorithm is adopted to separate the cell voltage response data segment and the battery pack loop current data segment at each test frequency point from the synchronously collected time series data according to the excitation application time window corresponding to each excitation frequency, so as to obtain the frequency segment cell voltage dataset and the frequency segment loop current dataset. Based on the frequency-segmented cell voltage dataset and the frequency-segmented loop current dataset, the Fast Fourier Transform algorithm is used to perform frequency domain analysis on each data segment to obtain the cell voltage frequency domain parameters and loop current frequency domain parameters at each test frequency point. Based on the frequency domain parameters of the cell voltage and the frequency domain parameters of the circuit current, the AC impedance parameters of each cell at each test frequency point are calculated using a complex impedance algorithm.

7. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 6, characterized in that, The method for obtaining the multidimensional impedance determination parameter set also includes: Based on the AC impedance parameters of each cell at each test frequency point, for each test frequency point, the maximum and minimum values ​​of the impedance amplitude of all cells at the corresponding frequency are removed, and then the arithmetic average algorithm is used to calculate the remaining impedance amplitude to obtain the reference average value of the cell impedance at the corresponding test frequency point. Based on the AC impedance amplitude of each cell and the reference average impedance of the cell at the corresponding test frequency point, calculate the relative impedance deviation ratio of each cell. The impedance relative deviation ratio is compared with the preset impedance deviation allowable threshold. If the impedance relative deviation ratio is greater than the impedance deviation allowable threshold, the impedance deviation flag of the corresponding cell is set to an effective state; otherwise, it is set to an invalid state. The impedance deviation flag of each cell is obtained. Based on the list of thermal runaway strongly correlated characteristic frequencies pre-stored in the main chip, the AC impedance amplitude of each cell at the corresponding characteristic frequency is extracted from the AC impedance parameters of each cell at each test frequency point, and used as the thermal runaway correlated impedance parameter of each cell. The thermal runaway-related impedance parameter is compared with the preset characteristic frequency impedance alarm threshold. If the thermal runaway-related impedance parameter exceeds the characteristic frequency impedance alarm threshold, the thermal runaway risk flag position of the corresponding cell is set to an effective state; otherwise, it is set to an invalid state. The thermal runaway risk flag position of each cell is obtained.

8. The early warning method for thermal runaway of a lithium-ion battery pack as described in claim 7, characterized in that, The method for obtaining the multidimensional impedance determination parameter set also includes: Based on the real-time voltage data of each cell obtained from the cell condition discrimination data, the voltage characteristic values ​​of all cells are sorted and eliminated using a sorting and elimination algorithm to remove the maximum and minimum values, and then the average value of the remaining values ​​is calculated to obtain the reference average value of cell voltage characteristics; the deviation ratio of each cell voltage characteristic value relative to the reference average value of voltage characteristics is calculated and compared with a preset voltage deviation allowable threshold. If the voltage deviation exceeds the voltage deviation allowable threshold, the voltage deviation flag of the corresponding cell is set to a valid state; otherwise, it is set to an invalid state, and the voltage deviation flag of each cell is obtained. Based on the real-time temperature data of each cell obtained from the cell condition discrimination data, the temperature characteristic values ​​of all cells are sorted and eliminated by an algorithm to remove the maximum and minimum values, and then the average value of the remaining values ​​is calculated to obtain the reference average value of cell temperature characteristics. Calculate the deviation ratio of each cell's temperature characteristic value relative to the average temperature characteristic reference value, and compare it with a preset temperature deviation allowable threshold. If the deviation exceeds the temperature deviation allowable threshold, set the temperature deviation flag of the corresponding cell to an effective state; otherwise, set it to an invalid state. Obtain the temperature deviation flag of each cell. Based on the impedance deviation flag, thermal runaway risk flag, voltage deviation flag, and temperature deviation flag corresponding to each cell, a data fusion encapsulation algorithm is used to combine the four flags into a complete multidimensional state vector according to the cell's unique identifier. The state vectors of all cells are then summarized to obtain the multidimensional impedance judgment parameter set.

9. A warning device for thermal runaway of a lithium-ion battery pack, used to implement the warning method for thermal runaway of a lithium-ion battery pack according to any one of claims 1-8, characterized in that, include: The data acquisition module obtains cell operating condition data, including cell voltage, temperature and current data, through a configured sampling chip. The operating condition determination module obtains a determination result with an operating condition type level flag based on the cell operating condition discrimination data and operating condition determination rules; the operating condition types include ideal static operating condition, quasi-static operating condition and dynamic operating condition. The incentive matching module, based on the judgment result with the working condition type level flag, matches and obtains the AC incentive strategy corresponding to the current working condition level from the preset AC incentive strategy library; The excitation response module responds to the AC excitation strategy and synchronously collects the compensated AC excitation response information through a sampling chip configured with a working condition excitation compensation strategy; the working condition excitation compensation strategy is determined by the working condition type level flag bit. The impedance determination module obtains a multi-dimensional impedance determination parameter set based on the compensated AC excitation response information and a preset multi-dimensional impedance anomaly determination strategy. At the same time, it obtains the battery pack thermal runaway risk level based on the multi-dimensional impedance determination parameter set and encapsulates and reports the multi-dimensional impedance determination parameter set and the battery pack thermal runaway risk level through CAN communication. The multidimensional impedance judgment parameter set includes cell impedance deviation flag, cell thermal runaway risk flag, cell voltage deviation flag, and cell temperature deviation flag.

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