Energy storage lithium battery series cluster capacity loss analysis method and device

Abstract

The invention discloses an energy storage lithium battery series cluster capacity loss analysis method and device. The method comprises the following steps: determining a lower limit voltage monomer Cell <-1 > and an upper limit voltage monomer Cell <-2 >; estimating the SOC, the capacity and the DC internal resistance DCR of the two monomers; deconstructing cluster capacity loss under the condition of consistency of different monomers, calculating loss delta Q1, delta Q2 and delta Q3 caused by inconsistency of SOC, capacity and DCR, and distributing the loss delta Q1, delta Q2 and delta Q3; the cluster capacity is calculated according to the loss result, if the two monomers are the same, the cluster capacity is the monomer capacity, and if the two monomers are different, the loss belonging to the Cell-2 is subtracted from the Cell-1 capacity. The device comprises a monomer determination module, a state estimation module, a loss deconstruction module and a capacity calculation module. The cluster capacity loss can be quantitatively analyzed, the cluster capacity estimation precision is improved, and the method is suitable for battery cluster management and maintenance of scenes such as large-scale energy storage power stations and electric vehicle battery packs.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery capacity analysis, and in particular relates to a method and device for analyzing capacity loss of a series-connected cluster of energy storage lithium batteries. Background Art

[0002] In the field of electrochemical energy storage, lithium battery clusters are the core components of energy storage systems, and their performance evaluation is crucial to the safe operation and energy management of the entire energy storage power station. Currently, lithium battery clusters in electrochemical energy storage power stations are typically composed of multiple lithium battery cells connected in series. In large-scale energy storage systems, the battery cluster is the smallest unit directly controlled by the power conversion system (PCS). This makes the accuracy of cluster capacity estimation directly affect the system's safety and energy management efficiency.

[0003] Existing methods for estimating cluster capacity are primarily based on the assumption of uniformity among battery cells. This assumes that battery cells within a cluster have good consistency in key parameters such as capacity, SOC (State of Charge), and internal resistance. However, in actual applications, battery cells within a cluster are inevitably affected by various inconsistencies, such as inconsistent SOC, inconsistent thermodynamic capacity, and inconsistent internal resistance.

[0004] This inconsistency can lead to serious cluster capacity loss. Specifically, during discharge, if a cell in the cluster first reaches the lower voltage limit, the entire cluster stops discharging to prevent overdischarge. At this point, other cells may not have reached the lower voltage limit, preventing their full capacity from being released. Similarly, during charging, if a cell first reaches the upper voltage limit, cluster charging is terminated. This prevents the full utilization of the capacity of most cells in the cluster, leading to cluster capacity loss.

[0005] The defects and shortcomings of existing technologies are mainly reflected in two aspects: on the one hand, there is a lack of quantitative analysis of this cluster capacity loss, and it is impossible to make targeted operation and maintenance adjustments based on different influencing factors to mitigate the loss; on the other hand, the current method of estimating cluster capacity using a single parameter (such as average capacity or maximum / minimum voltage thresholds) will cause significant errors and cannot meet the needs of large-scale energy storage systems for high-precision cluster capacity estimation. Summary of the Invention

[0006] To this end, the present invention provides a method and device for analyzing capacity loss of a series-connected energy storage lithium battery cluster, which solves the problem that the capacity loss of a series-connected energy storage battery cluster is difficult to quantitatively decompose and has large estimation errors.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for analyzing capacity loss of a series-connected energy storage lithium battery cluster, comprising the following steps:

[0008] Determine the lower voltage limit single cell Cell-1 and the upper voltage limit single cell Cell-2;

[0009] Estimate the SOC, capacity, and DC internal resistance (DCR) of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 to obtain parameter estimation results;

[0010] Deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results to obtain a quantitative analysis result of the cluster capacity loss;

[0011] The cluster capacity is calculated based on the quantitative analysis results of cluster capacity loss.

[0012] As a preferred solution for the capacity loss analysis method of a series-connected energy storage lithium battery cluster, the step of determining the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 specifically includes:

[0013] Estimate the SOC of all cells in the cluster. Find the time point t1 at which the lowest cell voltage appears within the time interval of the estimated cluster capacity. Estimate the SOC of all cells i in the cluster at time point t1. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ;

[0014] In SOC min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1;

[0015] In SOC max,i The maximum value is found, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2.

[0016] As a preferred solution of the capacity loss analysis method of a series-connected lithium battery cluster for energy storage, the step of estimating the SOC, capacity, and DC internal resistance (DCR) of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 specifically includes:

[0017] Within the time interval for estimating the cluster capacity, at any moment, estimate the SOCs of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2, which are denoted as z1 and z2 respectively;

[0018] Within the time interval for estimating the cluster capacity, the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 are estimated, which are denoted as Q1 and Q2 respectively;

[0019] Within the time interval for estimating the cluster capacity, the highest SOC for DCR calculation is selected, and the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 are estimated at this SOC, which are denoted as R1 and R2 respectively.

[0020] As a preferred solution for the capacity loss analysis method of a series cluster of energy storage lithium batteries, the step of deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results specifically includes:

[0021] Calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the monomer SOC, ΔQ1 = Q j (z j -z k );

[0022] Calculate the cluster capacity loss ΔQ2 caused by the inconsistency of monomer capacity, ΔQ2 = Q j -Q k ;

[0023] Calculate the cluster capacity loss ΔQ3 caused by monomer DCR inconsistency, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k;

[0024] Perform cluster capacity loss allocation, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell.

[0025] As a preferred solution for the capacity loss analysis method of a series-connected lithium battery cluster, the step of calculating the cluster capacity based on the quantitative analysis results of the cluster capacity loss specifically includes:

[0026] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for:

[0027] Q cluster =Q1=Q2

[0028] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery cell Cell-2, and the cluster capacity Qcluster for

[0029] The present invention also provides a capacity loss analysis device for a series-connected energy storage lithium battery cluster, comprising:

[0030] A cell determination module is used to determine a lower voltage cell Cell-1 and an upper voltage cell Cell-2;

[0031] A state estimation module is used to estimate the SOC, capacity and DC internal resistance DCR of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 to obtain parameter estimation results;

[0032] a loss deconstruction module, configured to deconstruct the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results, and obtain a quantitative analysis result of the cluster capacity loss;

[0033] The capacity calculation module is used to calculate the cluster capacity according to the quantitative analysis result of the cluster capacity loss.

[0034] As a preferred solution of the capacity loss analysis device for energy storage lithium battery series cluster, the monomer determination module includes:

[0035] The single cell SOC statistics unit is used to estimate the SOC of all single cells in the cluster. Within the time interval of estimating the cluster capacity, the time point t1 at which the lowest single cell voltage appears is found, and the SOC of all single cells i in the cluster at time point t1 is estimated. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ;

[0036] Lower limit voltage single cell battery search unit, used to min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1;

[0037] Upper limit voltage single cell battery search unit, used in SOC max,i The maximum value is found, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2.

[0038] As a preferred solution for the capacity loss analysis device of a series cluster of energy storage lithium batteries, in the state estimation module:

[0039] An SOC estimation unit is configured to select any time within a time interval for estimating the cluster capacity and estimate the SOCs of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2, which are denoted as z1 and z2 respectively;

[0040] a capacity estimation unit, configured to estimate the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 within a time interval for estimating the cluster capacity, which are denoted as Q1 and Q2 respectively;

[0041] The DCR estimation unit is configured to select a maximum SOC for DCR calculation within a time interval for estimating the cluster capacity, and estimate the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 at the SOC, which are denoted as R1 and R2 respectively.

[0042] As a preferred solution of the capacity loss analysis device for energy storage lithium battery series clusters, the loss deconstruction module includes:

[0043] The first cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the monomer SOC, ΔQ1 = Q j (z j -z k );

[0044] The second cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ2 caused by the inconsistency of the monomer capacity, ΔQ2 = Q j -Q k ;

[0045] The third cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ3 caused by the inconsistency of the monomer DCR, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k;

[0046] The cluster capacity loss allocation unit is used to allocate cluster capacity loss, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell.

[0047] As a preferred solution of the capacity loss analysis device for energy storage lithium battery series cluster, in the capacity calculation module:

[0048] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for:

[0049] Q cluster =Q1=Q2

[0050] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery cell Cell-2, and the cluster capacity Q cluster for

[0051] The present invention has the following advantages:

[0052] First, by first determining the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2, a benchmark is provided for subsequent analysis to ensure the accuracy of the analysis object. Then, the SOC, capacity, and DC internal resistance (DCR) of the two cells are estimated to obtain key parameters and lay a data foundation for quantitative analysis. Next, the cluster capacity loss under different cell consistency conditions is deconstructed to achieve a precise analysis of the loss. Finally, the cluster capacity is calculated to form a complete analysis process.

[0053] Second, after identifying the two cells, their key parameters are estimated. By selecting appropriate time points and methods to estimate SOC, employing reliable methods to estimate capacity, and estimating DCR at specific SOCs, key parameters reflecting the cell state can be accurately obtained. These accurate parameters provide reliable data support for subsequent analysis of cluster capacity loss and calculation of cluster capacity, avoiding biased analysis results due to inaccurate parameter estimates and improving the reliability of the entire analysis method.

[0054] Third, based on the estimated key parameters, the cluster capacity loss under different cell consistency conditions is deconstructed. By calculating the cluster capacity loss caused by cell SOC inconsistency, capacity inconsistency, and DCR inconsistency, and distributing the loss, a quantitative analysis of cluster capacity loss is achieved. This quantitative deconstruction clearly defines the contribution of each factor to cluster capacity loss, enabling operations and maintenance personnel to make targeted adjustments based on specific loss sources, effectively mitigating cluster capacity loss and improving the performance and service life of the battery cluster.

[0055] Fourth, the cluster capacity is accurately calculated based on the cluster capacity loss obtained through decomposition, taking into account whether Cell-1 and Cell-2 are the same cell. This cluster capacity calculation method, which considers multiple factors and actual conditions, avoids the significant errors caused by the existing technology of using a single parameter to estimate cluster capacity. It can more accurately reflect the actual capacity of the series battery cluster, providing a more precise basis for the energy management and safe operation of the energy storage system, and improving the energy management efficiency and safety of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0057] Figure 1 A schematic flow chart of a method for analyzing capacity loss of a series-connected cluster of energy storage lithium batteries provided in an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the capacity loss analysis device architecture of a series-connected lithium battery energy storage cluster provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figure 1 Embodiment 1 of the present invention provides a method for analyzing capacity loss of a series-connected energy storage lithium battery cluster, comprising the following steps:

[0062] S1, determining the lower voltage limit single cell Cell-1 and the upper voltage limit single cell Cell-2;

[0063] Specifically, in a series-connected lithium-ion battery cluster, individual cells exhibit inconsistent performance parameters, resulting in different voltage change rates during charge and discharge. During discharge, one cell will reach its lower voltage limit first, while during charge, another will reach its upper voltage limit first. These two cells play a decisive role in the overall cluster capacity. Identifying these two key cells provides a benchmark and research target for subsequent capacity loss analysis and cluster capacity calculations.

[0064] S2. Estimate the SOC, capacity, and DC internal resistance (DCR) of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 to obtain parameter estimation results;

[0065] Specifically, SOC (State of Charge) reflects the battery's current remaining charge, capacity is its ability to store electrical energy, and DC resistance (DCR) affects the battery's voltage change and energy loss during charge and discharge. Accurately estimating these three parameters is crucial for analyzing individual battery performance differences and calculating cluster capacity loss. By estimating these parameters for key individual batteries, we can obtain key data reflecting their actual state, laying the foundation for subsequent loss analysis and capacity calculation.

[0066] S3. Deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results to obtain a quantitative analysis result of the cluster capacity loss;

[0067] Specifically, inconsistent SOC, inconsistent capacity, and inconsistent DCR among individual cells are the primary causes of cluster capacity loss. By establishing a mathematical model to calculate and analyze cluster capacity loss under these three inconsistencies, we can quantitatively deconstruct cluster capacity loss. This clarifies the contribution of each factor to cluster capacity loss, providing a basis for subsequent O&M adjustments and cluster capacity calculations.

[0068] S4. calculating the cluster capacity according to the quantitative analysis result of the cluster capacity loss;

[0069] Specifically, cluster capacity calculations need to consider whether lower-voltage cell Cell-1 and upper-voltage cell Cell-2 are the same battery cell. If they are the same cell, cluster capacity is determined by the capacity of that cell. If they are different cells, cluster capacity is calculated based on the cluster capacity loss attributable to upper-voltage cell Cell-2. This calculation method fully accounts for the impact of cell inconsistencies on cluster capacity and more accurately reflects the actual cluster capacity.

[0070] In this embodiment, in step S1, the step of determining the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 specifically includes:

[0071] Estimate the SOC of all cells in the cluster. Find the time point t1 at which the lowest cell voltage appears within the time interval of the estimated cluster capacity. Estimate the SOC of all cells i in the cluster at time point t1. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ;

[0072] Specifically, during the time interval for estimating cluster capacity, the voltage of a single cell changes with the charge and discharge process. By finding the time point t1 when the cell voltage is lowest and the time point t2 when the cell voltage is highest, and estimating the SOC of each cell at these times, the state of charge of each cell at these two critical time points can be determined. This is because there is a certain correspondence between voltage and SOC. The extreme voltage points can reflect the SOC of a single cell in a specific state, providing data support for the subsequent identification of key cells.

[0073] In SOC min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1;

[0074] Specifically, during the discharge process, the SOC of a single cell will gradually decrease. When the SOC of a single cell reaches the minimum value, its voltage is likely to reach the lower limit voltage first. Therefore, to find the SOC min,i The single cell corresponding to the minimum value in is the single cell that is most likely to reach the lower limit voltage first during the discharge process, and is recorded as Cell-1.

[0075] In SOC max,i Find the maximum value, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2;

[0076] Specifically, during the charging process, the SOC of the single battery will gradually increase. When the SOC of a single battery reaches its maximum value, its voltage is likely to reach the upper limit voltage first. Therefore, to find the SOC max,i The single cell corresponding to the maximum value in is the single cell that is most likely to reach the upper limit voltage first during the charging process, and is recorded as Cell-2.

[0077] In this embodiment, in step S2, the step of estimating the SOC, capacity, and DC internal resistance DCR of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 specifically includes:

[0078] Within the time interval for estimating the cluster capacity, at any moment, estimate the SOCs of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2, which are denoted as z1 and z2 respectively;

[0079] Specifically, the SOC varies at different times. However, within the time interval for cluster capacity estimation, using Kalman filtering and the ampere-hour integration method to estimate the SOC of key cells at any given moment can reveal their state of charge at that moment. This parameter is crucial for subsequent calculations of cluster capacity loss caused by SOC inconsistencies. By comparing the SOC of two key cells at the same moment, we can analyze the impact of these differences on cluster capacity.

[0080] Within the time interval for estimating the cluster capacity, the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 are estimated, which are denoted as Q1 and Q2 respectively;

[0081] Specifically, battery capacity gradually degrades with use, and the capacity of different cells may vary. Estimating the capacity of key cells can clarify their respective energy storage capabilities. Capacity inconsistency is a significant factor leading to cluster capacity loss. By estimating Q1 and Q2 based on a cell lifespan model, cluster capacity loss due to capacity inconsistency can be calculated.

[0082] Within the time interval for estimating the cluster capacity, a highest SOC for DCR calculation is selected, and the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 are estimated at the SOC, which are denoted as R1 and R2, respectively.

[0083] Specifically, the DC internal resistance (DCR) is related to the battery's state of charge (SOC), and DCR values ​​may vary at different SOCs. The highest SOC is chosen for DCR calculation because battery performance is relatively stable at this SOC, allowing for a more accurate DCR estimate. DCR inconsistency can affect the battery's voltage drop during charge and discharge, leading to cluster capacity loss. Estimating the DCR of key cells at this SOC provides accurate parameters for calculating cluster capacity loss caused by DCR inconsistency.

[0084] Among them, within the time interval for estimating the cluster capacity, select the time period in which the current changes from a certain value to 0 instantly or from 0 to a certain value instantly. From these time periods, find the time period L with the highest battery cell SOC. Calculate the DCR of Cell-1 and Cell-2 based on the instantaneous voltage change when the current suddenly changes in time period L, and record them as R1 and R2, respectively.

[0085] In this embodiment, in step S3, the step of deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation result specifically includes:

[0086] Calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the monomer SOC, ΔQ1 = Q j (zj -z k ); When the SOC of the individual cells are inconsistent, during the charge and discharge process, the individual cells with lower SOC will reach the lower or upper voltage limit first, thereby limiting the charge and discharge depth of the entire cluster and causing cluster capacity loss. j Indicates the capacity of one of the key single cells, z j and z k They represent the SOC of two key single cells respectively. By multiplying their difference and capacity, the cluster capacity loss caused by SOC inconsistency can be quantified.

[0087] Calculate the cluster capacity loss ΔQ2 caused by the inconsistency of monomer capacity, ΔQ2 = Q j -Q k In a series cluster of cells with inconsistent capacities, the cell with smaller capacity will reach its charge and discharge limit first, thus limiting the capacity of the entire cluster. j and Q k They represent the capacities of two key single cells respectively, and their difference is the cluster capacity loss caused by capacity inconsistency, because the capacity of the cluster will be limited by the single cell with smaller capacity.

[0088] Calculate the cluster capacity loss ΔQ3 caused by monomer DCR inconsistency, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k; inconsistent DCR will lead to different voltage drops of single cells during the charge and discharge process. During the charging process, the voltage of the single cell with a larger DCR rises faster and is more likely to reach the upper limit voltage U first. max The formula uses the inverse function f of the OCV-charge capacity curve function -1 , according to the voltage U max -IR k Calculate the corresponding capacity and use the capacity Q of another key single battery j Subtracting this capacity gives the cluster capacity loss caused by DCR inconsistency.

[0089] Perform cluster capacity loss allocation, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell; in order to clarify the responsibility of each battery cell for the cluster capacity loss, loss allocation is required. Ensure that each loss component ΔQ iThe non-negative value is used to ensure the rationality of the calculation results. Attributing cluster capacity loss to the corresponding battery cells according to different loss formulas can provide clear targets for subsequent targeted operations and maintenance, such as focusing on or adjusting the battery cells that cause the greatest losses.

[0090] In this embodiment, in step S4, the step of calculating the cluster capacity based on the quantitative analysis result of the cluster capacity loss specifically includes:

[0091] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for:

[0092] Q cluster =Q1=Q2

[0093] When Cell-1 and Cell-2 are the same battery cell, it means that the cell reaches the voltage limit first during both discharge and charge. At this time, the capacity of the entire cluster is completely determined by the capacity of the cell. Therefore, the cluster capacity is equal to the capacity of the cell, that is, Q1 = Q2, so Q cluster =Q1=Q2.

[0094] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery cell Cell-2, and the cluster capacity Q cluster for

[0095]

[0096] When Cell-1 and Cell-2 are different monomers, the cluster capacity is affected by the two monomers and the inconsistency between them. The cluster capacity loss attributed to Cell-2 = represents the cluster capacity loss due to the existence of Cell-2 and its inconsistency with Cell-1. Therefore, by subtracting these losses from the capacity Q1 of Cell-1, we can get the cluster capacity Q after considering all influencing factors. cluster ,This calculation method fully reflects the comprehensive impact of single cell inconsistency on cluster capacity.

[0097] The application scenarios of the present invention are as follows:

[0098] Battery Cluster Management in Large-Scale Energy Storage Power Plants: Large-scale energy storage power plants typically contain numerous lithium battery clusters connected in series. Over long-term operation, these clusters can suffer from cluster capacity loss due to individual cell inconsistencies, impacting the overall performance and energy management efficiency of the energy storage plant. The method of this invention analyzes the capacity loss of each battery cluster and accurately calculates the cluster capacity. This provides a basis for energy scheduling, optimizing charging and discharging strategies, and maintaining and replacing battery clusters within the energy storage plant, ultimately improving its operational efficiency and economic viability.

[0099] Performance evaluation of electric vehicle battery packs: Electric vehicle battery packs typically consist of multiple lithium-ion cells connected in series. The capacity of the battery pack directly affects the vehicle's range. The method described in this paper can be used to analyze the capacity loss of electric vehicle battery packs during use, helping automakers and users understand the causes of battery pack performance degradation, develop appropriate battery maintenance plans, and extend the battery pack's service life.

[0100] Backup power system battery cluster maintenance: Backup power systems, such as those used in data centers, hospitals, and communication base stations, typically utilize lithium battery clusters connected in series as energy storage units. By analyzing the capacity loss of backup power system battery clusters using the method described in this invention, problems can be promptly identified, allowing for proactive maintenance and replacement, ensuring the system's proper functioning at critical moments.

[0101] Quality control during lithium battery production and R&D: During the production and R&D process, lithium battery performance needs to be evaluated and optimized. The method of this invention can be used to analyze the capacity loss of lithium battery series clusters, helping researchers understand the impact of individual cell inconsistencies on cluster performance, thereby optimizing lithium battery production processes and designs, and improving battery quality and performance.

[0102] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present disclosure. The multiple devices will interact with each other to complete the described method for analyzing capacity loss of a series-connected lithium battery energy storage system.

[0103] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] Example 2

[0105] See also Figure 2 Embodiment 2 of the present invention further provides a device for analyzing capacity loss of a series-connected energy storage lithium battery cluster, comprising:

[0106] The cell determination module 100 is configured to determine a lower voltage cell Cell-1 and an upper voltage cell Cell-2;

[0107] The state estimation module 200 is configured to estimate the SOC, capacity, and DC internal resistance (DCR) of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 to obtain parameter estimation results.

[0108] A loss deconstruction module 300 is used to deconstruct the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results to obtain a quantitative analysis result of the cluster capacity loss;

[0109] The capacity calculation module 400 is configured to calculate the cluster capacity according to the quantitative analysis result of the cluster capacity loss.

[0110] In this embodiment, the monomer determination module 100 includes:

[0111] The single cell SOC statistics unit 101 is used to estimate the SOC of all single cells in the cluster. Within the time interval of estimating the cluster capacity, the time point t1 at which the lowest cell voltage appears is found, and the SOC of all cells i in the cluster at time point t1 is estimated. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ;

[0112] The lower limit voltage single cell battery search unit 102 is used to min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1;

[0113] Upper limit voltage single cell battery search unit 103, used to max,i The maximum value is found, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2.

[0114] In this embodiment, in the state estimation module 200:

[0115] The SOC estimation unit 201 is configured to select any time within the time interval for estimating the cluster capacity and estimate the SOCs of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2, which are denoted as z1 and z2 respectively;

[0116] The capacity estimation unit 202 is configured to estimate the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 within a time interval for estimating the cluster capacity, which are denoted as Q1 and Q2 respectively.

[0117] The DCR estimating unit 203 is configured to select a maximum SOC for DCR calculation within a time interval for estimating the cluster capacity, and estimate the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 at the SOC, which are denoted as R1 and R2 respectively.

[0118] In this embodiment, the loss deconstruction module 300 includes:

[0119] The first cluster capacity loss calculation unit 301 is used to calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the cell SOC, ΔQ1 = Q j (z j -z k );

[0120] The second cluster capacity loss calculation unit 302 is used to calculate the cluster capacity loss ΔQ2 caused by the cell capacity inconsistency, ΔQ2 = Q j -Q k ;

[0121] The third cluster capacity loss calculation unit 303 is used to calculate the cluster capacity loss ΔQ3 caused by the inconsistency of the monomer DCR, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k;

[0122] The cluster capacity loss allocation unit 304 is used to allocate cluster capacity loss, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell.

[0123] In this embodiment, in the capacity calculation module 400:

[0124] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for:

[0125] Q cluster =Q1=Q2

[0126] If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery cell Cell-2, and the cluster capacity Q cluster for

[0127] It should be noted that the information interaction, execution process, etc. between the above-mentioned device modules are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0128] Example 3

[0129] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores program code for a method for analyzing capacity loss of a cluster of energy storage lithium batteries connected in series. The program code includes instructions for executing the method for analyzing capacity loss of a cluster of energy storage lithium batteries connected in series according to embodiment 1 or any possible implementation thereof.

[0130] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0131] Example 4

[0132] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0133] The processor and the memory communicate with each other via a bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute a capacity loss analysis method for a series cluster of energy storage lithium batteries in accordance with embodiment 1 or any possible implementation thereof.

[0134] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0135] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0136] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0137] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for analyzing capacity loss of a series-connected lithium battery energy storage cluster, characterized in that: The following steps are involved: Determine the lower voltage limit single cell Cell-1 and the upper voltage limit single cell Cell-2; Estimate the SOC, capacity, and DC internal resistance (DCR) of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 to obtain parameter estimation results; Deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results to obtain a quantitative analysis result of the cluster capacity loss; The cluster capacity is calculated based on the quantitative analysis results of cluster capacity loss.

2. The capacity loss analysis method of a series-connected energy storage lithium battery cluster according to claim 1, characterized in that: The step of determining the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 specifically includes: Estimate the SOC of all cells in the cluster. Find the time point t1 at which the lowest cell voltage appears within the time interval of the estimated cluster capacity. Estimate the SOC of all cells i in the cluster at time point t1. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ; In SOC min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1; In SOC max,i The maximum value is found, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2.

3. The capacity loss analysis method of a series-connected energy storage lithium battery cluster according to claim 1, characterized in that: The step of estimating the SOC, capacity, and DC internal resistance DCR of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 specifically includes: Within the time interval for estimating the cluster capacity, at any moment, estimate the SOCs of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2, which are denoted as z1 and z2 respectively; Within the time interval for estimating the cluster capacity, the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 are estimated, which are denoted as Q1 and Q2 respectively; Within the time interval for estimating the cluster capacity, the highest SOC for DCR calculation is selected, and the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 are estimated at this SOC, which are denoted as R1 and R2 respectively.

4. The method for analyzing capacity loss of a series-connected energy storage lithium battery cluster according to claim 3, wherein: The step of deconstructing the cluster capacity loss under different monomer consistency conditions based on the parameter estimation result specifically includes: Calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the monomer SOC, ΔQ1 = Q j (z j -z k ); Calculate the cluster capacity loss ΔQ2 caused by the inconsistency of monomer capacity, ΔQ2 = Q j -Q k ; Calculate the cluster capacity loss ΔQ3 caused by monomer DCR inconsistency, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k; Perform cluster capacity loss allocation, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell.

5. The method for analyzing capacity loss of a series-connected energy storage lithium battery cluster according to claim 4, wherein: The step of calculating the cluster capacity based on the quantitative analysis result of the cluster capacity loss specifically includes: If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for: Q cluster =Q1=Q2 If the lower voltage limit battery Cell-1 and the upper voltage limit battery Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery Cell-2, and the cluster capacity Q cluster for 6. A device for analyzing capacity loss of a series-connected lithium battery cluster, characterized in that: include: A cell determination module is used to determine a lower voltage cell Cell-1 and an upper voltage cell Cell-2; A state estimation module is used to estimate the SOC, capacity and DC internal resistance DCR of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2 to obtain parameter estimation results; a loss deconstruction module, configured to deconstruct the cluster capacity loss under different monomer consistency conditions based on the parameter estimation results, and obtain a quantitative analysis result of the cluster capacity loss; The capacity calculation module is used to calculate the cluster capacity according to the quantitative analysis result of the cluster capacity loss.

7. The capacity loss analysis device for a series-connected energy storage lithium battery cluster according to claim 6, characterized in that: The monomer determination module includes: The single cell SOC statistics unit is used to estimate the SOC of all single cells in the cluster. Within the time interval of estimating the cluster capacity, the time point t1 at which the lowest single cell voltage appears is found, and the SOC of all single cells i in the cluster at time point t1 is estimated. min,i , find the time point t2 when the highest cell voltage appears, and estimate the SOC of all cells i in the cluster at time point t2 max,i ; Lower limit voltage single cell battery search unit, used to min,i Find the minimum value among them, and the single cell corresponding to the minimum value is recorded as the lower limit voltage single cell Cell-1; Upper limit voltage single cell battery search unit, used in SOC max,i The maximum value is found, and the single cell corresponding to the maximum value is recorded as the upper limit voltage single cell Cell-2.

8. The capacity loss analysis device for a series-connected energy storage lithium battery cluster according to claim 6, characterized in that: In the state estimation module: An SOC estimation unit is configured to select any time within a time interval for estimating the cluster capacity and estimate the SOCs of the lower-limit voltage single battery Cell-1 and the upper-limit voltage single battery Cell-2, which are denoted as z1 and z2 respectively; a capacity estimation unit, configured to estimate the capacities of the lower-limit voltage single cell Cell-1 and the upper-limit voltage single cell Cell-2 within a time interval for estimating the cluster capacity, which are denoted as Q1 and Q2 respectively; The DCR estimation unit is configured to select a maximum SOC for DCR calculation within a time interval for estimating the cluster capacity, and estimate the DCRs of the lower-limit voltage cell Cell-1 and the upper-limit voltage cell Cell-2 at the SOC, which are denoted as R1 and R2 respectively.

9. The capacity loss analysis device for a series-connected energy storage lithium battery cluster according to claim 8, characterized in that: The loss deconstruction module includes: The first cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ1 caused by the inconsistency of the monomer SOC, ΔQ1 = Q j (z j -z k ); The second cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ2 caused by the inconsistency of the monomer capacity, ΔQ2 = Q j -Q k ; The third cluster capacity loss calculation unit is used to calculate the cluster capacity loss ΔQ3 caused by the inconsistency of the monomer DCR, ΔQ3 = Q j -f -1 (U max -IR k ), where f is the OCV-charge capacity curve function of single cell k; The cluster capacity loss allocation unit is used to allocate cluster capacity loss, where i and k are both 1 or 2, ensuring ΔQ i ≥0 (i=1,2,3), and the cluster capacity loss is attributed to the corresponding battery cell.

10. The capacity loss analysis device for a series-connected energy storage lithium battery cluster according to claim 7, characterized in that: In the capacity calculation module: If the lower voltage limit battery cell Cell-1 and the upper voltage limit battery cell Cell-2 are the same battery cell, the cluster capacity Q cluster for: Q cluster =Q1=Q2 If the lower voltage limit battery Cell-1 and the upper voltage limit battery Cell-2 are different battery cells, find all cluster capacity losses attributable to the upper voltage limit battery Cell-2, and the cluster capacity Q cluster for

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