A lithium ion battery storage method and system based on a BMS architecture

Abstract

The present application relates to the technical field of battery management, and particularly relates to a lithium ion battery storage method and system based on a BMS architecture, the method comprising: grouping and classifying battery packs according to internal resistance ranges, collecting internal resistance fluctuation amplitudes and terminal voltage deviation rates of each cluster in real time under constant charging current conditions; calculating internal resistance sensitivity coefficients of each cluster under corresponding groups based on the above parameters, thereby quantifying cluster-level circulating current risk levels, and synchronously obtaining the number of batteries in the cluster, internal resistance standard deviation and charging current conditions; comprehensively considering the circulating current risk level, the number of batteries in the cluster, the internal resistance standard deviation and the charging condition, calculating the compensation priority index of the battery cluster; selecting the battery cluster with the highest priority as a reference cluster, and adjusting the internal resistance difference compensation voltage of non-reference clusters through the BMS, dynamically balancing the terminal voltage of each cluster, suppressing the circulating current effect, and improving the stability and energy utilization efficiency of the battery storage process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, and particularly relates to a lithium ion battery storage method and system based on a BMS architecture. BACKGROUND

[0002] In a lithium ion battery pack energy storage system, the internal resistance consistency of a battery cluster as a basic unit directly affects the system performance. Due to manufacturing process, use condition and aging degree difference, there is inherent discreteness in the internal resistance of different battery clusters, and the internal resistance will dynamically fluctuate under the influence of factors such as temperature and state of charge (SOC) in the storage process. When the battery pack is charged at a constant current, the internal resistance difference causes the end voltage of each cluster to respond asynchronously, forming a potential difference between clusters, and further causing a circulating current phenomenon. The circulating current not only causes energy loss and aggravates battery aging, but also may cause local overheating, threatening the safety and reliability of the system.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a lithium ion battery storage method and system based on a BMS architecture, aiming to solve the technical problem that the internal resistance of different clusters in the current lithium ion battery pack exists difference, causing a circulating current in the storage process of the lithium ion battery pack.

[0005] To achieve the above purpose, the present application provides a lithium ion battery storage method based on a BMS architecture, which comprises:

[0006] The lithium ion battery pack is divided into internal resistance grouping specifications according to the internal resistance range, and the internal resistance fluctuation amplitude and the end voltage deviation rate in the storage process are collected in real time in each grouping specification battery cluster, and the input charging current of all battery clusters in the lithium ion battery pack remains constant;

[0007] The internal resistance fluctuation amplitude and the end voltage deviation rate of each grouping specification battery cluster are outputted to obtain the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification;

[0008] The circulating current risk level of different grouping specification battery clusters is quantified based on the internal resistance sensitivity coefficient, and the number of intra-cluster batteries, intra-cluster internal resistance standard deviation and charging current working condition of different grouping specification battery clusters are obtained;

[0009] The compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of intra-cluster batteries, the intra-cluster internal resistance standard deviation and the charging current working condition;

[0010] The battery cluster with the highest compensation priority is selected as a reference cluster. During the storage process of the lithium ion battery pack, the BMS adjusts the voltage of the non-reference cluster battery by compensating for the difference in internal resistance, so that the terminal voltages of each cluster tend to be balanced.

[0011] Optionally, the real-time collection of the internal resistance fluctuation amplitude and the terminal voltage deviation rate of each battery cluster in each grouping specification during the storage process comprises:

[0012] In each battery cluster of each grouping specification, any battery cluster to be analyzed is determined as a target battery cluster, and the remaining battery clusters in the grouping specification except for the target battery cluster are defined as reference battery clusters;

[0013] The real-time internal resistance, port voltage, historical internal resistance mean value of the target battery cluster, and the port voltage mean value of the reference battery cluster during the storage process are collected in real time;

[0014] The internal resistance fluctuation amplitude is determined based on the deviation degree of the real-time internal resistance of the target battery cluster and the historical mean value, and the terminal voltage deviation rate is calculated based on the port voltage of the target battery cluster and the port voltage mean value of the reference battery cluster.

[0015] Optionally, the internal resistance fluctuation amplitude and the terminal voltage deviation rate of each battery cluster in each grouping specification are outputted as the internal resistance sensitivity coefficient of each battery cluster in the corresponding grouping specification, comprising:

[0016] The internal resistance fluctuation amplitude of each battery cluster in each grouping specification is taken as a first difference factor, and the terminal voltage deviation rate is taken as a second difference factor;

[0017] The first difference factor and the second difference factor are normalized, the weighting weight of the first difference factor and the second difference factor is dynamically adjusted based on the aging degree of each battery cluster in each grouping specification, a weighted sum index is formed, and the weighted sum index comprises an internal resistance fluctuation weight.

[0018] A sliding time window is introduced, the fluctuation frequency of the weighted sum index in the time window is counted, and the internal resistance sensitivity coefficient of each battery cluster in the corresponding grouping specification is calculated based on the first difference factor, the second difference factor, the weighted sum index, and the fluctuation frequency.

[0019] Optionally, the calculation formula of the terminal voltage deviation rate is:

[0020]

[0021] In the formula, δ U is the terminal voltage deviation rate, U t is the port voltage of the target battery cluster, U r_avg is the port voltage mean value of the reference battery cluster.

[0022] The calculation formula of the internal resistance sensitivity coefficient is:

[0023] S = γ · ΔR r + (1 - γ) · δ U + δ · f

[0024] In the formula, S is the internal resistance sensitivity coefficient, γ is the internal resistance fluctuation weight, ΔR r is the internal resistance fluctuation amplitude, δ U is the terminal voltage deviation rate, δ is the frequency correction coefficient, and f is the fluctuation frequency.

[0025] Optionally, the internal resistance sensitivity coefficient is used to quantify the circulating current risk level of the battery cluster of different grouping specifications, including:

[0026] The inter-cluster average internal resistance difference of the battery cluster of different grouping specifications is quantified based on the internal resistance sensitivity coefficient.

[0027] The circulating current risk level is calculated based on the inter-cluster average internal resistance difference and the internal resistance sensitivity coefficient of the battery cluster.

[0028] The calculation formula of the circulating current risk level is as follows:

[0029] R k = k1 · S + k2 · |ΔR a |

[0030] In the formula, R k is the circulating current risk level, S is the internal resistance sensitivity coefficient, |ΔR a | is the inter-cluster average internal resistance difference, k1 and k2 are working condition coefficients, k2 increases during fast charging, and k1 increases during slow charging.

[0031] Optionally, the compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of batteries in the cluster, the intra-cluster internal resistance standard deviation, and the charging current working condition, including:

[0032] The stage correction factor of the charging stage is determined based on the charging current working condition.

[0033] The compensation priority index of the battery cluster is calculated according to the circulating current risk level, the number of batteries in the cluster, the intra-cluster internal resistance standard deviation, and the stage correction factor, and the calculation formula of the compensation priority index is as follows:

[0034]

[0035] In the formula, P is the compensation priority index, R k is the circulating current risk level, n is the number of batteries in the cluster, σ R is the intra-cluster internal resistance standard deviation, and η sThe stage correction factor is 1.2-1.5 in the constant current stage and 0.8-1.0 in the constant voltage stage.

[0036] Optionally, the battery cluster with the highest compensation priority is selected as the reference cluster, and during the power storage process of the lithium ion battery pack, the voltage adjustment of the internal resistance difference compensation is implemented on the non-reference cluster battery by the BMS to make the terminal voltages of each cluster tend to be balanced, comprising:

[0037] The battery cluster with the highest compensation priority is selected as the reference cluster, and the average internal resistance of the battery in the reference cluster is calculated;

[0038] The real-time internal resistance of each battery in the non-reference cluster is obtained, and the compensation voltage of the real-time internal resistance of each battery in the non-reference cluster is calculated according to the average internal resistance of the battery in the reference cluster, the real-time internal resistance of each battery in the non-reference cluster, and the input charging current;

[0039] During the power storage process of the lithium ion battery pack, the voltage adjustment of the internal resistance difference compensation is implemented on the non-reference cluster battery by the BMS to make the terminal voltages of each cluster tend to be balanced, wherein the calculation formula of the compensation voltage is:

[0040] ΔU i =K·(R i -R b )·I c

[0041] In the formula, ΔU i is the compensation voltage of the real-time internal resistance of each battery in the non-reference cluster, K is a temperature compensation coefficient, R i is the real-time internal resistance of each battery in the non-reference cluster, R b is the average internal resistance of the battery in the reference cluster, and I c is the input charging current.

[0042] In addition, to achieve the above-mentioned purpose, the application also provides a lithium ion battery power storage system based on a BMS architecture, which comprises:

[0043] An internal resistance grouping module is used to divide the lithium ion battery pack according to internal resistance grouping specifications according to the internal resistance range, and the internal resistance fluctuation amplitude and the terminal voltage deviation rate during the power storage process are collected in real time in each battery cluster of the grouping specification, and the input charging current of all battery clusters in the lithium ion battery pack remains constant;

[0044] A coefficient calculation module is used to output the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of each battery cluster of the grouping specification;

[0045] a risk quantification module, configured to quantify a circulating current risk level of the battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient, and to obtain the number of batteries in each cluster, the internal resistance standard deviation in each cluster, and the charging current working condition;

[0046] an index calculation module, configured to calculate a compensation priority index of the battery clusters based on the circulating current risk level, the number of batteries in each cluster, the internal resistance standard deviation in each cluster, and the charging current working condition;

[0047] a balancing adjustment module, configured to select the battery cluster with the highest compensation priority as a reference cluster, and to adjust the internal resistance difference compensation voltage of the non-reference cluster batteries through the BMS during the energy storage process of the lithium ion battery pack, so that the terminal voltages of the clusters tend to be balanced.

[0048] In addition, to achieve the above object, the present application also provides a lithium ion battery energy storage device based on a BMS architecture, which comprises a memory, a processor, and a lithium ion battery energy storage program based on a BMS architecture stored on the memory and executable on the processor, and the lithium ion battery energy storage program based on a BMS architecture is configured to implement the steps of the lithium ion battery energy storage method based on a BMS architecture as described in any one of the above.

[0049] In addition, to achieve the above object, the present application also provides a storage medium having a lithium ion battery energy storage program based on a BMS architecture stored thereon, and the lithium ion battery energy storage program based on a BMS architecture, when executed by a processor, implements the steps of the lithium ion battery energy storage method based on a BMS architecture as described in any one of the above.

[0050] The present application provides a lithium ion battery energy storage method based on a BMS architecture, which divides the battery clusters by grouping specifications according to the internal resistance range, and combines the real-time collected internal resistance fluctuation amplitude and terminal voltage deviation rate to accurately calculate the internal resistance sensitivity coefficient, effectively identifies the circulating current risk of different clusters, provides a scientific basis for balancing control, and significantly improves the balancing efficiency. The circulating current risk level is quantified based on the internal resistance sensitivity coefficient, and the compensation priority index is dynamically calculated based on the number of batteries in each cluster, the internal resistance standard deviation, and the charging current working condition, so that the high-risk clusters are preferentially compensated, the negative impact of circulating current on the battery pack is significantly reduced, and the battery life is prolonged. By selecting the battery cluster with the highest compensation priority as the reference cluster and dynamically adjusting the terminal voltage of the non-reference cluster, the terminal voltages of the clusters tend to be balanced, the energy loss is reduced, and the overall energy utilization efficiency of the battery pack is improved. Under the condition of constant charging current, the BMS is used for real-time monitoring and adjustment, which effectively suppresses the terminal voltage deviation caused by internal resistance difference, and improves the stability and reliability of the battery pack during the energy storage process. The multi-dimensional parameters such as internal resistance fluctuation, terminal voltage deviation, number of batteries in each cluster, and charging current are comprehensively considered, so that the scheme can adapt to the balancing demand under different working conditions, and has strong universality and practicality. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a hardware running environment of the lithium ion battery power storage device structure schematic diagram based on the BMS architecture involved in the embodiment of the present application.

[0052] Figure 2 is a flowchart of the first embodiment of the lithium ion battery power storage method based on the BMS architecture of the present application.

[0053] Figure 3 is a structure block diagram of the first embodiment of the lithium ion battery power storage system based on the BMS architecture of the present application.

[0054] The implementation of the object, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0056] Referring to Figure 1 , Figure 1 is a hardware running environment of the lithium ion battery power storage device structure schematic diagram based on the BMS architecture involved in the embodiment of the present application.

[0057] As Figure 1 shown, the lithium ion battery power storage device based on the BMS architecture can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between the components. The user interface 1003 can include a display screen (Display), and the optional user interface 1003 can also include a standard wired interface, a wireless interface, and the wired interface of the user interface 1003 can be a USB interface in the present application. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 can be a high-speed random access memory (RAM), and can also be a stable memory (Non-volatile Memory, NVM), such as a disk memory. The memory 1005 can also be an independent storage device from the aforementioned processor 1001.

[0058] Those skilled in the art can understand that Figure 1The structure shown in the figure does not constitute a limitation on the lithium ion battery storage device based on the BMS architecture, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0059] As shown in Figure 1 The memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a lithium ion battery storage program based on the BMS architecture.

[0060] In the lithium ion battery storage device based on the BMS architecture shown in Figure 1 The network interface 1004 is mainly used to connect the background server and communicate data with the background server; the user interface 1003 is mainly used to connect the external device; the lithium ion battery storage device based on the BMS architecture calls the lithium ion battery storage program based on the BMS architecture stored in the memory 1005 through the processor 1001, and executes the lithium ion battery storage method based on the BMS architecture provided by the embodiment of the application.

[0061] Based on the above hardware structure, embodiments of the lithium ion battery storage method based on the BMS architecture of the application are proposed.

[0062] Referring to Figure 2 , Figure 2 The flowchart of the first embodiment of the lithium ion battery storage method based on the BMS architecture of the application is proposed.

[0063] In the first embodiment, the lithium ion battery storage method based on the BMS architecture includes the following steps:

[0064] S10: The lithium ion battery pack is grouped according to the internal resistance range, and the internal resistance fluctuation amplitude and the terminal voltage deviation rate in the storage process are collected in real time in each battery cluster of the grouping specification, and the input charging current of all battery clusters in the lithium ion battery pack remains constant.

[0065] It should be noted that the lithium ion battery pack is an energy storage system composed of multiple lithium ion battery clusters combined in series and parallel, used for storing and releasing electric energy. Its performance is affected by the consistency of each battery cluster, and the internal resistance difference is one of the core factors leading to inter-cluster circulation. The internal resistance is the equivalent resistance inside the battery, including ohmic resistance and polarization resistance, among which the ohmic resistance can be the material contact resistance and the electrolyte resistance, and the polarization resistance can be the electrochemical reaction polarization and the concentration polarization. The internal resistance will affect the response speed of the battery terminal voltage and is an important parameter for measuring the battery state, including the aging degree and the state of charge. The internal resistance grouping specification divides the multiple battery clusters in the lithium ion battery pack into different groups according to the size range of the internal resistance of the battery cluster, such as high internal resistance group, medium internal resistance group and low internal resistance group. The purpose of grouping is to reduce the internal resistance difference of the battery clusters in the same group through clustering management and reduce the risk of cross-group circulation. The battery cluster is the basic component unit of the lithium ion battery pack, which can be composed of tens to hundreds of battery monomers in series and parallel, has an independent input and output port, and can be regarded as a whole to participate in the charging and discharging process of the battery pack.

[0066] It should be understood that real-time acquisition refers to high-frequency real-time monitoring of key parameters of the battery cluster by sensors (such as current sensors and voltage sensors) of the battery management system (BMS), ensuring the timeliness and accuracy of the data and providing real-time data support for subsequent control strategies. The storage process refers to the charging process of the battery pack, which means that the external power source inputs electric energy to the battery pack to store chemical energy. Here, it specifically refers to the constant current charging condition, the input charging current remains constant, and the influence of internal resistance difference on terminal voltage is more significant under this condition, which is easy to cause the potential difference between clusters. The internal resistance fluctuation range refers to the change range of the real-time internal resistance of the battery cluster relative to the initial value or average value during the charging process, which is affected by factors such as temperature, SOC and polarization effect. The greater the fluctuation range, the poorer the stability of the internal resistance, and the more likely the terminal voltage response deviates from the expected value. The terminal voltage deviation rate is the difference between the real-time terminal voltage of a single battery cluster and the average terminal voltage of all battery clusters in the same group. The deviation rate directly reflects the degree of potential difference between clusters and is a direct cause of circulation. The input charging current remains constant, which means that the total charging current input by the external power source remains fixed and does not change with the battery state. Under the constant current condition, the internal resistance difference will cause different terminal voltage rising rates of each cluster, and then form a potential difference, which is a key condition for triggering circulation.

[0067] Specifically, the real-time acquisition of the internal resistance fluctuation range and the terminal voltage deviation rate in the storage process in each grouping specification battery cluster includes:

[0068] In each grouping specification battery cluster, any battery cluster to be analyzed is determined as a target battery cluster, and the remaining battery clusters in the same grouping specification except the target battery cluster are defined as reference battery clusters;

[0069] collecting, in real time, a real-time internal resistance, a port voltage, a historical internal resistance mean value of the target battery cluster, and a port voltage mean value of the reference battery cluster during the power storage process;

[0070] determining an internal resistance fluctuation amplitude based on a deviation degree of the real-time internal resistance of the target battery cluster from the historical mean value, and calculating an end voltage deviation rate based on the port voltage of the target battery cluster and the port voltage mean value of the reference battery cluster.

[0071] It should be noted that the target battery cluster refers to a single battery cluster selected as the current analysis object within the grouping specification. Each analysis is performed on one target cluster, and the target battery cluster is used as a specific evaluation unit for internal resistance fluctuation and end voltage deviation. By comparing with other clusters in the same group (reference clusters), the individual state difference of the target cluster is quantified. The reference battery cluster is a collection of all other battery clusters in the grouping specification except the target battery cluster, i.e., a reference group within the same group. The reference battery cluster provides the average state within the same group as a reference, such as the port voltage mean value, to measure the deviation degree of the target cluster. Based on the assumption that the internal resistance characteristics of the battery clusters in the same group are similar, the reference clusters are divided according to the internal resistance range during grouping, and the overall performance of the reference clusters can be regarded as the normal state of the group. The difference between the target cluster and the reference cluster reflects the degree of individual abnormality. The real-time internal resistance is the equivalent internal resistance measurement value of the target battery cluster at the current sampling time, which includes the dynamic integrated value of ohmic resistance and polarization resistance. The real-time internal resistance can be measured in real time by the alternating current impedance method or the direct current pulse method, and is affected by factors such as charging current, SOC, and temperature, reflecting the real-time state of the internal electrochemical process of the battery. The real-time internal resistance is a transient value, and the historical internal resistance mean value is the average value of a certain period of time (such as the last N sampling points), which is used to filter out short-term noise and reflect the long-term trend of internal resistance. The port voltage is the real-time voltage value of the positive and negative output ports of the battery cluster, which is a comprehensive representation of the battery electromotive force, internal resistance drop, and polarization voltage. The historical internal resistance mean value is the internal resistance average value of the target battery cluster in a certain period of time (such as the last 5 minutes or 100 sampling points during charging). The port voltage mean value is the average value of the port voltage of the reference battery cluster at the same time. The end voltage deviation rate is the deviation degree of the real-time internal resistance of the target battery cluster relative to the historical internal resistance mean value. The end voltage deviation rate is used to quantify the dynamic fluctuation amplitude of the internal resistance and reflect the stability of the internal state of the battery cluster. For example, a large deviation degree indicates that the polarization is intensified, the internal contact is poor, or the aging is accelerated.

[0072] S20: outputting an internal resistance sensitive coefficient of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitude and the end voltage deviation rate of the battery cluster of each grouping specification.

[0073] It should be noted that the internal resistance sensitivity coefficient is a quantitative index of the sensitivity of the terminal voltage of the battery cluster to the change of the internal resistance under a specific grouping specification. The coefficient reflects the influence of the dynamic change of the internal resistance on the potential difference between clusters by fusing the internal resistance fluctuation amplitude and the terminal voltage deviation rate, and is a core parameter for evaluating the circulation risk. The internal resistance fluctuation amplitude reflects the stability of the internal resistance of the battery cluster itself. The greater the fluctuation, the more unstable the internal resistance, and the more significant the influence of the terminal voltage on the internal resistance. The terminal voltage deviation rate reflects the deviation degree of the target cluster from the average voltage of the same group. The greater the deviation rate, the greater the potential difference between clusters, and the stronger the driving force of circulation. The internal resistance sensitivity coefficient is a comprehensive mapping of the two. Under a constant charging current, the ability of the terminal voltage to abnormally deviate due to the change of the internal resistance. The higher the coefficient, the higher the risk of circulation caused by the internal resistance problem of the cluster.

[0074] Specifically, the internal resistance fluctuation amplitude and the terminal voltage deviation rate of the battery cluster based on each grouping specification output the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification, including:

[0075] The internal resistance fluctuation amplitude of the battery cluster of each grouping specification is taken as a first difference factor, and the terminal voltage deviation rate is taken as a second difference factor.

[0076] The first difference factor and the second difference factor are normalized, the weighting weight of the first difference factor and the second difference factor is dynamically adjusted based on the aging degree of the battery cluster of each grouping specification, a weighted sum index is formed, and the weighted sum index includes an internal resistance fluctuation weight.

[0077] A sliding time window is introduced, the fluctuation frequency of the weighted sum index in the time window is counted, and the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification is calculated based on the first difference factor, the second difference factor, the weighted sum index and the fluctuation frequency.

[0078] It should be noted that the first difference factor directly corresponds to the internal resistance fluctuation amplitude, that is, the deviation degree of the real-time internal resistance of the target battery cluster relative to the historical mean value. As a core parameter reflecting the dynamic change of the internal resistance, the first difference factor reflects the longitudinal difference between the internal resistance characteristics of the battery cluster itself and the historical state. The second difference factor directly corresponds to the terminal voltage deviation rate, that is, the difference proportion (absolute value) of the port voltage of the target battery cluster and the mean value of the reference cluster in the same group. The second difference factor reflects the lateral difference between the target cluster and other clusters in the same group. The normalization process is used to convert the first / second difference factor with different dimensions and large differences in value range into a dimensionless and unified range. The aging degree is a comprehensive index for measuring the aging state of the battery cluster, which is usually evaluated by the internal resistance growth amplitude, capacity attenuation rate, cycle number and other parameters. The new battery has low aging degree and high internal resistance stability, and the terminal voltage deviation is mainly caused by short-term polarization effect, so the second difference factor (voltage deviation rate) has a higher weight. The aged battery has high aging degree: the internal resistance significantly increases and fluctuates with the cycle number, and the influence of internal resistance fluctuation on voltage deviation is more significant, so the first difference factor (internal resistance fluctuation) has a higher weight. The weighted weight is used to quantify the contribution of the first / second difference factor to the final sensitive coefficient, which is dynamically adjusted by the aging degree, rather than a fixed value. The weighted sum is the result of summing the normalized first / second difference factors according to the weighted weight, and the weighted sum index converts the longitudinal (time dimension) and lateral (group dimension) differences into a single comprehensive index, reflecting the degree of deviation of the current state of the battery cluster from the normal state in the group; the sliding time window is used in time series data processing, and a fixed length time window is set, such as the last 5 minutes or 100 sampling points. The window slides forward with the collection of new data, and only the latest data in the window is retained for analysis. The sliding time window is used to avoid the influence of accidental interference on the result and focus on the trend change. The fluctuation frequency is the number of times that the weighted sum index exceeds the preset threshold in the sliding time window, reflecting the instability of the state of the battery cluster. The higher the frequency, the more frequently the cluster deviates abnormally in the recent period, even if the current deviation does not exceed the standard, it may also indicate potential risks, such as the internal resistance fluctuation entering the intensification stage.

[0079] In the present embodiment, the calculation formula of the terminal voltage deviation rate is:

[0080]

[0081] In the formula, δ U is the terminal voltage deviation rate, U t is the port voltage of the target battery cluster, U r_avg is the mean value of the port voltage of the reference battery cluster;

[0082] The calculation formula of the internal resistance sensitive coefficient is:

[0083] S = γ · ΔR r + (1 - γ) · δ U+ δ · f

[0084] wherein S is the internal resistance sensitivity coefficient, γ is the internal resistance fluctuation weight, ΔR r is the internal resistance fluctuation amplitude, δ U is the terminal voltage deviation rate, δ is the frequency correction coefficient, and f is the fluctuation frequency.

[0085] It should be noted that the terminal voltage deviation rate δ U reflects the deviation of the target battery cluster port voltage from the average of the same group of reference clusters. The port voltage U t of the target battery cluster at the current time point contains the combined output value of electromotive force, internal resistance voltage drop and polarization voltage. The internal resistance sensitivity coefficient S represents the risk degree of abnormal terminal voltage of the battery cluster caused by internal resistance characteristics, and the greater the value, the higher the risk. The internal resistance fluctuation weight γ is dynamically adjusted by the aging degree of the battery cluster. For example, the more serious the aging is, the closer γ is to 1, and the more emphasis is placed on the influence of internal resistance fluctuation. The internal resistance fluctuation amplitude ΔR r is the deviation of the real-time internal resistance of the target cluster from the historical average. The frequency correction coefficient δ is used to quantify the influence weight of the fluctuation frequency on the sensitivity coefficient, which can be determined by engineering debugging or algorithm optimization.

[0086] S30: quantifying the circulating current risk level of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient, and obtaining the number of battery clusters within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current working condition of the battery clusters of different grouping specifications.

[0087] It should be noted that the circulating current risk level is a graded assessment based on the internal resistance sensitivity coefficient, the number of cells within a cluster, the standard deviation of the internal resistance within a cluster, and the charging current conditions. This assessment evaluates the likelihood and severity of inter-cluster circulating currents caused by voltage differences during charging. The number of cells within a cluster refers to the number of individual cells connected in series or parallel within a single cluster. A larger number of cells within a cluster increases the likelihood of initial or aging differences in parameters such as internal resistance and capacity between cells accumulating, leading to a higher probability of the cluster terminal voltage deviating from the group mean. The standard deviation of the internal resistance within a cluster is the standard deviation of the internal resistance of all individual cells within the cluster. A smaller standard deviation indicates more uniform internal resistance among the cells, closer polarization characteristics and aging levels, and lower voltage dispersion. Conversely, a larger standard deviation indicates abnormal local internal resistance within the cluster, such as poor contact in individual cells or thickening of the SEI film, increasing the likelihood of cluster terminal voltage fluctuations. Even if the internal resistance fluctuation of a cluster is small, a significant increase in the standard deviation amplifies the impact of internal resistance fluctuations on the terminal voltage. Due to uneven distribution of internal resistance voltage drop caused by individual cell differences, the cluster terminal voltage is dominated by abnormal cells. The charging current condition refers to the magnitude and variation pattern of the current flowing through the battery cluster during charging, including the current during the constant current stage, the current decay curve during the constant voltage stage, and the pulse charging current waveform. Under high current conditions, fluctuations in the same internal resistance will cause larger voltage deviations, directly increasing the internal resistance sensitivity coefficient. Under different current conditions, the battery polarization response time is different (high current polarization is more significant and recovery is slower), resulting in differences in the dynamic characteristics of inter-cluster voltage deviations, such as the periodic fluctuation characteristics of voltage deviations during pulse charging.

[0088] Specifically, the quantification of the circulating current risk level of battery clusters with different group specifications based on the internal resistance sensitivity coefficient includes:

[0089] The average internal resistance difference between battery clusters of different group specifications is quantified based on the internal resistance sensitivity coefficient.

[0090] The circulating current risk level is calculated based on the average internal resistance difference between the battery clusters and the internal resistance sensitivity coefficient.

[0091] The formula for calculating the circulation risk level is as follows:

[0092] R k =k1·S+k2·|ΔR a |

[0093] In the formula, R k The risk level is represented by S, where S is the internal resistance sensitivity coefficient, and |ΔR| is the circulatory risk level. a | represents the average internal resistance difference between clusters, and k1 and k2 are operating condition coefficients. k2 increases during fast charging, and k1 increases during slow charging.

[0094] It should be noted that the average internal resistance difference between clusters |ΔR a| The core index for characterizing the internal resistance difference between different grouping specifications of battery clusters, that is, the absolute value of the difference between the average internal resistance of the target battery cluster and the reference cluster. The working condition coefficients k1 and k2 are weight coefficients dynamically adjusted according to the charging current working condition, used to balance the internal resistance sensitive coefficient S and the average inter-cluster internal resistance difference |ΔR a | The contribution degree to the risk level is the bridge connecting the theoretical model and the actual working condition.

[0095] S40: Calculate the compensation priority index of the battery cluster based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the charging current working condition.

[0096] It should be noted that the compensation priority index is used to represent the quantitative index of the battery cluster that needs to be compensated (such as equalization control, current adjustment) during charging. By comprehensively considering the circulating current risk, the consistency within the cluster, the scale effect, and the working condition characteristics, the BMS provides accurate compensation ranking decision basis.

[0097] Specifically, the compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the charging current working condition, which includes:

[0098] Determine the stage correction factor of the charging stage based on the charging current working condition;

[0099] Calculate the compensation priority index of the battery cluster according to the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the stage correction factor, wherein the calculation formula of the compensation priority index is as follows:

[0100]

[0101] In the formula, P is the compensation priority index, R k is the circulating current risk level, n is the number of batteries in the cluster, σ R is the standard deviation of the internal resistance in the cluster, and η s is the stage correction factor. The stage correction factor is 1.2-1.5 in the constant current stage and 0.8-1.0 in the constant voltage stage.

[0102] It should be noted that the stage correction factor η s reflects the dynamic adjustment coefficient of the compensation priority in different stages of the charging process, and the core function is to adjust the evaluation weight according to the current characteristics and risk characteristics of the charging stage. In the constant current stage, the current is constant, and the internal resistance voltage drop is the main component of the terminal voltage. The influence of inter-cluster internal resistance difference and intra-cluster dispersion on voltage deviation is directly amplified, and the risk escalation speed is fast, so η sTake 1.2~1.5, raise the compensation priority; the voltage is constant in the constant voltage stage, the charging current gradually decreases as the battery SOC rises, the internal resistance pressure drop ratio decreases, the polarization effect and voltage balance become the core concern, and the risk urgency decreases, so η s Take 0.8~1.0, appropriately reduce the compensation priority to avoid overcompensation. In the constant current stage, when the battery terminal voltage does not reach the charging cutoff voltage (such as 4.2V for lithium batteries), the BMS maintains constant current charging, and η s Force to enable high correction factor; in the constant voltage stage, when the voltage reaches the cutoff voltage, the BMS switches to constant voltage mode, and the current gradually decays to the cutoff current (such as 0.05C), and η s Automatic switching to low correction factor. The larger the number of batteries in the cluster, the wider the impact of cluster failure, and the higher the compensation necessity;

[0103] If the standard deviation of the internal resistance in the cluster is large, that is, the dispersion in the cluster is serious, although it should be compensated in theory, but the ratio is small due to the large denominator, which seems to reduce the priority, but in fact it is a correction of the compensation performance: clusters with too high dispersion may need more complex balancing strategies (such as replacing single cells), and relying solely on BMS compensation is limited, so the denominator is suppressed to guide manual intervention;

[0104] If the standard deviation of the internal resistance in the cluster is small, the consistency is good, but the number of batteries in the cluster is large, which means that the cluster is a large-scale high-quality cluster, and once the risk occurs, such as R k rise due to external factors, it needs to be protected first, so the ratio amplifies its priority. In the constant current stage, focus on risk prevention and control: η s >1 forces to raise the compensation priority of all clusters to ensure fast response under large current, such as when a cluster's P suddenly increases in the constant current stage, the BMS can start hardware balancing within 200ms; in the constant voltage stage, optimize energy efficiency, η s <1 reduces the compensation priority and allows the BMS to allocate resources to clusters that really need balancing, such as only compensating clusters with P>80 to avoid invalid balancing energy consumption under small current.

[0105] S50: Select the battery cluster with the highest compensation priority as the reference cluster. During the charging process of the lithium ion battery pack, the BMS adjusts the voltage of the non-reference cluster batteries through internal resistance difference compensation, so that the terminal voltage of each cluster tends to be balanced.

[0106] It should be noted that the reference cluster is selected as the battery cluster with the end voltage equalization reference benchmark in the battery pack equalization process, and the essence is the optimal voltage target cluster dynamically generated as the absolute reference point of voltage equalization. The end voltage of the non-reference cluster needs to converge to it, so as to eliminate the deviation of the end voltage between the clusters; The end voltage curve of the reference cluster represents the ideal charging trajectory of the battery pack under the current working condition, and the other clusters are synchronized with it through compensation adjustment. The cluster with the highest compensation priority index can be directly selected, that is, the battery cluster with the highest risk level, significant scale effect and in the high correction factor stage; If there are multiple clusters with the same compensation priority index, the cluster with the smallest internal resistance standard deviation in the cluster is preferred; The cluster with the real-time end voltage closest to the average voltage of the battery pack reduces the adjustment range and energy consumption. Only one reference cluster can exist at the same time to avoid equalization conflicts caused by multiple reference points; Recalculate P value every 500 ms, if the original reference cluster P value decreases to the top three, trigger reference switching. The internal resistance difference is the equivalent internal resistance difference between the target cluster and the reference cluster, and the internal resistance difference directly leads to the end voltage deviation under the same charging current. The compensation voltage is the reverse adjustment voltage generated by the BMS through the equalization circuit, which is used to offset the end voltage deviation caused by the internal resistance difference. A compensation amount opposite to the internal resistance voltage drop can be added in the charging loop of the target cluster.

[0107] Specifically, the battery cluster with the highest compensation priority is selected as the reference cluster. During the charging process of the lithium ion battery pack, the BMS adjusts the internal resistance difference compensation voltage of the non-reference cluster battery, so that the end voltage of each cluster tends to be balanced, which comprises:

[0108] The battery cluster with the highest compensation priority is selected as the reference cluster, and the average internal resistance of the batteries in the reference cluster is calculated.

[0109] The real-time internal resistance of each battery in the non-reference cluster is obtained, and the compensation voltage of the real-time internal resistance of each battery in the non-reference cluster is calculated according to the average internal resistance of the batteries in the reference cluster, the real-time internal resistance of each battery in the non-reference cluster, and the input charging current.

[0110] During the charging process of the lithium ion battery pack, the BMS adjusts the internal resistance difference compensation voltage of the non-reference cluster battery, so that the end voltage of each cluster tends to be balanced, wherein the calculation formula of the compensation voltage is:

[0111] ΔU i =K·(R i -R b )·I c

[0112] In the formula, ΔU i is the compensation voltage of the real-time internal resistance of each battery in the non-reference cluster, K is the temperature compensation coefficient, R i is the real-time internal resistance of each battery in the non-reference cluster, and R b is the average internal resistance of the batteries in the reference cluster, Ic Input charging current.

[0113] It should be noted that the average internal resistance of the reference cluster is a global reference benchmark for the internal resistance equalization of the whole battery pack, and each battery of the non-reference cluster needs to converge to this value to eliminate the terminal voltage deviation caused by the internal resistance difference; the average internal resistance of the reference cluster reflects the overall polarization characteristics of the reference cluster, and the lower the average internal resistance value, the stronger the charging acceptance ability of the cluster, which can be used as an ideal target for compensation adjustment of other clusters. The real-time internal resistance of each battery of the non-reference cluster is the equivalent internal resistance of the i-th battery in the non-reference cluster measured in real time during the charging process, which includes ohmic resistance and polarization resistance. The temperature compensation coefficient is a correction coefficient reflecting the change of the battery internal resistance with temperature, which is used to compensate for the internal resistance measurement deviation caused by temperature difference. The internal resistance decreases with the increase of temperature, which is a negative temperature characteristic. If not compensated, the actual voltage drop of the same internal resistance difference at high temperature will be reduced, resulting in overcompensation; at low temperature, the opposite is true, and the compensation may be insufficient.

[0114] It should be understood that if R i R b , the internal resistance of the battery is too large, the terminal voltage is too high under the same current, and a reverse compensation voltage needs to be applied by the BMS, which is opposite to the charging voltage, to offset part of the internal resistance voltage drop and make the terminal voltage drop to the reference level; if R i R b , the internal resistance of the battery is too small, the terminal voltage is too low, and a same-direction compensation voltage needs to be applied, which is the same as the charging voltage, to raise the terminal voltage to the reference level. In fact, this can be achieved by adjusting the charging current distribution, avoiding direct voltage rise.

[0115] In addition, an embodiment of the present application also provides a storage medium, wherein the storage medium has a BMS architecture-based lithium ion battery storage program stored thereon, and the BMS architecture-based lithium ion battery storage program is executed by a processor to implement the steps of the BMS architecture-based lithium ion battery storage method.

[0116] In addition, with reference to Figure 3 , an embodiment of the present application also provides a BMS architecture-based lithium ion battery storage system, which comprises:

[0117] An internal resistance grouping module 10 is configured to group the lithium ion battery pack according to internal resistance ranges, and to collect the internal resistance fluctuation amplitude and the terminal voltage deviation rate in real time during the storage process in each battery cluster of each grouping specification, wherein the input charging current of all battery clusters in the lithium ion battery pack remains constant;

[0118] A coefficient calculation module 20 is configured to output the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of each battery cluster.

[0119] a risk quantification module 30 configured to quantify a circulating current risk level of the battery clusters of different grouping specifications based on the internal resistance sensitive coefficient, and to obtain the number of batteries in each cluster, the internal resistance standard deviation in each cluster, and the charging current working condition of the battery clusters;

[0120] an index calculation module 40 configured to calculate a compensation priority index of the battery clusters based on the circulating current risk level, the number of batteries in each cluster, the internal resistance standard deviation in each cluster, and the charging current working condition;

[0121] a balancing adjustment module 50 configured to select a battery cluster with the highest compensation priority as a reference cluster, and to implement internal resistance difference compensation voltage adjustment on the non-reference cluster batteries by the BMS to make the terminal voltages of the clusters tend to be balanced during the lithium ion battery pack power storage process.

[0122] Other embodiments or specific implementations of the lithium ion battery power storage system based on the BMS architecture according to the present application can refer to the above-mentioned method embodiments, and will not be described here again.

[0123] It should be noted that in this document, the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or system. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, method, article or system including the element.

[0124] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. In the system unit claims in which several systems are listed, several of these systems can be embodied by the same hardware item. The use of the words first, second, and third does not represent any order, and these words can be interpreted as names.

[0125] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art contribution can be embodied in the form of software products, the computer software product is stored in a storage medium (such as read only memory image (Read Only Memory image, ROM) / random access memory (Random Access Memory, RAM), disk, optical disk), including a number of instructions to make a terminal user equipment (may be a mobile phone, computer, server, air conditioner, or network user equipment, etc.) executes the method described in various embodiments of the present application.

[0126] The above is only the preferred embodiment of the present application, not to limit the patent scope of the present application, any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A lithium ion battery storage method based on a BMS architecture, characterized by, The method comprises: grouping specifications of lithium ion battery packs according to internal resistance ranges, collecting internal resistance fluctuation amplitudes and terminal voltage deviation rates in real time in each battery cluster of each grouping specification during the power storage process, and keeping the input charging current of all battery clusters in the lithium ion battery pack constant; outputting internal resistance sensitivity coefficients of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitudes and the terminal voltage deviation rates of the battery cluster of each grouping specification; quantifying the circulating current risk levels of different grouping specification battery clusters based on the internal resistance sensitivity coefficients, and obtaining the number of batteries in the cluster, the cluster internal resistance standard deviation and the charging current working condition of the battery cluster of different grouping specifications; calculating the compensation priority index of the battery cluster based on the circulating current risk level, the number of batteries in the cluster, the cluster internal resistance standard deviation and the charging current working condition; selecting the battery cluster with the highest compensation priority as the reference cluster, and adjusting the internal resistance difference compensation voltage of the non-reference cluster battery through the BMS during the power storage process of the lithium ion battery pack, so that the terminal voltages of each cluster tend to be balanced; wherein the real-time collection of the internal resistance fluctuation amplitude and the terminal voltage deviation rate in each battery cluster of each grouping specification comprises: determining any to-be-analyzed battery cluster as a target battery cluster in each battery cluster of each grouping specification, and defining the remaining battery clusters except the target battery cluster in the grouping specification as reference battery clusters; collecting the real-time internal resistance, port voltage, historical internal resistance mean value of the target battery cluster and the port voltage mean value of the reference battery cluster in real time during the power storage process; determining the internal resistance fluctuation amplitude based on the deviation degree of the real-time internal resistance and the historical mean value of the target battery cluster, and calculating the terminal voltage deviation rate based on the port voltage of the target battery cluster and the port voltage mean value of the reference battery cluster; wherein the outputting of the internal resistance sensitivity coefficients of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of the battery cluster of each grouping specification comprises: taking the internal resistance fluctuation amplitude of each battery cluster of each grouping specification as a first difference factor, and taking the terminal voltage deviation rate as a second difference factor; normalizing the first difference factor and the second difference factor, dynamically adjusting the weighting weight of the first difference factor and the second difference factor based on the aging degree of the battery cluster of each grouping specification, forming a weighted sum index, and the weighted sum index comprises an internal resistance fluctuation weight; introducing a sliding time window, counting the fluctuation frequency of the weighted sum index in the time window, and calculating the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification based on the first difference factor, the second difference factor, the weighted sum index and the fluctuation frequency; wherein the calculation formula of the terminal voltage deviation rate is: ， wherein is the terminal voltage deviation rate, is the port voltage of the target battery cluster, is the port voltage mean value of the reference battery cluster; the calculation formula of the internal resistance sensitivity coefficient is: ， In the formula, is an internal resistance sensitivity coefficient, is an internal resistance fluctuation weight, is an internal resistance fluctuation amplitude, is an end voltage deviation rate, is a frequency correction coefficient, is a fluctuation frequency; wherein the quantifying of the circulating current risk levels of different grouping specification battery clusters based on the internal resistance sensitivity coefficients comprises: quantifying the inter-cluster average internal resistance difference of different grouping specification battery clusters based on the internal resistance sensitivity coefficients; calculating the circulating current risk level based on the inter-cluster average internal resistance difference and the internal resistance sensitivity coefficient of the battery cluster; wherein the calculation formula of the circulating current risk level is as follows: ， In the formula, is the circulation risk level, is the internal resistance sensitivity coefficient, is the average inter-cluster internal resistance difference, and is the working condition coefficient, when fast charging increases, and when slow charging increases; The compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the charging current condition, and the compensation priority index includes: Determine the stage correction factor of the charging stage based on the charging current condition; The compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the stage correction factor, and the calculation formula of the compensation priority index is as follows: ， In the formula, is a priority index, is a circulation risk level, is the number of batteries in the cluster, is the standard deviation of the internal resistance in the cluster, is a stage correction factor, which is 1.2-1.5 in the constant current stage and 0.8-1.0 in the constant voltage stage. The battery cluster with the highest compensation priority is selected as the reference cluster, and during the storage process of the lithium ion battery pack, the internal resistance difference compensation voltage adjustment is implemented on the non-reference cluster battery by the BMS to make the terminal voltages of each cluster tend to be balanced, including: The battery cluster with the highest compensation priority is selected as the reference cluster, and the average internal resistance of the batteries in the reference cluster is calculated; The real-time internal resistance of each battery in the non-reference cluster is obtained, and the compensation voltage of the real-time internal resistance of each battery in the non-reference cluster is calculated based on the average internal resistance of the batteries in the reference cluster, the real-time internal resistance of each battery in the non-reference cluster, and the input charging current; During the storage process of the lithium ion battery pack, the internal resistance difference compensation voltage adjustment is implemented on the non-reference cluster battery by the BMS to make the terminal voltages of each cluster tend to be balanced, and the calculation formula of the compensation voltage is: ， wherein, is the compensation voltage for the real-time internal resistance of each battery of the non-reference cluster, is the temperature compensation coefficient, is the real-time internal resistance of each battery of the non-reference cluster, is the average internal resistance of the batteries of the reference cluster, is the input charging current.

2. A lithium ion battery storage system based on a BMS architecture, characterized by, The lithium ion battery storage system based on the BMS architecture includes: The internal resistance grouping module is used for internal resistance grouping specification division of the lithium ion battery pack according to the internal resistance range, and the internal resistance fluctuation amplitude and the terminal voltage deviation rate during the storage process are collected in real time in each battery cluster of the grouping specification, and the input charging current of all battery clusters in the lithium ion battery pack remains constant; The coefficient calculation module is used for outputting the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of each battery cluster of the grouping specification; The risk quantification module is used for quantifying the circulating current risk level of different grouping specification battery clusters based on the internal resistance sensitivity coefficient, and obtaining the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the charging current condition of different grouping specification battery clusters; The index calculation module is used for calculating the compensation priority index of the battery cluster based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of the internal resistance in the cluster, and the charging current condition; The equalization adjustment module is used for selecting the battery cluster with the highest compensation priority as the reference cluster, and during the storage process of the lithium ion battery pack, the internal resistance difference compensation voltage adjustment is implemented on the non-reference cluster battery by the BMS to make the terminal voltages of each cluster tend to be balanced; The internal resistance fluctuation amplitude and the terminal voltage deviation rate during the storage process are collected in real time in each battery cluster of the grouping specification, including: Any battery cluster to be analyzed is determined as a target battery cluster in each battery cluster of the grouping specification, and the remaining battery clusters in the grouping specification except the target battery cluster are defined as reference battery clusters; The real-time internal resistance, port voltage, historical internal resistance mean of the target battery cluster, and the port voltage mean of the reference battery cluster during the storage process are collected in real time; determine an internal resistance fluctuation amplitude based on a deviation degree of a real-time internal resistance of the target battery cluster from a historical mean value, and calculate an end voltage deviation rate based on a port voltage of the target battery cluster and a mean value of the port voltage of the reference battery cluster; The internal resistance fluctuation amplitude and the end voltage deviation rate of each grouping specification battery cluster are used to output an internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification, including: The internal resistance fluctuation amplitude of each grouping specification battery cluster is taken as a first difference factor, and the end voltage deviation rate is taken as a second difference factor; The first difference factor and the second difference factor are normalized, the weighting weight of the first difference factor and the second difference factor is dynamically adjusted based on the aging degree of each grouping specification battery cluster, a weighted sum index is formed, and the weighted sum index includes an internal resistance fluctuation weight; A sliding time window is introduced, the fluctuation frequency of the weighted sum index in the time window is counted, and the internal resistance sensitivity coefficient of each battery cluster under the corresponding grouping specification is calculated based on the first difference factor, the second difference factor, the weighted sum index and the fluctuation frequency; The calculation formula of the end voltage deviation rate is: ， wherein is the terminal voltage deviation rate, is the port voltage of the target battery cluster, is the port voltage mean value of the reference battery cluster; The calculation formula of the internal resistance sensitivity coefficient is: ， In the formula, is an internal resistance sensitivity coefficient, is an internal resistance fluctuation weight, is an internal resistance fluctuation amplitude, is an end voltage deviation rate, is a frequency correction coefficient, is a fluctuation frequency; The internal resistance sensitivity coefficient is used to quantify the circulating current risk level of different grouping specification battery clusters, including: The internal resistance sensitivity coefficient is used to quantify the inter-cluster average internal resistance difference of different grouping specification battery clusters; The circulating current risk level is calculated based on the inter-cluster average internal resistance difference and the internal resistance sensitivity coefficient of the battery cluster; The calculation formula of the circulating current risk level is as follows: ， In the formula, is the circulation risk level, is the internal resistance sensitivity coefficient, is the average inter-cluster internal resistance difference, and is the working condition coefficient, when fast charging is increased, and when slow charging is increased; The compensation priority index of the battery cluster is calculated based on the circulating current risk level, the number of intra-cluster batteries, the intra-cluster internal resistance standard deviation and the charging current working condition, including: A stage correction factor of a charging stage is determined based on the charging current working condition; The compensation priority index of the battery cluster is calculated according to the circulating current risk level, the number of intra-cluster batteries, the intra-cluster internal resistance standard deviation and the stage correction factor, and the calculation formula of the compensation priority index is as follows: ， In the formula, is a priority index, is a circulation risk level, is the number of batteries in a cluster, is the standard deviation of internal resistance in a cluster, is a stage correction factor, which is 1.2-1.5 in the constant current stage and 0.8-1.0 in the constant voltage stage. The battery cluster with the highest compensation priority is selected as the reference cluster, and during the charging process of the lithium ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster battery, so that the end voltages of the clusters tend to be balanced, including: The battery cluster with the highest compensation priority is selected as the reference cluster, and the average internal resistance of the battery of the reference cluster is calculated; The real-time internal resistance of each battery of the non-reference cluster is obtained, and the compensation voltage of the real-time internal resistance of each battery of the non-reference cluster is calculated according to the average internal resistance of the battery of the reference cluster, the real-time internal resistance of each battery of the non-reference cluster and the input charging current; During the charging process of the lithium ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster battery, so that the end voltages of the clusters tend to be balanced, and the calculation formula of the compensation voltage is: ， wherein, is the compensation voltage for the real-time internal resistance of each battery of the non-reference cluster, is the temperature compensation coefficient, is the real-time internal resistance of each battery of the non-reference cluster, is the average internal resistance of the batteries of the reference cluster, is the input charging current.

3. A lithium ion battery storage device based on a BMS architecture, characterized by, The device comprises a memory, a processor, and a BMS architecture-based lithium ion battery storage program stored on the memory and executable on the processor, and the BMS architecture-based lithium ion battery storage program is configured to implement the steps of the BMS architecture-based lithium ion battery storage method according to claim 1.

4. A storage medium, characterized by The storage medium stores a BMS architecture-based lithium ion battery storage program, and the BMS architecture-based lithium ion battery storage program is executable on the processor to implement the steps of the BMS architecture-based lithium ion battery storage method according to claim 1.

Images