Lithium ion battery power storage method and system based on BMS architecture

Abstract

The invention relates to the technical field of battery management, in particular to a lithium ion battery power storage method and system based on a BMS architecture, and the method comprises the steps: carrying out the grouping specification division of a battery pack according to an internal resistance range, and collecting the internal resistance fluctuation amplitude and terminal voltage deviation rate of each cluster in real time under a constant charging current working condition; calculating the internal resistance sensitivity coefficient of each cluster under the corresponding group based on the parameters, so as to quantify the cluster-level circulating current risk level, and synchronously obtaining the number of batteries in the cluster, the internal resistance standard deviation and the charging current working condition; calculating a compensation priority index of the battery cluster by integrating the circulating current risk grade, the number of batteries in the cluster, the internal resistance standard deviation and the charging working condition; and the battery cluster with the highest priority is selected as a reference cluster, internal resistance difference compensation voltage regulation is performed on the non-reference cluster through the BMS, the terminal voltage of each cluster is dynamically balanced, the circulation effect is inhibited, and the stability and energy utilization efficiency of the battery pack in the power storage process are improved.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a lithium-ion battery storage method and system based on a BMS architecture. Background Art

[0002] In lithium-ion battery energy storage systems, the battery cluster serves as the basic unit, and the consistency of its internal resistance directly impacts system performance. Due to differences in manufacturing processes, operating conditions, and aging, the internal resistance of different battery clusters is inherently discrete. Furthermore, during the storage process, the internal resistance fluctuates dynamically due to factors such as temperature and state of charge (SOC). When the battery pack is charged at a constant current, the internal resistance differences cause the voltage responses at the terminals of each cluster to be asynchronous, creating potential differences between clusters and inducing circulating currents. Circulating currents not only cause energy loss and accelerate battery aging, but can also lead to localized overheating, threatening system safety and reliability.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention 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 of lithium-ion battery packs varies, resulting in circulating current in the lithium-ion battery pack during the storage process.

[0005] To achieve the above objectives, the present invention provides a lithium-ion battery storage method based on a BMS architecture, the method comprising:

[0006] Dividing the lithium-ion battery pack into internal resistance groups according to their internal resistance ranges, and collecting the internal resistance fluctuation amplitude and terminal voltage deviation rate of each battery cluster in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant.

[0007] Based on the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification, the internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification is output;

[0008] quantifying the circulating current risk levels of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient, and obtaining the number of batteries within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current operating conditions of the battery clusters of different grouping specifications;

[0009] Calculating a compensation priority index for 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 operating conditions;

[0010] The battery cluster with the highest compensation priority is selected as the reference cluster. During the storage process of the lithium-ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage at each cluster tend to be balanced.

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

[0012] Determine any battery cluster to be analyzed in each battery cluster grouping specification as a target battery cluster, and define all other battery clusters in the grouping specification except the target battery cluster as reference battery clusters;

[0013] Real-time collection of the real-time internal resistance, port voltage, historical internal resistance average of the target battery cluster and the port voltage average of the reference battery cluster during the storage process;

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

[0015] Optionally, outputting the internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification based on the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification includes:

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

[0017] Normalizing the first difference factor and the second difference factor, and dynamically adjusting weights of the first difference factor and the second difference factor based on the aging degree of the battery clusters of each group specification to form a weighted sum index, wherein the weighted sum index includes an internal resistance fluctuation weight;

[0018] A sliding time window is introduced to count the fluctuation frequency of the weighted sum index within the time window, 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.

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

[0020]

[0021] Where, δ U is the terminal voltage deviation rate, U t is the terminal voltage of the target battery cluster, U r_avg is the mean port voltage 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] Where 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, quantifying the circulating current risk levels of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient includes:

[0026] quantifying the average internal resistance difference between battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient;

[0027] Calculating a circulating current risk level based on an average internal resistance difference between battery clusters and an internal resistance sensitivity coefficient;

[0028] The calculation formula for the circulation risk level is as follows:

[0029] R k =k1·S+k2·|ΔR a |

[0030] Where R k is the circulating risk level, S is the internal resistance sensitivity coefficient, |ΔR a | is the average internal resistance difference between clusters, k1 and k2 are operating condition coefficients, k2 increases during fast charging, and k1 increases during slow charging.

[0031] Optionally, the calculating of 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 operating condition includes:

[0032] Determining a stage correction factor for a charging stage based on the charging current operating condition;

[0033] 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. The calculation formula of the compensation priority index is as follows:

[0034]

[0035] Where P is the compensation priority index, R k is the circulation risk level, n is the number of batteries in the cluster, σ R is the standard deviation of the internal resistance within the cluster, η sis a stage correction factor, which is 1.2 to 1.5 in the constant current stage and 0.8 to 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 charging process of the lithium-ion battery pack, the BMS performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the terminal voltages of each cluster tend to be balanced, including:

[0037] Selecting a battery cluster with the highest compensation priority as a reference cluster, and calculating an average internal resistance of batteries in the reference cluster;

[0038] Obtaining the real-time internal resistance of each battery in the non-reference cluster, and calculating a compensation voltage for the real-time internal resistance of each battery in the non-reference cluster 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;

[0039] During the charging process of the lithium-ion battery pack, the BMS performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster terminal tend to be balanced. The calculation formula of the compensation voltage is:

[0040] ΔU i =K·(R i -R b )·I c

[0041] Where 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, R b is the average internal resistance of the battery in the benchmark cluster, I c is the input charging current.

[0042] In addition, to achieve the above objectives, the present invention further provides a lithium-ion battery storage system based on a BMS architecture, the lithium-ion battery storage system based on a BMS architecture comprising:

[0043] An internal resistance grouping module is used to divide lithium-ion battery packs into internal resistance grouping specifications according to their internal resistance ranges, and to collect the internal resistance fluctuation amplitude and terminal voltage deviation rate of each battery cluster in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant.

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

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

[0046] An index calculation module, configured to calculate a compensation priority index of a 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 operating conditions;

[0047] The balancing adjustment module is used to select the battery cluster with the highest compensation priority as the reference cluster. During the storage process of the lithium-ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced.

[0048] In addition, to achieve the above-mentioned objectives, the present invention also provides a lithium-ion battery storage device based on the BMS architecture, the device comprising: a memory, a processor, and a lithium-ion battery storage program based on the BMS architecture stored in the memory and executable on the processor, the lithium-ion battery storage program based on the BMS architecture being configured to implement the steps of the lithium-ion battery storage method based on the BMS architecture as described above.

[0049] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a lithium-ion battery storage program based on the BMS architecture is stored. When the lithium-ion battery storage program based on the BMS architecture is executed by a processor, the steps of the lithium-ion battery storage method based on the BMS architecture as described in any one of the above are implemented.

[0050] The present invention provides a lithium-ion battery storage method based on a BMS architecture. The method divides battery clusters into groups and specifications according to their internal resistance range, and combines the internal resistance fluctuation amplitude and terminal voltage deviation rate collected in real time to accurately calculate the internal resistance sensitivity coefficient, effectively identify the circulation risk of different clusters, provide a scientific basis for balancing control, and significantly improve the balancing efficiency. The circulation risk level is quantified based on the internal resistance sensitivity coefficient, and combined with the number of batteries in the cluster, the internal resistance standard deviation and the charging current operating conditions, the compensation priority index is dynamically calculated, and compensation is preferentially implemented for high-risk clusters, significantly reducing the negative impact of circulation on the battery pack and extending the battery life. 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 voltage of each cluster is balanced, energy loss is reduced, and the overall energy utilization efficiency of the battery pack is improved. Under constant charging current conditions, through real-time monitoring and adjustment by the BMS, the terminal voltage deviation caused by internal resistance differences is effectively suppressed, and the stability and reliability of the battery pack during the storage process are improved. By comprehensively considering multi-dimensional parameters such as internal resistance fluctuation, terminal voltage deviation, number of batteries in the cluster and charging current, the solution can adapt to the balancing needs under different working conditions and has strong versatility and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the structure of a lithium-ion battery storage device based on a BMS architecture in the hardware operating environment involved in the embodiment of the present invention;

[0052] Figure 2 This is a flow chart of a first embodiment of a lithium-ion battery power storage method based on a BMS architecture according to the present invention;

[0053] Figure 3 This is a structural block diagram of the first embodiment of the lithium-ion battery storage system based on the BMS architecture of the present invention.

[0054] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

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

[0056] Reference Figure 1 , Figure 1 This is a structural diagram of a lithium-ion battery storage device based on a BMS architecture in the hardware operating environment involved in an embodiment of the present invention.

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

[0058] Those skilled in the art will 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 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

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

[0060] exist Figure 1 In the lithium-ion battery storage device based on the BMS architecture shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to peripheral devices; 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 in the embodiment of the present invention.

[0061] Based on the above hardware structure, an embodiment of the lithium-ion battery storage method based on the BMS architecture of the present invention is proposed.

[0062] Reference Figure 2 , Figure 2 This is a flow chart of a first embodiment of a lithium-ion battery power storage method based on a BMS architecture of the present invention, and provides a first embodiment of a lithium-ion battery power storage method based on a BMS architecture of the present invention.

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

[0064] S10: Divide the lithium-ion battery pack into internal resistance groups according to the internal resistance range, and collect the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant.

[0065] It should be noted that a lithium-ion battery pack is an energy storage system composed of multiple lithium-ion battery clusters connected in series and parallel, used to store and release electrical energy. Its performance is affected by the consistency of each battery cluster, and internal resistance variation is a key factor leading to inter-cluster circulation. Internal resistance is the equivalent resistance within the battery, including ohmic internal resistance and polarization internal resistance. Ohmic internal resistance can be attributed to material contact resistance and electrolyte resistance, while polarization internal resistance can be attributed to electrochemical reaction polarization and concentration polarization. Internal resistance affects the battery terminal voltage response speed and is an important parameter for measuring battery status, which includes aging and state of charge. Internal resistance grouping refers to the division of multiple battery clusters within a lithium-ion battery pack into different groups based on their internal resistance range, such as high internal resistance group, medium internal resistance group, and low internal resistance group. The purpose of grouping is to reduce internal resistance variations among battery clusters within the same group through cluster management, thereby reducing the risk of inter-cluster circulation. A battery cluster is the basic building block of a lithium-ion battery pack. It can be composed of dozens to hundreds of battery cells connected in series and parallel. It has independent input and output ports and can be considered as a whole participating in the charging and discharging process of the battery pack.

[0066] It should be understood that real-time data acquisition refers to the high-frequency, real-time monitoring of key battery cluster parameters through sensors (such as current and voltage sensors) in the battery management system (BMS). This ensures the timeliness and accuracy of the data and provides real-time data support for subsequent control strategies. The storage process, or charging of a battery pack, refers to the process by which an external power source inputs electrical energy into the battery pack, allowing the batteries to store chemical energy. This specifically refers to the constant-current charging condition, where the input charging current remains constant. Under this condition, internal resistance differences have a more significant impact on the terminal voltage, easily leading to inter-cluster potential differences. The internal resistance fluctuation amplitude refers to the range of variation in the real-time internal resistance relative to the initial or average value during the charging process, as the internal resistance of a battery cluster is affected by factors such as temperature, SOC, and polarization. A larger fluctuation amplitude indicates poorer internal resistance stability and a greater likelihood that the terminal voltage response will deviate from expectations. The terminal voltage deviation rate is the ratio of the real-time terminal voltage of a single battery cluster to the average terminal voltage of all battery clusters within the same group. The deviation rate directly reflects the extent of inter-cluster potential differences and is a direct cause of circulating currents. Maintaining a constant input charging current means that the total external charging current remains fixed throughout the entire battery pack charging process, unaffected by changes in battery status. Under constant current conditions, differences in internal resistance lead to different voltage rise rates at each cluster terminal, creating a potential difference that is crucial for triggering circulating current.

[0067] Specifically, the real-time collection of the internal resistance fluctuation amplitude and terminal voltage deviation rate during the storage process in each battery cluster of group specifications includes:

[0068] Determine any battery cluster to be analyzed in each battery cluster grouping specification as a target battery cluster, and define all other battery clusters in the grouping specification except the target battery cluster as reference battery clusters;

[0069] Real-time collection of the real-time internal resistance, port voltage, historical internal resistance average of the target battery cluster and the port voltage average of the reference battery cluster during the storage process;

[0070] The internal resistance fluctuation amplitude is determined based on the deviation between the real-time internal resistance of the target battery cluster and the historical average, and the terminal voltage deviation rate is calculated based on the port voltage of the target battery cluster and the average port voltage of the reference battery cluster.

[0071] It should be noted that the target battery cluster refers to a single battery cluster selected for analysis within the grouping specification. Each analysis targets a single target cluster, traversing each cluster individually or filtering as needed. The target battery cluster serves as a specific evaluation unit for internal resistance fluctuation and terminal voltage deviation, and its individual state differences are quantified by comparison with other clusters in the same group (reference cluster). The reference battery cluster is the collection of all other battery clusters within the grouping specification, excluding the target battery cluster, serving as the reference group within the same group. The reference battery cluster provides a benchmark for the average state within the group, such as the mean terminal voltage, to measure the degree of deviation of the target cluster. Based on the assumption that battery clusters within the same group have similar internal resistance characteristics, and since the groups are divided by internal resistance range, the overall performance of the reference cluster can be considered 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 of the target battery cluster at the current sampling moment, which includes the dynamic combined value of the ohmic internal resistance and polarization internal resistance. Real-time internal resistance can be measured in real time using either the AC impedance method or the DC pulse method. It is affected by factors such as charging current, SOC, and temperature, and reflects the real-time state of the battery's internal electrochemical processes. The real-time internal resistance is an instantaneous value, while the historical internal resistance mean is the average value over a period of time (e.g., the last N sampling points). This 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 at the positive and negative output ports of the battery cluster, a comprehensive reflection of the battery's electromotive force, internal resistance voltage drop, and polarization voltage. The historical internal resistance mean is the average internal resistance of the target battery cluster over a period of time (e.g., the last 5 minutes or 100 sampling points during charging). The port voltage mean is the average port voltage of the reference battery cluster at the same time. The terminal voltage deviation rate represents the deviation of the target battery cluster's real-time internal resistance from the historical internal resistance mean. The terminal voltage deviation rate quantifies the dynamic fluctuation of internal resistance and reflects the stability of the battery cluster's internal state. Large deviations indicate increased polarization, poor internal contact, or accelerated aging.

[0072] S20: Outputting an internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of the battery cluster of each group specification.

[0073] It should be noted that the internal resistance sensitivity coefficient is a quantitative indicator of the sensitivity of the terminal voltage to changes in the internal resistance of a battery cluster under specific grouping specifications. This coefficient reflects the ability of dynamic changes in internal resistance to affect the potential difference between clusters by integrating the internal resistance fluctuation amplitude and the terminal voltage deviation rate. It is a core parameter for assessing circulating current risk. The internal resistance fluctuation amplitude reflects the stability of the battery cluster's internal resistance. The greater the fluctuation, the more unstable the internal resistance and the more significantly the terminal voltage is affected by the internal resistance. The terminal voltage deviation rate reflects the degree of deviation between the target cluster and 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 for circulating current. The internal resistance sensitivity coefficient is a comprehensive mapping of the two, reflecting the ability of internal resistance changes to cause abnormal deviations in terminal voltage under constant charging current. The higher the coefficient, the higher the risk of circulating current caused by internal resistance problems in the cluster.

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

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

[0076] Normalizing the first difference factor and the second difference factor, and dynamically adjusting weights of the first difference factor and the second difference factor based on the aging degree of the battery clusters of each group specification to form a weighted sum index, wherein the weighted sum index includes an internal resistance fluctuation weight;

[0077] A sliding time window is introduced to count the fluctuation frequency of the weighted sum index within the time window, 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 of the target battery cluster's real-time internal resistance from its historical mean. As the core parameter reflecting the dynamic changes in internal resistance, the first difference factor reflects the vertical difference between the battery cluster's internal resistance characteristics and its historical state. The second difference factor directly corresponds to the terminal voltage deviation rate, that is, the ratio (absolute value) of the difference between the terminal voltage of the target battery cluster and the mean of the reference cluster in the same group. The second difference factor reflects the horizontal difference between the target cluster and other clusters in the same group. Normalization is the process of converting the first and second difference factors, which have different dimensions and widely varying value ranges, into dimensionless, uniform ranges. Aging is a comprehensive indicator measuring the aging state of a battery cluster, typically assessed through parameters such as internal resistance growth amplitude, capacity decay rate, and number of cycles. New batteries have a low degree of aging and high internal resistance stability. Terminal voltage deviations are primarily caused by short-term polarization effects, so the second difference factor (voltage deviation rate) is weighted more heavily. Aged batteries have a high degree of aging: internal resistance increases significantly with cycle number and fluctuates more strongly. Internal resistance fluctuations have a more significant impact on voltage deviations, so the first difference factor (internal resistance fluctuation) is weighted more heavily. The weighted weights quantify the contribution of the first and second difference factors to the final sensitivity coefficient. They are dynamically adjusted based on aging rather than being fixed values. The weighted sum index is the sum of the normalized first and second difference factors according to their weights. This index transforms the differences in the vertical (time dimension) and horizontal (group dimension) dimensions into a single comprehensive index, reflecting the degree to which the current state of the battery cluster deviates from the normal state within the group. In time series data processing, a sliding time window sets a fixed time window, such as the last 5 minutes or 100 sampling points. The window slides forward as new data is collected, retaining only the most recent data within the window for analysis. The sliding time window is used to minimize the impact of random interference on the results and focus on trend changes. The fluctuation frequency is the number of times the weighted sum index exceeds a preset threshold within the sliding time window, reflecting the instability of the battery cluster state. A higher frequency indicates that the cluster has frequently experienced abnormal deviations in the recent past. Even if the current deviation does not exceed the standard, it may indicate potential risks, such as the intensification of internal resistance fluctuations.

[0079] In this real-time example, the calculation formula of the terminal voltage deviation rate is:

[0080]

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

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

[0083] S=γ·ΔR r +(1-γ)·δ U+δ·f

[0084] Where 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 mean value of the same reference cluster. The target battery cluster port voltage at the current moment is U t The integrated output value includes electromotive force, internal resistance voltage drop, and polarization voltage. The internal resistance sensitivity coefficient S indicates the risk of abnormal terminal voltage and circulating current caused by the internal resistance characteristics of the battery cluster. The larger the value, the higher the risk. The internal resistance fluctuation weight γ is dynamically adjusted according to the aging degree of the battery cluster. For example, the more severe the aging, the closer γ is to 1, which emphasizes the impact of internal resistance fluctuation. The internal resistance fluctuation amplitude ΔR r This is the deviation of the target cluster's real-time internal resistance from its historical mean. The frequency correction factor δ is used to quantify the impact of the fluctuation frequency on the sensitivity coefficient and can be determined through engineering debugging or algorithm optimization.

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

[0087] It should be noted that the circulating current risk level is a graded assessment of the likelihood and severity of inter-cluster circulating current caused by terminal voltage differences during charging, based on the internal resistance sensitivity coefficient, the number of cells within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current conditions. The number of cells within a cluster refers to the number of cells connected in series or parallel within a single battery cluster. The greater the number of cells within a cluster, the more likely initial or aging differences in parameters such as internal resistance and capacity will accumulate between cells, increasing the probability that the cluster terminal voltage will deviate from the group mean. The standard deviation of the internal resistance within a cluster refers to the standard deviation of the internal resistance of all cells within a single battery cluster. A smaller standard deviation indicates more uniform internal resistance within the cluster, closer polarization characteristics and aging levels, and lower terminal voltage dispersion. Conversely, a larger standard deviation indicates a higher likelihood of cluster terminal voltage fluctuations due to localized internal resistance anomalies, such as poor contact between individual cells or thickened SEI films. Even if the internal resistance fluctuation within a cluster is small, a significantly increased standard deviation can amplify the impact of internal resistance fluctuations on the terminal voltage. Cell variations can lead to uneven distribution of internal resistance voltage drops, and the cluster terminal voltage can be dominated by abnormal cells. The charging current profile describes the magnitude and variation of the current flowing through the battery cluster during the charging process, including the current during the constant current phase, the current decay curve during the constant voltage phase, and the pulse charging current waveform. Under high current conditions, fluctuations in the same internal resistance can lead to larger voltage deviations, directly increasing the internal resistance sensitivity coefficient. Battery polarization response times vary under different current conditions (high current polarization is more pronounced, and recovery is slower), leading to differences in the dynamic characteristics of voltage deviations between clusters. For example, voltage deviations exhibit periodic fluctuations during pulse charging.

[0088] Specifically, the quantifying the circulating current risk levels of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient includes:

[0089] quantifying the average internal resistance difference between battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient;

[0090] Calculating a circulating current risk level based on an average internal resistance difference between battery clusters and an internal resistance sensitivity coefficient;

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

[0092] R k =k1·S+k2·|ΔR a |

[0093] Where R k is the circulating risk level, S is the internal resistance sensitivity coefficient, |ΔR a | is the average internal resistance difference between clusters, 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 aThe core indicator used to characterize the internal resistance differences between battery clusters of different grouping specifications is the absolute value of the difference between the mean internal resistance of the target battery cluster and the reference cluster. The operating condition coefficients k1 and k2 are weight coefficients dynamically adjusted according to the charging current operating conditions, used to balance the internal resistance sensitivity coefficient S and the average internal resistance difference |ΔR between clusters. a |The contribution to the risk level is the bridge connecting the theoretical model and actual working conditions.

[0095] S40: Calculating a 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 operating condition.

[0096] It should be noted that the compensation priority index is a quantitative indicator used to characterize the priority compensation (such as balancing control and current regulation) required for the battery cluster during the charging process. By comprehensively considering the circulation risk, intra-cluster consistency, scale effect and operating condition characteristics, it provides the BMS with a decision-making basis for accurate compensation sorting.

[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 condition, including:

[0098] Determining a stage correction factor for a charging stage based on the charging current operating condition;

[0099] 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. The calculation formula of the compensation priority index is as follows:

[0100]

[0101] Where P is the compensation priority index, R k is the circulation risk level, n is the number of batteries in the cluster, σ R is the standard deviation of the internal resistance within the cluster, η s is a stage correction factor, which is 1.2 to 1.5 in the constant current stage and 0.8 to 1.0 in the constant voltage stage.

[0102] It should be noted that the stage correction factor η s This factor reflects the dynamic adjustment coefficient for compensation priority at different stages of the charging process. Its core function is to adjust the assessment weight according to the current characteristics and risk characteristics of the charging stage. During the constant current stage, the current is constant, and the internal resistance voltage drop is the main component of the terminal voltage. The impact of the internal resistance difference between clusters and the discreteness within the cluster on the voltage deviation is directly amplified, and the risk escalates rapidly. Therefore, η sTaking 1.2 to 1.5, the compensation priority is improved; in the constant voltage stage, the voltage is constant, the charging current gradually decreases with the increase of battery SOC, the proportion of internal resistance voltage drop decreases, polarization effect and voltage balance become the core concerns, and the risk urgency decreases, so η s Take 0.8 to 1.0, appropriately reduce the compensation priority to avoid over-compensation. In the constant current stage, when the battery terminal voltage does not reach the charging cut-off voltage (such as 4.2V for lithium batteries), the BMS maintains constant current charging. At this time, η in the formula s Force the high correction factor to be enabled; in the constant voltage stage, when the voltage reaches the cut-off voltage, the BMS switches to the constant voltage mode, and the current gradually decays to the cut-off current (such as 0.05C). At this time, η s Automatically switches to a low correction factor. The larger the number of batteries in the cluster, the wider the impact of the cluster failure, and the higher the need for compensation;

[0103] If the standard deviation of the internal resistance within a cluster is large, that is, the cluster dispersion is severe, although compensation should theoretically be prioritized, the large denominator in the formula results in a small ratio, which may seem to reduce the priority, but in reality it is a correction to the cost-effectiveness of compensation: clusters with excessive dispersion may require more complex balancing strategies (such as replacing cells), and relying solely on BMS compensation is limited. Therefore, the denominator is used to suppress its priority and guide manual intervention.

[0104] If the internal resistance standard deviation of the cluster is small and the consistency is good, but the number of batteries in the cluster is large, it means that the cluster is a large-scale high-quality cluster. Once a risk occurs, such as R k Increased, need to be protected first, so the ratio amplifies its priority. Constant current stage focuses on risk prevention and control: through η s >1 forces the compensation priority of all clusters to be increased to ensure fast response under high current. For example, when P of a cluster suddenly increases during the constant current phase, the BMS can start hardware balancing within 200ms. In the constant voltage phase, energy efficiency is optimized, η s <1 reduces the compensation priority, allowing the BMS to allocate resources to clusters that really need balancing. For example, only compensate clusters with P>80 to avoid ineffective balancing energy consumption under low 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 performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced.

[0106] It should be noted that during the battery pack balancing process, the reference cluster is selected as the reference for terminal voltage balancing. Essentially, it is a dynamically generated optimal voltage target cluster. As the absolute reference point for voltage balancing, the terminal voltages of non-reference clusters must converge to it, thereby eliminating inter-cluster terminal voltage deviations. The terminal voltage curve of the reference cluster represents the ideal charging trajectory of the battery pack under the current operating conditions, and other clusters synchronize with it through compensation adjustments. The cluster with the highest compensation priority index (i.e., the cluster with the highest risk level, significant scale effect, and high correction factor) can be directly selected. If multiple clusters have the same compensation priority index, the cluster with the smallest internal resistance standard deviation is prioritized. The cluster with the real-time terminal voltage closest to the battery pack average voltage is selected to reduce the adjustment range and minimize energy consumption. Only one reference cluster can exist at a time to avoid balancing conflicts caused by multiple reference points. The P value is recalculated every 500ms. If the P value of the original reference cluster falls outside the top three, a reference switch is triggered. The internal resistance difference is the difference in equivalent internal resistance between the target cluster and the reference cluster, which directly leads to terminal voltage deviations at the same charging current. The compensation voltage is a reverse regulation voltage generated by the BMS through the balancing circuit, which is used to offset the terminal voltage deviation caused by the internal resistance difference. It can be a compensation amount opposite to the internal resistance voltage drop superimposed in the target cluster charging circuit.

[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 performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced, including:

[0108] Selecting a battery cluster with the highest compensation priority as a reference cluster, and calculating an average internal resistance of batteries in the reference cluster;

[0109] Obtaining the real-time internal resistance of each battery in the non-reference cluster, and calculating a compensation voltage for the real-time internal resistance of each battery in the non-reference cluster 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 performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster terminal tend to be balanced. The calculation formula of the compensation voltage is:

[0111] ΔU i =K·(R i -R b )·I c

[0112] Where 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, R b is the average internal resistance of the battery in the benchmark cluster, Ic is the input charging current.

[0113] It should be noted that the average internal resistance of the battery in the reference cluster serves as the global reference for internal resistance balancing across the entire battery pack. Each battery in the non-reference cluster must converge to this value to eliminate terminal voltage deviations caused by internal resistance differences. The average internal resistance of the battery in the reference cluster reflects the overall polarization characteristics of the reference cluster. The lower the average internal resistance, the greater the cluster's charge acceptance, making it an ideal target for compensation adjustments in other clusters. The real-time internal resistance of each battery in the non-reference cluster is the equivalent internal resistance measured in real time during charging for the i-th battery in the non-reference cluster, which includes both ohmic and polarization resistances. The temperature compensation coefficient is a correction factor that reflects the temperature variation of the battery's internal resistance and is used to compensate for internal resistance measurement deviations caused by temperature differences. Internal resistance decreases with increasing temperature, a negative temperature characteristic. Without compensation, the actual voltage drop for the same internal resistance difference at high temperatures will decrease, resulting in overcompensation. The opposite effect occurs at low temperatures, potentially leading to undercompensation.

[0114] It should be understood that if R i >R b , the battery internal resistance is too large, and the terminal voltage is too high under the same current. It is necessary to apply a reverse compensation voltage through the BMS, which is opposite to the charging voltage to offset part of the internal resistance voltage drop and reduce the terminal voltage to the reference level; if R i <R b , then the internal resistance of the battery is too small and the terminal voltage is too low. It is necessary to apply a compensating voltage in the same direction as the charging voltage to increase the terminal voltage to the reference level. This can actually be achieved by adjusting the charging current distribution to avoid direct voltage boosting.

[0115] In addition, an embodiment of the present invention further proposes a storage medium, on which a lithium-ion battery storage program based on a BMS architecture is stored. When the lithium-ion battery storage program based on the BMS architecture is executed by a processor, the steps of the lithium-ion battery storage method based on the BMS architecture as described above are implemented.

[0116] In addition, refer to Figure 3 The embodiment of the present invention further provides a lithium-ion battery storage system based on a BMS architecture, wherein the lithium-ion battery storage system based on the BMS architecture includes:

[0117] The internal resistance grouping module 10 is used to divide the lithium-ion battery pack into internal resistance group specifications according to the internal resistance range, and collect the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant;

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

[0119] a risk quantification module 30 for 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 batteries within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current operating conditions of the battery clusters of different grouping specifications;

[0120] An index calculation module 40 is configured to calculate a compensation priority index of a battery cluster based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of internal resistance in the cluster, and the charging current operating conditions;

[0121] The balancing adjustment module 50 is used to 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 performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced.

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

[0123] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0124] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent superiority or inferiority of the embodiments. In a unit claim that lists several systems, several of these systems may be embodied by the same item of hardware. The use of the terms first, second, and third, etc., does not denote any order and should be interpreted as designations.

[0125] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a magnetic disk, or an optical disk), and includes a number of instructions for enabling an end-user device (which can be a mobile phone, a computer, a server, an air conditioner, or a network user device, etc.) to execute the methods described in the various embodiments of the present invention.

[0126] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A lithium-ion battery storage method based on a BMS architecture, characterized in that: The method comprises: Dividing the lithium-ion battery pack into internal resistance groups according to their internal resistance ranges, and collecting the internal resistance fluctuation amplitude and terminal voltage deviation rate of each battery cluster in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant. Based on the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification, the internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification is output; quantifying the circulating current risk levels of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient, and obtaining the number of batteries within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current conditions of the battery clusters of different grouping specifications; Calculating a compensation priority index for 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 operating conditions; The battery cluster with the highest compensation priority is selected as the reference cluster. During the storage process of the lithium-ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage at each cluster tend to be balanced.

2. The lithium-ion battery power storage method based on the BMS architecture according to claim 1, characterized in that: The real-time collection of the internal resistance fluctuation amplitude and terminal voltage deviation rate during the storage process in each battery cluster of group specifications includes: Determine any battery cluster to be analyzed in each battery cluster grouping specification as a target battery cluster, and define all other battery clusters in the grouping specification except the target battery cluster as reference battery clusters; Real-time collection of the real-time internal resistance, port voltage, historical internal resistance average of the target battery cluster and the port voltage average of the reference battery cluster during the storage process; The internal resistance fluctuation amplitude is determined based on the deviation between the real-time internal resistance of the target battery cluster and the historical average, and the terminal voltage deviation rate is calculated based on the port voltage of the target battery cluster and the average port voltage of the reference battery cluster.

3. The lithium-ion battery power storage method based on the BMS architecture according to claim 2, characterized in that: The outputting of the internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification based on the internal resistance fluctuation amplitude and the terminal voltage deviation rate of the battery cluster of each group specification includes: The internal resistance fluctuation amplitude of each battery cluster of each group specification is used as a first difference factor, and the terminal voltage deviation rate is used as a second difference factor; Normalizing the first difference factor and the second difference factor, and dynamically adjusting weights of the first difference factor and the second difference factor based on the aging degree of the battery clusters of each group specification to form a weighted sum index, wherein the weighted sum index includes an internal resistance fluctuation weight; A sliding time window is introduced to count the fluctuation frequency of the weighted sum index within the time window, 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.

4. The lithium-ion battery power storage method based on the BMS architecture according to claim 3, characterized in that: The calculation formula of the terminal voltage deviation rate is: Where, δ U is the terminal voltage deviation rate, U t is the terminal voltage of the target battery cluster, U r_avg is the mean port voltage of the reference battery cluster; The calculation formula of the internal resistance sensitivity coefficient is: S=γ·ΔR r +(1-c)·d U +δ·f Where 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.

5. The lithium-ion battery power storage method based on the BMS architecture according to claim 1, characterized in that: The quantification of the circulating current risk levels of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient includes: quantifying the average internal resistance difference between battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient; Calculating a circulating current risk level based on an average internal resistance difference between battery clusters and an internal resistance sensitivity coefficient; The calculation formula for the circulation risk level is as follows: R k =k1·S+k2·|ΔR a | Where R k is the circulating risk level, S is the internal resistance sensitivity coefficient, |ΔR a | is the average internal resistance difference between clusters, k1 and k2 are operating condition coefficients, k2 increases during fast charging, and k1 increases during slow charging.

6. The lithium-ion battery power storage method based on the BMS architecture according to claim 5, characterized in that: 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 operating condition, including: Determining a stage correction factor for a charging stage based on the charging current operating 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. The calculation formula of the compensation priority index is as follows: Where P is the compensation priority index, R k is the circulation risk level, n is the number of batteries in the cluster, σ R is the standard deviation of the internal resistance within the cluster, η s is a stage correction factor, which is 1.2 to 1.5 in the constant current stage and 0.8 to 1.0 in the constant voltage stage.

7. The lithium-ion battery power storage method based on the BMS architecture according to claim 1, characterized in that: 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 performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced, including: Selecting a battery cluster with the highest compensation priority as a reference cluster, and calculating an average internal resistance of batteries in the reference cluster; Obtaining the real-time internal resistance of each battery in the non-reference cluster, and calculating a compensation voltage for the real-time internal resistance of each battery in the non-reference cluster 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; During the charging process of the lithium-ion battery pack, the BMS performs internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster terminal tend to be balanced. The calculation formula of the compensation voltage is: ΔU i =K·(R i -R b )·AND c Where, 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, R b is the average internal resistance of the battery in the benchmark cluster, I c is the input charging current.

8. A lithium-ion battery storage system based on a BMS architecture, characterized in that: The lithium-ion battery storage system based on the BMS architecture includes: An internal resistance grouping module is used to divide lithium-ion battery packs into internal resistance grouping specifications according to their internal resistance ranges, and to collect the internal resistance fluctuation amplitude and terminal voltage deviation rate of each battery cluster in real time during the storage process. The input charging current of all battery clusters in the lithium-ion battery pack is kept constant. A coefficient calculation module is used to output the internal resistance sensitivity coefficient of each battery cluster under the corresponding group specification based on the internal resistance fluctuation amplitude and terminal voltage deviation rate of the battery cluster of each group specification; a risk quantification module, configured to quantify the circulating current risk level of battery clusters of different grouping specifications based on the internal resistance sensitivity coefficient, and obtain the number of batteries within the cluster, the standard deviation of the internal resistance within the cluster, and the charging current operating conditions of the battery clusters of different grouping specifications; An index calculation module, configured to calculate a compensation priority index of a battery cluster based on the circulating current risk level, the number of batteries in the cluster, the standard deviation of internal resistance in the cluster, and the charging current operating conditions; The balancing adjustment module is used to select the battery cluster with the highest compensation priority as the reference cluster. During the storage process of the lithium-ion battery pack, the BMS is used to implement internal resistance difference compensation voltage adjustment on the non-reference cluster batteries to make the voltage of each cluster tend to be balanced.

9. A lithium-ion battery storage device based on a BMS architecture, characterized in that: The device includes: a memory, a processor, and a lithium-ion battery storage program based on the BMS architecture stored in the memory and executable on the processor, wherein the lithium-ion battery storage program based on the BMS architecture is configured to implement the steps of the lithium-ion battery storage method based on the BMS architecture as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a lithium-ion battery storage program based on the BMS architecture. When the lithium-ion battery storage program based on the BMS architecture is executed by the processor, the steps of the lithium-ion battery storage method based on the BMS architecture according to any one of claims 1 to 7 are implemented.

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