Battery pack safety management method, system, medium and program product based on BMS

Abstract

A battery pack safety management method, system, medium and program product based on BMS, in which temperature data, voltage data and current data are obtained; temperature gradient data is calculated according to temperature data; the heat diffusion direction between each battery cell is determined, and the heat source battery cell where heat is accumulated is determined; power fluctuation characteristics are calculated according to voltage data and current data of the heat source battery cell; the influence degree of the heat source battery cell on the adjacent battery cell is determined, and the propagation risk value is obtained; the safety area of ​​the battery pack is divided based on the propagation risk value, and the high-risk area is marked; the key battery cells whose charge and discharge depth exceeds the preset safety threshold are screened; the safety buffer distance is determined, and the current power dynamic limit interval is established according to the safety buffer distance; the charge and discharge current of the key battery cell and all battery cells within the preset range of the key battery cell are adjusted according to the current power dynamic limit interval. The application improves the safety of the battery pack.

Description

Technical Field

[0001] The present application belongs to the field of battery packs, and in particular, relates to a battery pack safety management method, system, medium and program product based on BMS. Background Art

[0002] In energy storage systems, the battery management system (BMS) is the core component to ensure the safe operation of the battery pack, and its management capabilities are directly related to the safety and reliability of the entire system. Traditional battery pack safety management methods mainly rely on single threshold judgment and simple logic control, which is difficult to effectively deal with the complex working conditions and changing environments that occur during the actual use of the battery pack, and is prone to misjudgment or missed judgment, thus affecting the service life and safety of the battery pack.

[0003] In the related technology, the battery status data can be first screened and classified to obtain temperature data, current data and voltage data; then a temperature time series vector is generated according to the temperature data, a current time series vector is generated according to the current data, and a voltage time series vector is generated according to the voltage data; the temperature time series vector is input into the battery thermal effect analysis model to process the temperature data and obtain a temperature detection result; the current time series vector and the voltage time series vector are spliced ​​and the spliced ​​vector is input into the battery charge and discharge state analysis model to perform charge and discharge state analysis and obtain a charge and discharge state analysis result; finally, the charge and discharge process of the target battery in the next preset time period is controlled according to the temperature detection result and the charge and discharge state analysis result.

[0004] However, there are complex electrochemical and thermodynamic coupling effects between the individual battery cells in the battery pack, which can cause safety hazards to spread contagiously within the battery pack. When an abnormality occurs in a battery cell, its impact will be transmitted to the adjacent batteries through the coupling effect, and may trigger a chain reaction, reducing the safety of the battery pack. Summary of the invention

[0005] The present application provides a battery pack safety management method, system, medium and program product based on BMS, which are used to improve the safety of the battery pack.

[0006] In a first aspect, the present application provides a battery pack safety management method based on a BMS, which obtains temperature data, voltage data, and current data of each battery cell in the battery pack within a preset sampling time period;

[0007] Calculate the temperature difference between each battery cell and the adjacent battery cell according to the temperature data to obtain temperature gradient data;

[0008] Determine the heat diffusion direction between the battery cells according to the temperature gradient data, and determine the heat source battery cell where the heat is accumulated according to the heat diffusion direction;

[0009] Calculate the power fluctuation characteristics of the heat source battery cell according to the voltage data and current data of the heat source battery cell;

[0010] Combined with the temperature gradient data and power fluctuation characteristics, the influence of the heat source battery cell on the adjacent battery cells is determined to obtain the propagation risk value;

[0011] Divide the safe area of ​​the battery pack based on the propagation risk value, and mark the area where the battery cells with the propagation risk value exceeding the preset threshold are located as a high-risk area;

[0012] Screen key battery cells in high-risk areas whose charge and discharge depth exceeds a preset safety threshold;

[0013] Determine the safety buffer distance according to the location of the key battery cells, and establish a dynamic current and power limit range based on the safety buffer distance;

[0014] The charge and discharge current of the key battery cell and all battery cells within the preset range of the key battery cell are adjusted according to the current power dynamic limit range.

[0015] By adopting the above technical solution, the temperature, voltage and current data of the battery cells are collected, the temperature gradient data is calculated and the direction of heat diffusion is determined, so that the position of the heat source battery cells can be accurately located. By combining the power fluctuation characteristics of the heat source battery cells to analyze the degree of its influence on the adjacent battery cells, the risk of heat propagation can be quantitatively evaluated. Based on the propagation risk value, the safe area is divided and the key battery cells in the high-risk area are identified. A targeted current power dynamic limit interval is established, so as to achieve precise regulation of the charging and discharging current of the key battery cells and their surrounding cells, reduce the over-protection problem caused by the unified restriction strategy for the entire battery pack, and improve the charging and discharging performance of the battery pack while ensuring safety. By establishing a safe buffer distance, a dynamic protection barrier is created for the high-risk area, which slows down the spread of thermal runaway in the battery pack and improves the safety and reliability of the battery pack.

[0016] In combination with some embodiments of the first aspect, in some embodiments, calculating the temperature difference between each battery cell and an adjacent battery cell according to the temperature data to obtain the temperature gradient data specifically includes:

[0017] Determine the immediate neighbors of each battery cell;

[0018] Calculate the instantaneous temperature difference between each battery cell and the directly adjacent cell at preset time intervals within a preset sampling period;

[0019] Generate a temperature change sequence according to the instantaneous temperature difference, and calculate the change rate and fluctuation amplitude of the temperature change sequence;

[0020] The instantaneous temperature difference is weighted based on the rate of change and fluctuation amplitude to obtain the temperature gradient data.

[0021] By adopting the above technical solution, the directly adjacent cells of each battery cell are identified and the instantaneous temperature difference is calculated within the preset sampling time period to obtain basic data reflecting the local heat conduction characteristics. By generating a temperature change sequence and analyzing its change rate and fluctuation amplitude, the dynamic change characteristics of the temperature gradient are captured. By adopting a weighted processing method based on the change rate and fluctuation amplitude, the temperature gradient data reflects both the absolute size of the temperature difference and the dynamic trend of the temperature change, reducing the limitation of using only static temperature differences to reflect the heat accumulation effect, and making the heat propagation risk assessment more accurate. By introducing the analysis of the time dimension, the ability to predict potential thermal runaway risks is improved, providing a more reliable basis for subsequent safety management and control.

[0022] In combination with some embodiments of the first aspect, in some embodiments, calculating the power fluctuation characteristics of the heat source battery cell according to the voltage data and current data of the heat source battery cell specifically includes:

[0023] The voltage data and current data of the heat source battery cells are formed into a voltage sequence and a current sequence in chronological order;

[0024] Calculate the changes of voltage sequence and current sequence at adjacent moments to obtain voltage change and current change;

[0025] Calculate the power variation trend according to the voltage variation and current variation to obtain the power fluctuation curve;

[0026] Analyze the peaks, troughs and duration of the power fluctuation curve to determine the power fluctuation characteristics.

[0027] By adopting the above technical solution, the voltage and current data of the heat source battery cells are organized in chronological order, and the changes at adjacent moments are calculated to obtain a complete data sequence reflecting the dynamic characteristics of power. The peaks, troughs and duration of the power fluctuation curve are analyzed to reveal the energy conversion law of the heat source battery cells. By analyzing the power change trend, the dynamic process of heat generation is accurately grasped, providing more comprehensive characteristic information for assessing the risk of heat propagation. This method establishes the correlation between heat generation and power fluctuation, and improves the accuracy of thermal runaway risk identification.

[0028] In combination with some embodiments of the first aspect, in some embodiments, adjusting the charge and discharge current of the key battery cell and all battery cells within a preset range of the key battery cell according to the current power dynamic limit interval specifically includes:

[0029] Determine the upper and lower limits of charge and discharge current for each battery cell according to the current power dynamic limit range;

[0030] Establish a current regulation optimization model with temperature balance as the goal, and use the upper and lower limits of charge and discharge current as constraints;

[0031] The battery cells are grouped in a hierarchical and progressive manner to obtain a number of battery cell groups;

[0032] The target current value of each battery cell group is calculated based on the optimization model, and the current value of each battery cell group is adjusted to the corresponding target current value according to a preset step size.

[0033] By adopting the above technical solutions, a dynamic current power limit range is established to determine the safe boundary of the charge and discharge current, and a current regulation optimization model with temperature balance as the goal is constructed. The battery cells are managed in a hierarchical and progressive grouping manner, realizing differentiated control of battery cells with different risk levels. The target current value of each battery cell group is calculated through the optimization model, and adjusted according to the preset step size, which improves the stability of the current regulation process. By reasonably setting the adjustment step size, the impact of current mutation on battery performance is reduced, and the service life and operational reliability of the battery pack are improved.

[0034] In combination with some embodiments of the first aspect, in some embodiments, the current regulation optimization model takes the weighted sum of the temperature balance objective function, the power fluctuation objective function and the current change rate objective function as the optimization objective, the temperature balance objective function is the sum of the squares of the differences between the temperature of each battery cell and the average temperature, the power fluctuation objective function is the sum of the squares of the power changes at each moment, and the current change rate objective function is the sum of the squares of the current changes of each battery cell at adjacent moments. The current regulation optimization model uses the upper limit of the charge and discharge current, the lower limit of the charge and discharge current, the maximum allowable temperature difference between adjacent battery cells, the maximum allowable current change rate and the total power balance of the battery pack as constraints, and inputs the real-time temperature data, temperature gradient data, current charge and discharge current value, current voltage value and ambient temperature data of each battery cell into the current regulation optimization model to obtain the target current regulation value, current regulation priority and regulation timing arrangement of each battery cell group.

[0035] By adopting the above technical scheme, a current regulation optimization model is established with the weighted sum of the temperature balance objective function, the power fluctuation objective function and the current change rate objective function as the optimization objective. The sum of the squares of the differences between the temperature of each battery cell and the average temperature is used as the temperature balance objective function, the sum of the squares of the power changes at each moment is used as the power fluctuation objective function, and the sum of the squares of the current changes of each battery cell at adjacent moments is used as the current change rate objective function. At the same time, considering the upper and lower limits of the charge and discharge current, the maximum allowable temperature difference between adjacent battery cells, the maximum allowable current change rate and the total power balance of the battery pack and other constraints, it is possible to achieve a multi-objective trade-off between temperature balance, power stability and current smoothness when adjusting the current.

[0036] In combination with some embodiments of the first aspect, in some embodiments, after adjusting the charge and discharge current of the key battery cell and all battery cells within a preset range of the key battery cell according to the current power dynamic limit interval, the method further includes:

[0037] Obtain the safety monitoring data of the battery pack, including the temperature rise rate, voltage change rate and internal resistance change of each battery cell;

[0038] Calculate the temperature anomaly index according to the temperature rise rate, calculate the voltage anomaly index according to the voltage change rate, and calculate the internal resistance anomaly index according to the internal resistance change;

[0039] The temperature anomaly index, voltage anomaly index and internal resistance anomaly index are weighted and combined to obtain a comprehensive safety index;

[0040] According to the changing trend of the comprehensive safety index, the range of the current power dynamic limit interval is adjusted.

[0041] By adopting the above technical solution, the safety monitoring data such as the temperature rise rate, voltage change rate and internal resistance change of each battery cell in the battery pack are obtained, and the temperature abnormality index, voltage abnormality index and internal resistance abnormality index are calculated respectively. These abnormality indexes are weighted and combined to obtain a comprehensive safety index, which can fully reflect the safety status of the battery cell. The range of the dynamic current and power limit interval is dynamically adjusted according to the change trend of the comprehensive safety index, making the current and power limit more adaptive and forward-looking, and the operating parameters can be adjusted in time at the early stage of the abnormal state. The system can actively prevent safety risks while ensuring performance, improve the safety and reliability of the system, and reduce the probability of safety accidents in the battery pack.

[0042] In combination with some embodiments of the first aspect, in some embodiments, adjusting the range of the current power dynamic limit interval according to the change trend of the comprehensive safety index specifically includes:

[0043] Calculate the rate of change and fluctuation of the comprehensive safety index within a preset time window;

[0044] Determine the security risk level based on the rate of change and fluctuation;

[0045] Set the current power limit coefficient according to the safety risk level;

[0046] The current power limit coefficient is applied to the upper and lower limits of the current power dynamic limit range.

[0047] By adopting the above technical solution, the rate of change and fluctuation of the comprehensive safety index within the preset time window are calculated, the safety risk level is determined according to the dynamic characteristics, and the corresponding current power limit coefficient is set according to different risk levels. The limit coefficient is applied to the upper and lower limits of the current power dynamic limit interval, realizing the refined hierarchical management of the current power limit. This adaptive adjustment method based on the safety risk level enables the system to take restriction measures of corresponding intensity according to the degree of risk, reducing the impact on normal operation while ensuring safety. By dynamically adjusting the restriction coefficient, the system can shrink the restriction interval in time to strengthen protection when the risk increases, and appropriately relax the restriction to improve performance when the risk decreases, which not only reduces the performance loss caused by overly conservative restrictions, but also reduces the safety hazards caused by insufficient restrictions.

[0048] In the second aspect, an embodiment of the present application provides a BMS-based battery pack safety management system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0049] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0051] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0052] 1. The present application provides a battery pack safety management method based on BMS, which collects the temperature, voltage and current data of the battery cells, calculates the temperature gradient data and determines the direction of heat diffusion, and can accurately locate the position of the heat source battery cells. By analyzing the degree of influence of the heat source battery cells on the adjacent battery cells in combination with the power fluctuation characteristics of the heat source battery cells, the risk of heat propagation can be quantitatively assessed. Based on the propagation risk value, the safe area is divided and the key battery cells in the high-risk area are identified, and a targeted current power dynamic limit interval is established, so as to achieve precise regulation of the charging and discharging current of the key battery cells and their surrounding cells, reduce the over-protection problem caused by the use of a unified restriction strategy for the entire battery pack, and improve the charging and discharging performance of the battery pack while ensuring safety. By establishing a safe buffer distance, a dynamic protection barrier is created for high-risk areas, slowing the spread of thermal runaway in the battery pack and improving the safety and reliability of the battery pack.

[0053] 2. The present application provides a battery pack safety management method based on BMS, which obtains safety monitoring data such as the temperature rise rate, voltage change rate and internal resistance change of each battery cell in the battery pack, calculates the temperature anomaly index, voltage anomaly index and internal resistance anomaly index respectively, and weightedly combines these anomaly indexes to obtain a comprehensive safety index, which can comprehensively reflect the safety status of the battery cell. The range of the current power dynamic limit interval is dynamically adjusted according to the changing trend of the comprehensive safety index, so that the current power limit is more adaptive and forward-looking, and the operating parameters can be adjusted in time at the early stage of the abnormal state. The system can actively prevent safety risks while ensuring performance, improve the safety and reliability of the system, and reduce the probability of safety accidents in the battery pack.

[0054] 3. The present application provides a battery pack safety management method based on BMS, which calculates the rate of change and fluctuation of the comprehensive safety index within a preset time window, determines the safety risk level according to dynamic characteristics, and sets the corresponding current power limit coefficient according to different risk levels. The limit coefficient is applied to the upper and lower limits of the current power dynamic limit interval, thereby realizing refined hierarchical management of the current power limit. This adaptive adjustment method based on the safety risk level enables the system to take restriction measures of corresponding intensity according to the degree of risk, reducing the impact on normal operation while ensuring safety. By dynamically adjusting the restriction coefficient, the system can shrink the restriction interval in time to strengthen protection when the risk increases, and appropriately relax the restriction to improve performance when the risk decreases, which not only reduces the performance loss caused by overly conservative restrictions, but also reduces the safety hazards caused by insufficient restrictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a battery pack safety management method based on BMS in an embodiment of the present application.

[0056] Figure 2 It is a flow chart of a dynamic adjustment method based on multi-dimensional security indicators in an embodiment of the present application.

[0057] Figure 3 It is a schematic diagram of the physical device structure of a battery pack safety management system based on BMS provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0059] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0060] The following uses an embodiment and combines Figure 1 , a battery pack safety management method based on BMS in an embodiment of the present application is described:

[0061] See also Figure 1 , which is a flow chart of a battery pack safety management method based on BMS in an embodiment of the present application.

[0062] S101, obtaining temperature data, voltage data and current data of each battery cell in the battery pack within a preset sampling time period;

[0063] This step is to monitor and sample the key parameters of each battery cell in the battery pack in real time. The system continuously collects the temperature, voltage and current data of each battery cell at a certain time interval through sensors or measuring devices to form time series data reflecting the changes in the state of the battery cell. The preset sampling time period is reasonably set according to the working characteristics and application scenarios of the battery pack, and it is necessary to balance data accuracy and calculation efficiency.

[0064] The system can integrate various types of sensors, such as temperature sensors such as thermocouples and thermistors, Hall effect current sensors, and voltage sampling circuits. These sensors are closely connected to each battery cell to obtain raw measurement data in real time. At the same time, the system also needs to be equipped with a data acquisition module to filter, amplify, A / D convert and process the sensor data, and transmit the digital data to the controller or storage medium. The controller can periodically read and save the measurement data of each battery cell according to the preset sampling frequency and time period to build a monitoring data set.

[0065] S102, calculating the temperature difference between each battery cell and an adjacent battery cell according to the temperature data to obtain temperature gradient data;

[0066] The system calculates the temperature difference between each battery cell and the adjacent battery cell based on the temperature data to obtain the temperature gradient data. Specifically: determine the directly adjacent cells of each battery cell;

[0067] Calculate the instantaneous temperature difference between each battery cell and the directly adjacent cell at preset time intervals within a preset sampling period;

[0068] Generate a temperature change sequence according to the instantaneous temperature difference, and calculate the change rate and fluctuation amplitude of the temperature change sequence;

[0069] The instantaneous temperature difference is weighted based on the rate of change and fluctuation amplitude to obtain the temperature gradient data.

[0070] The system calculates the temperature difference between each battery cell and its directly adjacent cells to obtain data reflecting the temperature gradient of the battery pack. The temperature difference is a key indicator of the direction and intensity of heat transfer between adjacent cells. A larger temperature difference means that heat flows from the high temperature area to the low temperature area. By tracking the time change of the temperature gradient, the heat dissipation performance and thermal imbalance risk of the battery pack can be evaluated.

[0071] The system first needs to determine the direct adjacent cells of each battery cell according to the topological structure of the battery pack and construct a cell adjacency table. Then, the system calculates the instantaneous temperature difference of each pair of adjacent cells at a preset time interval (such as 1 second) to form a time series of temperature differences. Considering the dynamic characteristics of temperature changes, the system also needs to perform smoothing filtering on the time series to remove high-frequency noise and transient interference. In addition, the system can further calculate the first-order derivative and second-order derivative of the temperature difference series to obtain the rate of change and acceleration of the temperature gradient as auxiliary indicators for evaluating the thermal diffusion process. Finally, the system uses weighted averaging or other strategies to integrate the temperature difference values ​​at multiple times to obtain stable and reliable temperature gradient data.

[0072] S103, determining the heat diffusion direction between the battery cells according to the temperature gradient data, and determining the heat source battery cell where the heat is accumulated according to the heat diffusion direction;

[0073] The system determines the direction of local heat flow by judging the sign of the temperature difference between each pair of adjacent battery cells. A positive temperature difference corresponds to heat transfer from a high-temperature cell to a low-temperature cell. On this basis, the system comprehensively analyzes all temperature difference data and identifies the area where the heat eventually converges, namely the heat source battery cell. The heat source cell usually has the highest temperature and transfers heat in multiple directions, and is a high-risk cell that triggers thermal runaway.

[0074] The system first makes a sign judgment on the temperature difference data of each pair of adjacent battery cells. If the temperature difference is positive, the heat flows from the first cell to the second cell, and if the temperature difference is negative, the direction is opposite. By iteratively analyzing all the temperature difference data of the battery pack, the system can obtain a heat flow direction diagram, which intuitively represents the heat absorption and heat emission of each battery cell. Based on the heat flow direction diagram, the system uses graph theory algorithms, such as the shortest path, minimum spanning tree, etc., to search for the heat transfer path to each cell. The end point where the path converges corresponds to the heat source cell. In addition, the system can also calculate the thermal aggregation factor of each cell, taking into account the incoming heat and the outgoing heat. The cell with the largest balance factor is also the heat source cell.

[0075] The actual thermal distribution of the battery pack is relatively complex, and there may be multiple heat source monomers, forming local high points of heat. In order to comprehensively assess the risk of thermal runaway, the system needs to scan and identify all heat accumulation areas and establish a heat source distribution map. In addition, the location of the heat source monomer may dynamically migrate as the operating conditions change, and it is necessary to track the evolution trend of the heat source area in real time. The system can introduce a thermal network model based on the energy balance equation, combined with temperature field simulation technology, to dynamically estimate the heat redistribution process within the battery pack and perceive the changes in heat sources in advance. At the same time, the system can also use big data analysis and machine learning technology to explore the correlation between the historical operating conditions of the battery pack and the distribution of heat sources, establish a heat accumulation risk assessment model, and assist in safety management decisions.

[0076] S104, calculating the power fluctuation characteristics of the heat source battery cells according to the voltage data and current data of the heat source battery cells;

[0077] The system calculates the power fluctuation characteristics of the heat source battery cells according to the voltage data and current data of the heat source battery cells. Specifically, the voltage data and current data of the heat source battery cells are combined into a voltage sequence and a current sequence in time sequence;

[0078] Calculate the changes of voltage sequence and current sequence at adjacent moments to obtain voltage change and current change;

[0079] Calculate the power variation trend according to the voltage variation and current variation to obtain the power fluctuation curve;

[0080] Analyze the peaks, troughs and duration of the power fluctuation curve to determine the power fluctuation characteristics.

[0081] The system calculates the instantaneous power time series of the heat source monomer to characterize the power fluctuation during its charging and discharging process. The power fluctuation characteristics reflect the intensity and instability of the energy conversion of the battery monomer, which is closely related to the triggering of thermal runaway events. By comparing and analyzing the power fluctuation differences between the heat source monomer and other normal monomers, high-risk monomers can be identified and their thermal runaway risk level can be assessed.

[0082] The system first calculates the instantaneous power value at each sampling moment according to the formula P=U×I based on the current and voltage sampling data of the heat source battery cells, and generates a power time series. Then, the system performs feature extraction and fluctuation analysis on the power series and calculates the following indicators:

[0083] Power average: reflects the average charge and discharge level of the monomer;

[0084] Power variance: reflects the discrete degree and fluctuation range of single power;

[0085] Power peak: reflects the maximum pulse power that the monomer can withstand;

[0086] Power slope: reflects the speed and intensity of the change of monomer power;

[0087] Power spectrum: reflects the periodic characteristics and dominant frequency of single-cell power fluctuations.

[0088] S105, combining the temperature gradient data and the power fluctuation characteristics, determining the influence of the heat source battery cell on the adjacent battery cells, and obtaining a propagation risk value;

[0089] The system determines the diffusion trend and propagation intensity of thermal runaway in a local area by analyzing the temperature difference between the heat source monomer and the adjacent monomers and the synchronization of power fluctuations. The propagation risk value is a quantitative indicator that comprehensively considers the heat diffusion speed, heat accumulation effect and power instability degree, and describes the possibility of a heat source monomer triggering a chain thermal runaway reaction.

[0090] The system first quantifies and scores the temperature difference data between the heat source monomer and each adjacent monomer. The larger the temperature difference, the higher the score. Then, the system calculates the correlation coefficient of the power fluctuation curve between the heat source monomer and the adjacent monomer to evaluate their degree of coupling in the time dimension. The stronger the correlation, the higher the score. On this basis, the system weightedly sums the temperature difference score and the power correlation score to obtain the propagation risk value. The weight coefficient can be set according to the statistical laws of the heat diffusion mechanism and the out-of-control cases. Finally, the system compares the propagation risk value with the pre-set safety threshold. The heat source monomer that exceeds the threshold is judged as a high-risk monomer and needs to be controlled and isolated as a priority.

[0091] Under actual working conditions, the propagation path of thermal runaway is relatively complex. In addition to heat diffusion and power coupling, there may also be factors such as electrolyte leakage and gas convection. In order to comprehensively assess the risk of propagation, the system can integrate more physical quantity sensors, such as pressure, gas concentration, strain, etc., in addition to temperature and power data, to build a multi-physics field coupling analysis model. In addition, the system can also use a thermal network method based on the law of conservation of energy to characterize the heat flow of the entire battery pack, considering the indirect heat transfer effect between non-adjacent cells. At the same time, the system can introduce Monte Carlo simulation technology to evaluate the propagation path and risk distribution under different initial conditions and boundary conditions through random sampling and probability analysis, thereby improving the universality and predictability of risk assessment.

[0092] S106, dividing the safe area of ​​the battery pack based on the propagation risk value, and marking the area where the battery cells whose propagation risk value exceeds a preset threshold are located as a high-risk area;

[0093] The system performs spatial cluster analysis on risk values ​​and divides adjacent monomers with similar risk levels into the same safe area. For areas containing high-risk monomers and with generally high risk values ​​of internal monomers, the system marks them as high-risk areas, representing potential sources of thermal runaway. The size and shape characteristics of high-risk areas reflect the impact range and diffusion trend of thermal runaway.

[0094] The system first normalizes the risk values ​​of all monomers and maps them to the interval of 0-1. Then, the system uses spatial clustering algorithms, such as DBSCAN, OPTICS, etc., to automatically group monomers with similar risk value distributions to form several safe areas. The clustering algorithm needs to set appropriate distance measurement functions and density thresholds to ensure the rationality of regional division. Next, the system calculates the average risk value of each area and sets a risk threshold (such as 0.8). For areas where the average risk value exceeds the threshold, the system marks them as high-risk areas, and generates regional boundaries and risk contour maps to intuitively display the spatial distribution and risk gradient of high-risk areas.

[0095] S107, screening key battery cells in high-risk areas whose charge and discharge depths exceed a preset safety threshold;

[0096] The system compares and analyzes the temperature and power fluctuations of each cell in the area to find the representative cell with the most dramatic changes and the highest risk. The representative cell is usually the central node of the high-risk area, which plays a role in amplification and conduction in the process of thermal runaway and is the object of key monitoring and control. By tracking the status changes of the representative cell, the safety trend of the entire high-risk area can be perceived in a timely manner.

[0097] The system first extracts the temperature and power fluctuation characteristic sequences of each battery cell in the high-risk area. Then, the system dynamically analyzes these characteristic sequences, focusing on the following indicators:

[0098] Short-time temperature rise rate: reflects the rate of increase of monomer temperature;

[0099] Second-order temperature derivative: reflects the acceleration trend of monomer temperature change;

[0100] Power short-term variance: reflects the severity of short-term fluctuations in single-cell power;

[0101] Power change slope: reflects the rate of increase or decrease of single power.

[0102] The system uses a sliding time window method to calculate these indicators and obtain a score sequence that reflects the dynamic changes of the monomer. Then, the system performs a weighted fusion on the score sequence of each indicator to obtain a comprehensive risk dynamic score for each monomer. Finally, the system selects the monomer with the highest comprehensive score as the representative monomer and generates a risk profile for the monomer, indicating the change curves of its key parameters such as temperature and power.

[0103] Under actual working conditions, the determination of representative monomers also needs to consider their relative position and communication influence within the high-risk area. Nodes located in the center of the region and maintaining a strong coupling relationship with most monomers are more suitable as representative monomers. In order to quantify the network centrality and communication ability of monomers, the system can refer to indicators in complex network theory, such as betweenness centrality, PageRank value, etc., to characterize the importance of monomers in the risk propagation network. In addition, the screening of representative monomers must also take into account the full-time domain characteristics and local characteristics, that is, the risk level is high throughout the period and the risk changes dramatically within the local time window. The system can use multi-time scale analysis methods, such as wavelet transform, to extract risk characteristics at different scales. At the same time, the system can also use online learning algorithms to update representative monomers in real time, and adaptively adjust the selection criteria of representative monomers according to the dynamic adjustment of risk distribution in the region to improve the sensitivity of risk perception.

[0104] S108, determining a safety buffer distance according to the position of the key battery cell, and establishing a current power dynamic limit interval according to the safety buffer distance;

[0105] The system continuously collects temperature and power data of representative monomers and inputs them into a pre-built safety prediction model for calculation, dynamically evaluating the probability of thermal runaway of the monomers in the future. The safety prediction model is trained based on a large amount of historical accident data and simulation test data, and can establish a nonlinear mapping relationship between temperature, power characteristics and thermal runaway risks. This model can quantitatively evaluate the safety status of representative monomers and infer the safety trend of the entire high-risk area.

[0106] The system adopts a rolling prediction method, dynamically collects temperature and power data representing the monomer at a certain time interval (such as 1 second) to form a data window reflecting the change of the monomer state. Then, the system performs feature engineering on the data window, extracts multi-dimensional features of temperature and power, such as mean, variance, slope, peak, etc., and generates a feature vector. Next, the system inputs the feature vector into the pre-established safety prediction model for calculation. The model can use a variety of machine learning algorithms, such as logistic regression, decision tree, support vector machine, etc., or deep learning algorithms, such as long short-term memory network (LSTM), convolutional neural network (CNN), etc. The output result of the model is the probability value of thermal runaway of the representative monomer in the future (such as the next 30 seconds). If the probability value exceeds the preset danger threshold (such as 0.7), the system determines that the representative monomer is at risk of thermal runaway and triggers the warning signal in time.

[0107] S109, regulating the charge and discharge current of the key battery cell and all battery cells within a preset range of the key battery cell according to the current power dynamic limit interval.

[0108] The system adjusts the charge and discharge current of the key battery cell and all battery cells within the preset range of the key battery cell according to the current power dynamic limit interval. Specifically: the upper and lower limits of the charge and discharge current of each battery cell are determined according to the current power dynamic limit interval;

[0109] Establish a current regulation optimization model with temperature balance as the goal, and use the upper and lower limits of charge and discharge current as constraints;

[0110] The battery cells are grouped in a hierarchical and progressive manner to obtain a number of battery cell groups;

[0111] The target current value of each battery cell group is calculated based on the optimization model, and the current value of each battery cell group is adjusted to the corresponding target current value according to a preset step size.

[0112] Among them, the current regulation optimization model takes the weighted sum of the temperature balance objective function, the power fluctuation objective function and the current change rate objective function as the optimization objective. The temperature balance objective function is the sum of the squares of the differences between the temperature of each battery cell and the average temperature. The power fluctuation objective function is the sum of the squares of the power changes at each moment. The current change rate objective function is the sum of the squares of the current changes of each battery cell at adjacent moments. The current regulation optimization model uses the upper limit of charge and discharge current, the lower limit of charge and discharge current, the maximum allowable temperature difference between adjacent battery cells, the maximum allowable current change rate and the total power balance of the battery pack as constraints. The real-time temperature data, temperature gradient data, current charge and discharge current value, current voltage value and ambient temperature data of each battery cell are input into the current regulation optimization model to obtain the target current regulation value, current regulation priority and regulation timing arrangement of each battery cell group.

[0113] The system first determines the maximum charging current and maximum discharging current allowed for each battery cell based on the dynamic current power limit range. Then, the system establishes a current regulation model with the optimization goal of achieving battery pack temperature balance, and uses the charge and discharge current limit as a constraint of the model. Next, the system divides the battery cells into several groups in a hierarchical and progressive manner, and calculates the target current value of each group of battery cells based on the optimization model. Finally, the system gradually adjusts the actual charge and discharge current of each group of battery cells to the target value according to the preset adjustment step. Through the above process, the temperature of each battery cell can be made as close as possible, the temperature gradient can be reduced, and the spread of high-temperature hot spots can be suppressed while ensuring battery safety.

[0114] In the above embodiment, the temperature, voltage and current data of the battery cell are collected, the temperature gradient data is calculated and the heat diffusion direction is determined, so that the position of the heat source battery cell can be accurately located. By analyzing the degree of influence of the heat source battery cell on the adjacent battery cells in combination with the power fluctuation characteristics of the heat source battery cell, the risk of heat propagation can be quantitatively evaluated. Based on the propagation risk value, the safe area is divided and the key battery cells in the high-risk area are identified, and a targeted current power dynamic limit interval is established, so as to achieve precise regulation of the charging and discharging current of the key battery cells and their surrounding cells, reduce the over-protection problem caused by the use of a unified restriction strategy for the entire battery pack, and improve the charging and discharging performance of the battery pack while ensuring safety. By establishing a safe buffer distance, a dynamic protection barrier is created for the high-risk area, which slows down the spread of thermal runaway in the battery pack and improves the safety and reliability of the battery pack.

[0115] In the above embodiment, the temperature, voltage and current characteristics of each battery cell in the battery pack are analyzed to achieve safety management based on heat propagation risk. In order to further improve the real-time and adaptability of battery pack safety management, the present application also provides a dynamic adjustment method based on multi-dimensional safety indicators. This method effectively supplements and optimizes the above embodiment by monitoring the safety status of the battery pack in real time and dynamically adjusting the current power limitation strategy. Figure 2 , a dynamic adjustment method based on multi-dimensional security indicators in an embodiment of the present application is described:

[0116] See also Figure 2 , which is a flow chart of a dynamic adjustment method based on multi-dimensional security indicators in an embodiment of the present application.

[0117] S201, obtaining safety monitoring data of the battery pack;

[0118] The system obtains the safety monitoring data of the battery pack, which includes the temperature rise rate, voltage change rate and internal resistance change of each battery cell.

[0119] The system collects various safety indicator data of the battery pack in real time through sensors and monitoring circuits. These safety indicators reflect the health status and potential failure risks of the battery cells, and are the basis for safety analysis and risk warning. The system focuses on key parameters such as temperature change, voltage change and internal resistance change of the battery cells. Among them, the temperature rise rate characterizes the heating level and thermal runaway risk of the battery cells, the voltage change rate characterizes the charging and discharging state and decay degree of the battery cells, and the internal resistance change reflects the conductivity and loss level of the battery cells. By comprehensively analyzing the changing trends and abnormalities of these indicators, the safety status of the battery pack can be comprehensively evaluated.

[0120] S202, calculating a temperature anomaly index according to a temperature rise rate, calculating a voltage anomaly index according to a voltage change rate, and calculating an internal resistance anomaly index according to an internal resistance change;

[0121] Based on the acquired safety monitoring data, the system calculates the degree of abnormality of each safety indicator and obtains the corresponding abnormality index. The abnormality index quantitatively describes the severity of the deviation of the current indicator value from the normal working range, and provides a quantitative basis for the comprehensive evaluation of the safety status of the battery pack. The system calculates the temperature abnormality index, voltage abnormality index and internal resistance abnormality index respectively, reflecting the abnormal risks of the battery monomer in terms of heat generation, charging and discharging, and conductivity. The temperature abnormality index is determined by the temperature rise rate. The higher the temperature rise rate, the greater the risk of poor heat dissipation or thermal runaway of the battery monomer, and the higher the index value. The voltage abnormality index is determined by the voltage change rate. The greater the voltage change rate, the more violent the fluctuation of the battery monomer charging and discharging state or the more serious the decline, and the higher the index value. The internal resistance abnormality index is determined by the change of internal resistance. The greater the change of internal resistance, the lower the conductivity of the battery monomer or the more severe the loss, and the higher the index value.

[0122] In the specific implementation, the system can establish a mapping relationship between the temperature rise rate, voltage change rate, internal resistance change and abnormal risk level for different battery types and application scenarios. For example, multiple warning thresholds for the temperature rise rate can be set according to the thermal stability and thermal management capabilities of the battery, and the temperature rise rate can be mapped to a temperature anomaly index between 0 and 1 using a fuzzy membership function. Similarly, the warning threshold for the voltage change rate can be set according to the rated voltage range and charge and discharge rate of the battery, and mapped to the voltage anomaly index. For the internal resistance anomaly index, the probability distribution of the internal resistance change can be obtained through statistical analysis of a large amount of internal resistance test data, and the internal resistance anomaly level can be determined using percentiles and other methods.

[0123] S203, performing weighted combination of the temperature anomaly index, the voltage anomaly index and the internal resistance anomaly index to obtain a comprehensive safety index;

[0124] In this step, the system integrates the abnormal indexes of multiple safety indicators into a comprehensive safety index in order to quantitatively evaluate and compare the overall safety status of the battery pack. The comprehensive safety index adopts a weighted summation method, integrating abnormal risk information in multiple dimensions such as temperature, voltage, and internal resistance, which can more comprehensively reflect the safety level of the battery pack. The system reasonably allocates weight coefficients according to the degree of influence of each abnormal index on battery safety. For example, since temperature anomalies pose the most serious threat to battery safety, the weight coefficient of the temperature anomaly index is usually the highest. Voltage anomalies are second, and the weight coefficient of internal resistance anomalies is relatively small. Through weighted combination, the comprehensive safety index can take into account multiple safety indicators, reflect their relative importance, and provide a unified reference standard for subsequent safety decisions.

[0125] In the specific implementation, the system can use methods such as hierarchical analysis method and entropy weight method to determine the weight coefficient of each abnormal index based on expert experience and historical data. For example, experts can be organized to compare the importance of each indicator in pairs, construct a judgment matrix, and calculate the weight vector through matrix operations. The system can also use the concept of information entropy to adaptively adjust the weight according to the degree of variation of each indicator. When the abnormal change of an indicator is large, its contribution to the comprehensive safety index should increase accordingly. In addition, the system can also consider factors such as the design parameters, process level, and operating conditions of the battery pack, and set the selection range and default value of the weight coefficient for different application scenarios. Users can also manually adjust the weight coefficient according to actual needs to customize a personalized comprehensive safety index.

[0126] S204, calculating the change rate and fluctuation range of the comprehensive safety index within a preset time window;

[0127] In this step, the system further analyzes the dynamic change characteristics of the comprehensive safety index, including the rate of change and the amplitude of fluctuation, to evaluate the development trend and stability of the safety status of the battery pack. The rate of change indicates the rate of change of the comprehensive safety index per unit time, reflecting the rate of deterioration or improvement of the safety level of the battery pack. The amplitude of fluctuation indicates the range of fluctuations of the comprehensive safety index over a period of time, reflecting the severity of fluctuations in the safety level of the battery pack. The system selects a suitable time window, such as 10 minutes, 30 minutes, etc., and calculates the difference and standard deviation of the comprehensive safety index within the window as measures of the rate of change and the amplitude of fluctuation, respectively. By tracking the rate of change and the amplitude of fluctuation, sudden changes and abnormal fluctuations in the safety status of the battery pack can be discovered in a timely manner, providing a basis for safety warnings and decision-making.

[0128] In the specific implementation, the system can use a sliding window method to update the change rate and fluctuation range of the comprehensive safety index in real time. When new monitoring data arrives, the system adds it to the time window, removes the earliest data, and keeps the window length unchanged. Then, the system calculates the updated change rate and fluctuation range based on the data sequence in the window. In order to smooth the impact of short-term fluctuations, the system can also perform exponential smoothing on the change rate and fluctuation range to obtain a robust estimate of the long-term trend. In addition, the system can adaptively adjust the length of the time window according to factors such as the working condition and health status of the battery pack. For example, when the battery pack is under harsh working conditions such as high load and high temperature, the time window can be appropriately narrowed to improve the sensitivity of anomaly detection. When the performance of the battery pack declines to a certain extent, the time window can be expanded to reduce the interference of indicator fluctuations on abnormal judgment.

[0129] S205. Determine the security risk level according to the change rate and fluctuation range;

[0130] In this step, the system determines the safety risk level of the battery pack based on the change rate and fluctuation range of the comprehensive safety index. The safety risk level divides the severity of the battery pack's safety status, such as normal, alert, dangerous, etc., to facilitate the adoption of corresponding safety precautions. The system sets multiple thresholds for the change rate and fluctuation range, corresponding to different risk levels. When the change rate or fluctuation range exceeds a certain threshold, the safety risk level will increase. For example, when the change rate exceeds 5% / minute, or the fluctuation range exceeds 10%, the risk level rises from normal to alert. When the change rate continues to increase to 10% / minute, or the fluctuation range expands to 20%, the risk level further increases to dangerous. The system monitors the change rate and fluctuation range in real time, dynamically evaluates the safety risk level, and promptly warns of the deterioration trend of the battery pack's safety status, providing a basis for safety management and control decisions.

[0131] In the specific implementation, the system can reasonably set the thresholds of the change rate and fluctuation range according to the design parameters, process characteristics, application scenarios and other factors of the battery pack. The selection of the threshold needs to balance between sensitivity and reliability to avoid too many false positives or false negatives. The system can summarize the dangerous critical values ​​of the change rate and fluctuation range through a large amount of experimental data and failure cases, and perform cross-validation and iterative optimization. At the same time, the system must also consider the joint impact of the change rate and fluctuation range. For example, when the change rate and fluctuation range are at medium levels respectively, their combination may represent a higher risk level. Therefore, the system can use fuzzy reasoning, Bayesian network and other methods to establish a nonlinear mapping relationship between the change rate and fluctuation range and the risk level, and comprehensively consider the interaction of the two indicators. The system can also design a multi-level risk level system to refine the description and judgment of the safety status and provide more specific guidance for subsequent decision-making and control.

[0132] S206, setting a current power limit coefficient according to the safety risk level;

[0133] In this step, the system dynamically sets the current power limit coefficient according to the identified safety risk level to impose safety constraints on the charging and discharging process of the battery pack. The current power limit coefficient is a value between 0 and 1, which indicates the ratio of the allowed charging and discharging current or power of the battery pack to the rated value. The smaller the coefficient, the higher the safety risk of the battery pack, and the greater the restriction on its charging and discharging capacity. The system pre-sets the corresponding relationship between the risk level and the limit coefficient. For example, at the normal risk level, the limit coefficient can be set to 1, that is, no additional restrictions are imposed on charging and discharging. When the risk level rises to the warning level, the limit coefficient may be lowered to 0.8, that is, the maximum charging and discharging current or power cannot exceed 80% of the rated value. If the risk level further deteriorates to danger, the limit coefficient drops to 0.5, that is, the allowed operating current or power of the battery pack is halved. By setting the corresponding current power limit coefficient at different risk levels, the safety risk of the battery pack can be suppressed from further escalation, and dangerous situations such as overcharging, overdischarging, and overheating can be avoided.

[0134] In the specific implementation, the system needs to comprehensively consider the design parameters, safety margin, working condition requirements and other factors of the battery pack, and reasonably configure the restriction coefficients under different risk levels. On the one hand, the setting of the restriction coefficient should be strict enough to ensure that the charging and discharging intensity is reduced in time under high-risk conditions to prevent safety accidents. On the other hand, the restriction coefficient should not be too conservative, so as not to affect the normal performance of the battery pack too much. Therefore, the system can optimize the matching relationship between the risk level and the restriction coefficient through a large number of simulation tests and experimental verifications, and strike a balance between safety and availability. At the same time, the system can also consider factors such as the health status and remaining life of the battery pack to adaptively adjust the restriction coefficient. For example, when the battery pack is in a high-risk operating state for a long time, or its performance indicators such as capacity and internal resistance are significantly attenuated, the system can increase the constraint strength of the restriction coefficient accordingly to provide the battery pack with more safety protection margin.

[0135] S207 . Apply the current power limit coefficient to the upper limit and the lower limit of the current power dynamic limit interval.

[0136] In this step, the system combines the current power limit coefficient corresponding to the safety risk level with the original current power dynamic limit interval of the battery pack to achieve safety constraint control of the charging and discharging process. The current power dynamic limit interval represents the current or power range allowed for the battery pack to work in the current state, corresponding to the upper and lower limits of the charging and discharging capacity. The system calculates the limit interval in real time based on the design parameters, health status, SOC / SOH and other factors of the battery pack. At the same time, the system dynamically adjusts the upper and lower limits of the limit interval according to the current power limit coefficient obtained in the previous steps. The system multiplies the original upper and lower limits by the limit coefficient to obtain the adjusted limit boundary. In the subsequent charging and discharging control, the working current or power of the battery pack must be strictly limited to the adjusted limit interval. Once the limit interval is exceeded, the system will immediately take protection measures such as current limiting and current interruption to avoid the safety risk of the battery pack from getting out of control. By introducing the limit coefficient related to the risk level into the adjustment of the dynamic limit interval, an adaptive hierarchical limit mechanism can be formed to achieve the optimal safety protection strategy under the real-time working condition constraints of the battery pack.

[0137] In the specific implementation, the system needs to establish a sound calculation model for the dynamic limit range of current and power, comprehensively consider the influencing factors such as battery capacity, internal resistance, SOC, temperature, etc., and update it in real time. At the same time, when the limit coefficient is applied to the limit range, the system needs to perform necessary boundary checks and constraint processing to avoid the problem of the adjusted limit range crossing the boundary or being too narrow. For example, when the limit coefficient is too small, the upper limit of the adjusted limit range may be lower than the lower limit. At this time, the system needs to forcibly set the upper limit to the lower limit value to ensure the effectiveness of the limit range. In addition, when the limit range is too narrow and the battery pack cannot work normally, the system also needs to feedback warning information to the upper layer, prompting the need to further reduce the safety risk level or take other protective measures. The system can also set the hysteresis and hysteresis strategy of the limit range adjustment to avoid the jitter of the control system caused by frequent fluctuations in the limit range. For example, when the risk level increases, the limit range can be reduced immediately. But when the risk level decreases, the recovery of the limit range can be relatively slow and delayed for a certain period of time to prevent the battery pack from repeating the safety state.

[0138] In the above embodiment, the safety monitoring data such as the temperature rise rate, voltage change rate and internal resistance change of each battery cell in the battery pack are obtained, and the temperature abnormality index, voltage abnormality index and internal resistance abnormality index are calculated respectively, and these abnormality indexes are weighted and combined to obtain a comprehensive safety index, which can fully reflect the safety status of the battery cell. The range of the current power dynamic limit interval is dynamically adjusted according to the change trend of the comprehensive safety index, so that the current power limit is more adaptive and forward-looking, and the operating parameters can be adjusted in time at the early stage of the abnormal state. The system can actively prevent safety risks while ensuring performance, improve the safety and reliability of the system, and reduce the probability of safety accidents in the battery pack.

[0139] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a battery pack safety management system based on BMS provided in an embodiment of the present application.

[0140] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0141] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0142] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0143] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0144] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0145] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0147] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0148] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0149] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0150] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A battery pack safety management method based on BMS, characterized in that: include: Acquire temperature data, voltage data and current data of each battery cell in the battery pack within a preset sampling time period; Calculate the temperature difference between each battery cell and an adjacent battery cell according to the temperature data to obtain temperature gradient data; Determine the heat diffusion direction between the battery cells according to the temperature gradient data, and determine the heat source battery cell where the heat is accumulated according to the heat diffusion direction; Calculating the power fluctuation characteristics of the heat source battery cell according to the voltage data and current data of the heat source battery cell; Combining the temperature gradient data and the power fluctuation characteristics, determining the degree of influence of the heat source battery cell on the adjacent battery cells, and obtaining a propagation risk value; Divide the safety area of ​​the battery pack based on the propagation risk value, and mark the area where the battery cells where the propagation risk value exceeds a preset threshold are located as a high-risk area; Screening key battery cells in the high-risk area whose charge and discharge depth exceeds a preset safety threshold; Determine a safety buffer distance according to the position of the key battery cell, determine a safety risk level based on the state of the battery cell within the safety buffer distance, and establish a current power dynamic limit interval corresponding to the safety risk level; The charge and discharge current of the key battery cell and all battery cells within a preset range of the key battery cell are adjusted according to the current power dynamic limit interval.

2. The method according to claim 1, characterized in that: Calculating the temperature difference between each battery cell and an adjacent battery cell according to the temperature data to obtain temperature gradient data specifically includes: Determining the immediate neighbors of each of the battery cells; Calculating the instantaneous temperature difference between each of the battery cells and the directly adjacent cells at preset time intervals within the preset sampling time period; Generate a temperature change sequence according to the instantaneous temperature difference, and calculate the change rate and fluctuation amplitude of the temperature change sequence; The instantaneous temperature difference is weighted based on the change rate and the fluctuation amplitude to obtain temperature gradient data.

3. The method according to claim 1, characterized in that: The calculating the power fluctuation characteristics of the heat source battery cell according to the voltage data and current data of the heat source battery cell specifically includes: Combining the voltage data and current data of the heat source battery cells into a voltage sequence and a current sequence in chronological order; Calculating the variation of the voltage sequence and the current sequence at adjacent moments to obtain a voltage variation and a current variation; Calculate the power variation trend according to the voltage variation and the current variation to obtain a power fluctuation curve; The peaks, troughs and duration of the power fluctuation curve are analyzed to determine the power fluctuation characteristics.

4. The method according to claim 1, characterized in that Regulating the charge and discharge current of the key battery cell and all battery cells within a preset range of the key battery cell according to the current power dynamic limit interval specifically includes: Determine the upper and lower limits of the charge and discharge current of each battery cell according to the current power dynamic limit interval; Establishing a current regulation optimization model with temperature balance as the goal, and taking the upper limit of the charge and discharge current and the lower limit of the charge and discharge current as constraint conditions; The battery cells are grouped in a hierarchical and progressive manner to obtain a plurality of battery cell groups; The target current value of each of the battery cell groups is calculated based on the optimization model, and the current value of each of the battery cell groups is adjusted to the corresponding target current value according to a preset step size.

5. The method according to claim 4, characterized in that The current regulation optimization model takes the weighted sum of the temperature balance objective function, the power fluctuation objective function and the current change rate objective function as the optimization objective, the temperature balance objective function is the square sum of the difference between the temperature of each battery cell and the average temperature, the power fluctuation objective function is the square sum of the power change at each moment, and the current change rate objective function is the square sum of the current change of each battery cell at adjacent moments. The current regulation optimization model takes the upper limit of the charge and discharge current, the lower limit of the charge and discharge current, the maximum allowable temperature difference between adjacent battery cells, the maximum allowable current change rate and the total power balance of the battery pack as constraints, and inputs the real-time temperature data, temperature gradient data, current charge and discharge current value, current voltage value and ambient temperature data of each battery cell into the current regulation optimization model to obtain the target current regulation value, current regulation priority and regulation timing arrangement of each battery cell group.

6. The method according to claim 1, characterized in that After adjusting the charge and discharge current of the key battery cell and all battery cells within the preset range of the key battery cell according to the current power dynamic limit interval, the method further includes: Acquiring safety monitoring data of the battery pack, wherein the safety monitoring data includes a temperature rise rate, a voltage change rate, and an internal resistance change of each of the battery cells; Calculating a temperature anomaly index according to the temperature rise rate, calculating a voltage anomaly index according to the voltage change rate, and calculating an internal resistance anomaly index according to the internal resistance change; Performing a weighted combination of the temperature anomaly index, the voltage anomaly index, and the internal resistance anomaly index to obtain a comprehensive safety index; According to the change trend of the comprehensive safety index, the range of the current power dynamic limit interval is adjusted.

7. The method according to claim 6, characterized in that The adjusting the range of the current power dynamic limit interval according to the change trend of the comprehensive safety index specifically includes: Calculating the rate of change and fluctuation of the comprehensive safety index within a preset time window; Determining a security risk level according to the change rate and the fluctuation range; Setting a current power limit coefficient according to the safety risk level; The current power limit coefficient is applied to the upper limit and the lower limit of the current power dynamic limit interval.

8. A battery pack safety management system based on BMS, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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