Method for evaluating state of health of an electric cell and energy storage system

Abstract

This application relates to a method for assessing the health status of a battery cell and an energy storage system. Multiple consecutive sampling times satisfying constant current discharge conditions are determined. For at least one battery cell in the battery, based on the voltage of each cell collected at the multiple sampling times, a relative health coefficient corresponding to the target state of charge (SOC) interval for the at least one cell is determined. Based on the relative health coefficient of the at least one cell, the health status of the at least one cell is determined. This application divides the battery cell into a first SOC interval and a second SOC interval based on the voltage sensitivity differences in different SOC intervals, determines the relative health coefficient for each SOC interval, and merges the relative health coefficients of the two SOC intervals to determine the health status of the cell. This approach retains the detection sensitivity of the high-sensitivity interval while utilizing the stability characteristics of the low-sensitivity interval to constrain the health status calculation results, effectively balancing the identification sensitivity and result stability of the battery cell health status assessment.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a method for assessing the health status of a battery cell and an energy storage system. Background Technology

[0002] The health status of a battery cell directly determines its usable capacity, charging and discharging internal resistance, and voltage characteristics. Deterioration in health status can lead to problems such as capacity decay, increased internal resistance, and poor consistency, ultimately resulting in safety risks. Therefore, estimating the health status of battery cells is extremely important.

[0003] Currently, battery management systems (BMS) typically estimate the health status of batteries using indicators such as cumulative charge and discharge, operating time, ambient temperature, and cycle count.

[0004] However, this type of method can only be used to assess the health of the battery, and cannot assess the health of individual cells within the battery. Summary of the Invention

[0005] Therefore, it is necessary to provide a cell health status assessment method and energy storage system that can evaluate the health status of individual cells within a battery, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a method for assessing the health status of a battery cell, the method comprising:

[0007] Determine multiple consecutive sampling times that satisfy the constant current discharge condition; the multiple sampling times correspond to multiple states of charge in the target state of charge interval, which includes a first state of charge interval and a second state of charge interval, and the upper limit of the first state of charge interval is less than the lower limit of the second state of charge interval.

[0008] For at least one cell in a battery, the relative health coefficient of at least one cell in the target state of charge range is determined based on the voltage of each cell collected at multiple sampling times.

[0009] The health status of at least one cell is determined based on the relative health coefficient of at least one cell.

[0010] In one embodiment, at least one battery cell includes multiple battery cells. Based on the voltages of each battery cell collected at multiple sampling times, a relative health coefficient for at least one battery cell within a target state of charge range is determined, including:

[0011] Based on the voltage of each cell collected at multiple sampling times, determine the maximum voltage deviation of at least one cell in the target state of charge range.

[0012] Based on at least one maximum voltage deviation and the preset voltage change corresponding to the target state of charge range, determine the target voltage health index of at least one cell.

[0013] Based on the target voltage health index of multiple cells, determine the relative health coefficient of at least one cell.

[0014] In one embodiment, based on the voltages of each cell collected at multiple sampling times, the maximum voltage deviation of at least one cell within the target state of charge range is determined, including:

[0015] For each sampling time, the average voltage corresponding to the sampling time is determined based on the voltage of each cell collected at the sampling time.

[0016] Determine the voltage deviation between the voltage of at least one cell and the average voltage;

[0017] For at least one battery cell, determine the maximum voltage deviation of at least one battery cell in the target state of charge range from the voltage deviations of at least one battery cell at each sampling time.

[0018] In one embodiment, a target voltage health index for at least one cell is determined based on at least one maximum voltage deviation and a preset voltage change corresponding to a target state of charge range, including:

[0019] The initial voltage health index of at least one cell is determined based on at least one maximum voltage deviation and the preset voltage change corresponding to the target state of charge range.

[0020] At least one initial voltage health index is subjected to a limiting process to obtain the target voltage health index of at least one cell.

[0021] In one embodiment, an initial voltage health index for at least one cell is determined based on at least one maximum voltage deviation and a preset voltage change corresponding to a target state of charge range, including:

[0022] Determine a first ratio between at least one maximum voltage deviation and a preset voltage change;

[0023] Determine the product of the preset influence coefficient and the first ratio, and determine the difference between the preset value and the product;

[0024] At least one difference is subjected to amplitude limiting to obtain the initial voltage health index of at least one cell.

[0025] In one embodiment, the maximum voltage deviation of at least one cell before the start of the first sampling time in a plurality of sampling times is 0.

[0026] In one embodiment, determining the relative health coefficient of at least one cell based on the target voltage health index of multiple cells includes:

[0027] Based on the target voltage health index of multiple cells, determine the average voltage health index corresponding to the target state of charge range;

[0028] For at least one battery cell, the relative health coefficient of at least one battery cell is determined based on the target voltage health index and the average voltage health index of at least one battery cell.

[0029] In one embodiment, determining the relative health coefficient of at least one battery cell based on the target voltage health index and the average voltage health index of at least one battery cell includes:

[0030] Determine a second ratio of at least one target voltage health index to the average voltage health index;

[0031] At least one second ratio is subjected to amplitude limiting to obtain the relative health coefficient of at least one cell.

[0032] In one embodiment, determining the health status of at least one battery cell based on a relative health coefficient of at least one battery cell includes:

[0033] The relative health coefficient of at least one cell is smoothed to obtain the target health coefficient of at least one cell.

[0034] The health status of at least one cell is determined based on the battery health status and the target health coefficient of at least one cell.

[0035] In one embodiment, the relative health coefficient includes a first relative health coefficient corresponding to a first state of charge interval and a second relative health coefficient corresponding to a second state of charge interval. Smoothing at least one relative health coefficient yields a target health coefficient for at least one battery cell, including:

[0036] The first relative health coefficient and the corresponding second relative health coefficient are weighted to obtain the intermediate relative health coefficient of at least one cell.

[0037] The target health coefficients of at least one intermediate relative health coefficient and the historical target state of charge interval are weighted to obtain the target health coefficients corresponding to at least one target state of charge interval; the historical target state of charge intervals are the target state of charge intervals in the previous time period corresponding to multiple sampling times.

[0038] In one embodiment, the first state of charge range includes the range where the state of charge is between 0% and 30%.

[0039] In one embodiment, the cell health status assessment method further includes:

[0040] If the battery current is less than the preset current, the fluctuation range of the battery current within the preset sliding window is less than the preset fluctuation range, the target equalization circuit is not activated, and the battery's state of charge is within the target state of charge range, then the sampling time is determined to meet the constant current discharge condition; the target equalization circuit includes the equalization circuit of each cell and the global equalization circuit of the energy storage system to which the battery belongs.

[0041] Secondly, this application also provides an energy storage system, which includes a battery management system, the battery management system comprising:

[0042] The first determining module is used to determine multiple consecutive sampling times that satisfy the constant current discharge condition; the multiple sampling times correspond to multiple states of charge in the target state of charge interval, the target state of charge interval includes a first state of charge interval and a second state of charge interval, the upper limit of the first state of charge interval is less than the lower limit of the second state of charge interval.

[0043] The second determining module is used to determine the relative health coefficient of at least one cell in the battery in the target state of charge range based on the voltage of each cell collected at multiple sampling times.

[0044] The third determining module is used to determine the health status of at least one battery cell based on the relative health coefficient of at least one battery cell.

[0045] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps provided in the first aspect.

[0046] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps provided in the first aspect.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps provided in the first aspect.

[0048] The aforementioned cell health status assessment method and energy storage system determine multiple consecutive sampling times that meet the constant current discharge condition. For at least one cell in the battery, based on the voltage of each cell collected at multiple sampling times, a relative health coefficient corresponding to the at least one cell in the target state of charge (SOC) interval is determined. Based on the relative health coefficient of the at least one cell, the health status of the at least one cell is determined. The multiple sampling times correspond to multiple SOC intervals within the target SOC interval, which includes a first SOC interval and a second SOC interval. The upper limit of the first SOC interval is less than the lower limit of the second SOC interval. In this embodiment, based on the difference in voltage sensitivity of the cell in different SOC intervals, a first SOC interval with higher sensitivity to health differences and a second SOC interval with relatively lower sensitivity to health differences are divided from the SOC interval. The relative health coefficients of the two SOC intervals are determined separately. By fusing the relative health coefficients of the two SOC intervals, the health status of the cell is determined. This approach retains the detection sensitivity of the high-sensitivity interval while utilizing the stability characteristics of the low-sensitivity interval to constrain the health status calculation results, effectively balancing the identification sensitivity and result stability of the cell health status assessment. Therefore, this application can determine the health status of the battery cell without increasing additional hardware costs, which is beneficial for its application in large-scale energy storage systems. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a diagram illustrating the application environment of a cell health status assessment method in one embodiment.

[0051] Figure 2 This is a flowchart illustrating a method for assessing the health status of a battery cell in one embodiment;

[0052] Figure 3 This is a flowchart illustrating a method for determining the relative health coefficient in one embodiment;

[0053] Figure 4 This is a flowchart illustrating a method for determining the target voltage health index in one embodiment;

[0054] Figure 5 This is a flowchart illustrating the method for determining the relative health coefficient in another embodiment;

[0055] Figure 6This is a flowchart illustrating a method for assessing the health status of a battery cell in another embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0058] The cell health status assessment method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown includes a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 1 As shown, the computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data related to determining the health status of the battery cells. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing the health status of battery cells. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0059] Those skilled in the art will understand that Figure 1The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0060] Alternatively, the battery cell can be a lithium battery, such as a lithium manganese iron phosphate cell, a lithium iron phosphate cell, or a lithium manganese phosphate cell.

[0061] Alternatively, the battery cell can be a sodium battery, such as a layered oxide sodium-ion battery cell, a Prussian blue sodium-ion battery cell, or a polyanionic sodium-ion battery cell.

[0062] Alternatively, the battery cell can be an aluminum battery, such as an aluminum-ion battery cell or an aluminum-air battery cell.

[0063] It is understood that the battery cell can also be a secondary battery whose health status can be characterized by parameters such as voltage, current, and temperature, such as solid-state batteries, lithium-sulfur batteries, potassium-ion batteries, and magnesium-ion batteries. The embodiments of this application do not limit the chemical system and material system of the battery cell.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a method for assessing the health status of a battery cell is provided, which can be applied to... Figure 1 The following explanation uses computer equipment as an example, including the following steps S201 to S203. Wherein:

[0065] S201, determine multiple consecutive sampling times that satisfy the constant current discharge condition; the multiple sampling times correspond to multiple states of charge in the target state of charge interval, the target state of charge interval includes a first state of charge interval and a second state of charge interval, the upper limit of the first state of charge interval is less than the lower limit of the second state of charge interval.

[0066] The first state-of-charge (SOC) range can be either 0%-30% or 0%-40%, meaning it's a low SOC range. This means that within the low SOC range, the voltage difference between cells in different health states is greater than in other SOC ranges; in other words, the first SOC range is highly sensitive to differences in health status. Further, the first SOC range can be a range where the SOC is between 10% and 30%.

[0067] The upper limit of the first state of charge (SOC) range is lower than the lower limit of the second SOC range, meaning the first SOC range is lower than the second SOC range. The second SOC range has a relatively lower voltage sensitivity to health differences. The second SOC range can be a medium SOC range or a medium-high SOC range. For example, the second SOC range can be a range where the SOC is between 30% and 80%, or between 30% and 90%, or between 40% and 70%.

[0068] It should be noted that the first and second state of charge ranges mentioned above can be adjusted according to the cell's chemical system, temperature conditions, calibration results, and system application scenarios.

[0069] The constant current discharge condition refers to the following conditions: the battery current is less than the preset current, the fluctuation range of the battery current within the preset sliding window is less than the preset fluctuation range, the target balancing circuit is not activated, and the battery's state of charge is within the target state of charge range. The target balancing circuit includes the balancing circuits for each cell and the global balancing circuit for the energy storage system to which the battery belongs.

[0070] The preset current is the calibrated minimum discharge current. For example, the preset current can be 0.2C, 0.25C, 0.3C, etc.

[0071] Get the maximum current of the battery within the preset sliding window. Minimum current Determine the difference between the maximum and minimum currents, and the absolute value of this difference relative to the average current. The ratio is the fluctuation range of the battery current within the preset sliding window. That is, the fluctuation range of the battery current within the preset sliding window is less than the preset fluctuation range. It can be represented as Optionally, the preset fluctuation range can be 3%, 5%, 6%, etc.

[0072] In this embodiment, for the current sampling time, if the battery current at the current sampling time is less than a preset current, then the current sampling time is determined to be an effective discharge; if the fluctuation amplitude of the battery current within the preset sliding window up to the current sampling time is less than the preset fluctuation amplitude, then the battery discharge at the current sampling time is approximately constant current discharge; if the target equalization circuit is not activated at the current sampling time, it proves that the equalization circuit does not disturb the voltage; if the battery's state of charge is within the target state of charge range at the current sampling time, for example, if the state of charge is within the first state of charge range, it indicates that the battery is currently in a low state of charge range. If all of the above conditions are met, then the current sampling time is determined to meet the constant current discharge condition.

[0073] Similarly, the above method is used to determine whether the next sampling time of the current sampling time meets the constant current discharge condition, until multiple consecutive sampling times that meet the constant current discharge condition are obtained.

[0074] Since the target state of charge interval includes the first state of charge interval and the second state of charge interval, multiple sampling times corresponding to the first state of charge interval and multiple sampling times corresponding to the second state of charge interval will be obtained. There is a correspondence between the sampling times and the state of charge in the target state of charge interval.

[0075] S202, for at least one cell in the battery, based on the voltage of each cell collected at multiple sampling times, determine the relative health coefficient of at least one cell in the target state of charge range.

[0076] Since the relative health coefficient is determined in the same way for the first state of charge interval and the second state of charge interval, the following embodiments in this application will all be described using the first state of charge interval as an example.

[0077] Taking one of the battery cells as an example, the maximum voltage deviation of the battery cell in the target state of charge range can be determined based on the voltage of each battery cell collected at multiple sampling times. Based on the maximum voltage deviation and the preset voltage change corresponding to the first state of charge range, the target voltage health index of the battery cell can be determined. Based on the target voltage health indices of multiple battery cells, the relative health coefficient of the battery cell can be determined.

[0078] In some possible implementations, due to the greater internal resistance of degraded cells under the same constant current discharge conditions, the slope of the change in state of charge is significantly larger. Therefore, it is also possible to fit the slope of the voltage change of individual cells with the change in state of charge within the target state of charge range; using the slope of all cells corresponding to the battery as a benchmark, the slope deviation of individual cells is calculated, and the slope deviation is used as the relative health coefficient of the cell.

[0079] S203, determine the health status of at least one cell based on the relative health coefficient of at least one cell.

[0080] In this embodiment of the application, the relative health coefficient of at least one cell can be smoothed to obtain the target health coefficient of at least one cell, thereby determining the health status of at least one cell based on the battery health status of the battery and the target health coefficient of at least one cell.

[0081] In some possible implementations, since each cell corresponds to a relative health coefficient (i.e., the first relative health coefficient) for the first state of charge interval and a relative health coefficient (i.e., the second relative health coefficient) for the second state of charge interval, the average of the first relative health coefficient and the second relative health coefficient is obtained, and this average is used as the target health coefficient of the cell.

[0082] In the aforementioned cell health status assessment method, multiple consecutive sampling times that meet the constant current discharge condition are determined. For at least one cell in the battery, based on the voltage of each cell collected at multiple sampling times, the relative health coefficient of at least one cell in the target state of charge interval is determined. Based on the relative health coefficient of at least one cell, the health status of at least one cell is determined. The multiple sampling times correspond to multiple states of charge within the target state of charge interval, which includes a first state of charge interval and a second state of charge interval. The upper limit of the first state of charge interval is less than the lower limit of the second state of charge interval. In this embodiment, based on the difference in voltage sensitivity of the cell in different state of charge intervals, a first state of charge interval with higher sensitivity to health differences and a second state of charge interval with relatively lower sensitivity are divided from the state of charge interval. The relative health coefficients of the two state of charge intervals are determined separately. By fusing the relative health coefficients of the two state of charge intervals, the health status of the cell is determined. This method retains the detection sensitivity of the high-sensitivity interval while utilizing the stability characteristics of the low-sensitivity interval to constrain the health status calculation results, effectively balancing the identification sensitivity and result stability of the cell health status assessment. Therefore, this application can determine the health status of the battery cell without increasing additional hardware costs, which is beneficial for its application in large-scale energy storage systems.

[0083] Figure 3 This is a flowchart illustrating a method for determining the relative health coefficient in one embodiment, as shown below. Figure 3 As shown, the embodiments of this application relate to some possible implementations of how to determine the relative health coefficient of at least one battery cell in a target state of charge range based on the voltage of each battery cell collected at multiple sampling times, including the following steps:

[0084] S301, based on the voltage of each cell collected at multiple sampling times, determine the maximum voltage deviation of at least one cell in the target state of charge range.

[0085] In this embodiment, for each sampling time, the average voltage corresponding to the sampling time can be determined based on the voltage of each cell collected at that sampling time. For any given cell, the voltage deviation between the cell's voltage and the average voltage is determined, and the maximum voltage deviation of the cell in the target state of charge range is determined from the voltage deviations of the cell at each sampling time.

[0086] In some possible implementations, for each sampling time, the voltages of all cells are sorted, the median of the voltages at the sampling time is calculated, and the voltage deviation of a single cell relative to the median is calculated based on the median. Among all sampling times corresponding to the target state of charge interval, the maximum voltage deviation of the cell in the target state of charge interval is determined from the voltage deviation of the cell.

[0087] S302, determine the target voltage health index of at least one cell based on at least one maximum voltage deviation and the preset voltage change corresponding to the target state of charge range.

[0088] In this embodiment of the application, for any given cell, the initial voltage health index of the cell can be determined based on the maximum voltage deviation corresponding to the cell and the preset voltage change corresponding to the target state of charge range. The initial voltage health index is then subjected to a limiting process to obtain the target voltage health index of the cell.

[0089] In some possible implementations, the initial voltage health index can also be used as the target voltage health index.

[0090] In some possible implementations, the initial voltage health index can be normalized, and the normalized initial voltage health index can be used as the target voltage health index.

[0091] S303, based on the target voltage health index of multiple cells, determines the relative health coefficient of at least one cell.

[0092] In this embodiment of the application, for one of the battery cells, the average voltage health index corresponding to the target state of charge range is determined based on the target voltage health index of multiple battery cells, and the relative health coefficient of the battery cell is determined based on the target voltage health index and the average voltage health index of the battery cell.

[0093] In some possible implementations, for one of the battery cells, the cell can be normalized according to the target voltage health index of multiple battery cells, and the normalization result can be directly used as the relative health coefficient of the cell.

[0094] In this embodiment, based on the voltages of each cell collected at multiple sampling times, the maximum voltage deviation of at least one cell within the target state of charge range is determined. Based on the at least one maximum voltage deviation and a preset voltage change corresponding to the target state of charge range, a target voltage health index for at least one cell is determined. Based on the target voltage health indices of multiple cells, a relative health coefficient for at least one cell is determined. In this embodiment, the maximum voltage deviation is used as a quantification index to calculate the target voltage health index of the cell. Then, the relative health coefficient of the cell is obtained by combining the target voltage health indices of multiple cells. Through a two-layer calculation logic of single-cell quantification and multi-cell fusion, differentiated assessment of the cell's health status is achieved, improving the accuracy of determining the relative health coefficient of the cell.

[0095] In an exemplary embodiment, determining the maximum voltage deviation of at least one cell within a target state of charge range based on the voltages of each cell collected at multiple sampling times includes:

[0096] For each sampling time, the average voltage corresponding to the sampling time is determined based on the voltage of each cell collected at the sampling time; the voltage deviation between the voltage of at least one cell and the average voltage is determined; for at least one cell, the maximum voltage deviation of at least one cell in the target state of charge range is determined from the voltage deviations of at least one cell at each sampling time.

[0097] In the embodiments of this application, according to Determine the average voltage at the sampling time. Where N is the number of battery cells. Let be the voltage of the j-th cell at the sampling time.

[0098] After obtaining the average voltage, for the i-th cell, the following is adopted: Determine the voltage deviation between the voltage of the i-th cell and the average voltage. Since the first state-of-charge interval includes multiple consecutive sampling times that satisfy the constant current condition, there are multiple voltage deviations for the i-th cell. The maximum voltage deviation is determined from these multiple deviations, which can be specifically expressed as: .

[0099] Among them, the maximum voltage deviation of at least one cell before the start of the first sampling time in multiple sampling times is 0, so that the maximum voltage deviation of the cell is updated only within the first state of charge interval / second state of charge interval.

[0100] In this embodiment, the voltage deviation between the voltage of at least one cell and the average voltage is determined. For at least one cell, the maximum voltage deviation of at least one cell within the target state of charge range is determined from the voltage deviations of at least one cell at each sampling time. This embodiment calculates the average voltage at each sampling time, calculates the voltage deviation of the cell based on the average voltage, and then filters the maximum voltage deviation within the target state of charge range, which can effectively capture the degradation characteristics of the cell during the discharge process. Furthermore, in this embodiment, the computational complexity of the maximum voltage deviation is low, making it suitable for real-time execution by the battery management system.

[0101] Figure 4 This is a flowchart illustrating a method for determining the target voltage health index in one embodiment, as shown below. Figure 4 As shown, the embodiments of this application relate to some possible implementations of how to determine the target voltage health index of at least one battery cell based on at least one maximum voltage deviation and a preset voltage change corresponding to the target state of charge range, including the following steps:

[0102] S401, determine the initial voltage health index of at least one cell based on at least one maximum voltage deviation and the preset voltage change corresponding to the target state of charge range.

[0103] In this embodiment of the application, based on the target state of charge interval and the pre-established mapping relationship between the state of charge interval and the voltage change, a pre-calibrated preset voltage change can be obtained, the first ratio of the maximum voltage deviation of the cell to the preset voltage change can be determined, the product of the preset influence coefficient and the first ratio can be determined, and the difference between the preset value and the product can be determined as the initial voltage health index of at least one cell.

[0104] In some possible implementations, a mapping relationship between the maximum voltage deviation and the voltage health index can be established in advance, such as a mapping table. Based on the mapping relationship, the base voltage index is found by using the maximum voltage deviation of the cell as an index. The base voltage index is then fine-tuned by combining the preset voltage change amount of the target state of charge range to obtain the initial voltage health index.

[0105] Optionally, the mapping relationship between the state of charge (SOC) range and the voltage change can be determined based on the voltage change pre-calibrated within the selected SOC range according to the open-circuit voltage-SOC curve of each cell. For example, for cells of the same type, based on the open-circuit voltage-SOC curve corresponding to each cell, the voltage change corresponding to the 0%-30% SOC range is determined, and the average, median, or mode of multiple voltage changes is calculated as the preset voltage change corresponding to the 0%-30% SOC range. Similarly, preset voltage changes corresponding to the 0%-40% SOC range, the 10%-30% SOC range, and the 40%-90% SOC range can also be determined.

[0106] S402, limit the voltage health index of at least one initial voltage to obtain the target voltage health index of at least one cell.

[0107] In the embodiments of this application, the following are adopted: Initial voltage health index After limiting the voltage, the target voltage health index of the battery cell is obtained. , The preset lower limit coefficient can be optionally set as follows: It can be 0.1, 0.2, 0.3, etc.

[0108] In this embodiment, an initial voltage health index for at least one battery cell is determined based on at least one maximum voltage deviation and a preset voltage change corresponding to the target state of charge interval. The initial voltage health index is then subjected to a limiting process to obtain the target voltage health index for the at least one battery cell. This embodiment calculates the initial voltage health index using the maximum voltage deviation and a preset voltage change, and the limiting process constrains the initial voltage health index, reducing sampling noise interference and thus improving the algorithm's robustness and result stability.

[0109] In an exemplary embodiment, determining the initial voltage health index of at least one battery cell based on at least one maximum voltage deviation and a preset voltage change corresponding to a target state of charge range includes: determining a first ratio of at least one maximum voltage deviation to a preset voltage change; determining the product of a preset influence coefficient and the first ratio; and determining the difference between the preset value and the product as the initial voltage health index of at least one battery cell.

[0110] In the embodiments of this application, the following are adopted: Determine the initial voltage health index ,in, For the preset voltage change, This is the preset influence coefficient, a dimensionless coefficient obtained from experimental calibration, used to control the degree of influence of voltage deviation on the initial voltage health index. 1 is the preset value.

[0111] In this embodiment, a first ratio of at least one maximum voltage deviation to a preset voltage change is determined; the product of a preset influence coefficient and the first ratio is determined, and the difference between the preset value and the product is determined; the initial voltage health index of at least one cell is determined for at least one difference. This embodiment determines the initial voltage health index through linear calculation, with a simple formula that is easy to run in real-time in resource-constrained battery management systems / dedicated integrated circuits, thus improving the efficiency of determining the initial voltage health index.

[0112] Figure 5 This is a flowchart illustrating the method for determining the relative health coefficient in another embodiment, as shown below. Figure 5 As shown, embodiments of this application relate to some possible implementations of how to determine the relative health coefficient of at least one battery cell based on the target voltage health index of multiple battery cells, including the following steps:

[0113] S501 determines the average voltage health index corresponding to the target state of charge range based on the target voltage health index of multiple cells.

[0114] In this embodiment of the application, the first state of charge interval is continued as an example, according to Determine the average voltage health index corresponding to the first state of charge interval. .in, Let be the target voltage health index of the j-th cell.

[0115] S502, for at least one cell, determine the relative health coefficient of at least one cell based on the target voltage health index and the average voltage health index of at least one cell.

[0116] In the embodiments of this application, a second ratio of at least one target voltage health index to an average voltage health index is determined, and the at least one second ratio is subjected to amplitude limiting processing to obtain the relative health coefficient of at least one cell.

[0117] In some possible implementations, after determining a second ratio of at least one target voltage health index to an average voltage health index, at least one second ratio can be used as the relative health coefficient of at least one cell.

[0118] In this embodiment, an average voltage health index corresponding to a target state of charge range is determined based on the target voltage health index of multiple battery cells. For at least one battery cell, a relative health coefficient is determined based on the target voltage health index and the average voltage health index of the at least one battery cell. This embodiment determines the average voltage health index based on the target voltage health index of multiple battery cells, thereby using the average voltage health index to achieve a relative assessment of a single battery cell, assess the health status of individual battery cells, and identify faulty battery cells with abnormal health conditions.

[0119] Related technologies typically evaluate the battery pack as a whole, which cannot distinguish the differences in the health status of individual cells within the pack. The embodiments of this application achieve cell-level health status evaluation by independently analyzing the maximum voltage deviation of each cell. This enables the identification of individual abnormal cells within the battery pack, refining the evaluation granularity from the battery pack level to the cell level.

[0120] The cell health status assessment provided in this application does not limit the number of cells to be assessed. In practical applications, the health status of a single cell can be assessed and monitored; several cells (i.e., a subset of cells) that may have abnormalities can be selected from the battery pack for health status assessment; or all cells in the battery pack can be assessed for health status.

[0121] In an exemplary embodiment, determining the relative health coefficient of at least one battery cell based on a target voltage health index and an average voltage health index includes: determining a second ratio of at least one target voltage health index to an average voltage health index; and applying a limiting process to the at least one second ratio to obtain the relative health coefficient of at least one battery cell.

[0122] In this embodiment of the application, taking the first state of charge interval as an example, according to Determine the target voltage health index of the i-th cell. With average voltage health index The second ratio, using For the second ratio After applying amplitude limiting, the relative health coefficient of the i-th cell is obtained. , which is the first relative health coefficient of the i-th cell.

[0123] in, These are the lower and upper limits of the amplitude limiting (e.g., 0.7 and 1.3), used to suppress the influence of abnormal segments on the results. For example, the lower limit of amplitude limiting is 0.7 and the upper limit of amplitude limiting is 1.3. The lower limit of amplitude limiting can also be 0.5 and the upper limit of amplitude limiting is 1.3, or the lower limit of amplitude limiting can also be 0.4 and the upper limit of amplitude limiting is 1. That is, the lower and upper limits of amplitude limiting can be determined according to the actual situation. The embodiments of this application do not limit the lower and upper limits of amplitude limiting.

[0124] Similarly, for the second state-of-charge interval, the relative health coefficient of the i-th cell can also be obtained using the same method. The second relative health coefficient of the i-th cell.

[0125] In this embodiment, a second ratio of at least one target voltage health index to an average voltage health index is determined; the at least one second ratio is then limited to obtain the relative health coefficient of at least one cell. This embodiment quantifies the degree of health deviation of the cell relative to the battery by using the second ratio of the target voltage health index to the average voltage health index, and limits the output range of the relative health coefficient by limiting the second ratio. Thus, by combining the aforementioned mechanisms of current stability determination and equilibrium state elimination, this application exhibits good robustness against noise, occasional abnormal operating conditions, and abnormal disturbances during a single discharge process, making it suitable for long-term online operation.

[0126] Figure 6 This is a flowchart illustrating a cell health status assessment method in another embodiment, as shown below. Figure 6 As shown, embodiments of this application relate to some possible implementations of how to determine the health status of at least one battery cell based on its relative health coefficient, including the following steps:

[0127] S601, smooth the relative health coefficient of at least one cell to obtain the target health coefficient of at least one cell.

[0128] In this embodiment of the application, for one of the battery cells, the first relative health coefficient and the corresponding second relative health coefficient of the battery cell can be weighted to obtain the intermediate relative health coefficient of the battery cell. The intermediate relative health coefficient of the battery cell and the target health coefficient of the historical target state of charge interval are weighted to obtain the target health coefficient of the battery cell in the target state of charge interval. The historical target state of charge interval is the target state of charge interval of the previous time period corresponding to multiple sampling times.

[0129] In some possible implementations, the intermediate relative health coefficient of the cell can also be used as the target health coefficient of the cell.

[0130] S602, determine the health status of at least one cell based on the battery health status of the battery and the target health coefficient of at least one cell.

[0131] In this embodiment of the application, the battery health status is... and the target health coefficient of the i-th cell Multiplying them together gives the health status of the battery cell. Specifically, it can be expressed as .

[0132] Furthermore, the battery management system can provide early warnings, dynamically adjust charge and discharge limits, optimize balancing strategies, and perform targeted maintenance or replacement during the operation and maintenance phase for cells with poor health status (weak cells) based on the target health coefficient or health status of each cell, thereby improving the safety and whole life cycle utilization of the energy storage system.

[0133] In this embodiment, the relative health coefficient of at least one cell is smoothed to obtain the target health coefficient of at least one cell. Based on the battery health status of at least one battery and the target health coefficient of the cell, the health status of at least one cell is determined. This embodiment not only evaluates the overall health status of the battery pack but also quantifies the health differences between individual cells. This allows the battery management system to identify abnormal cells across the entire battery pack and optimize the balancing strategy.

[0134] In an exemplary embodiment, smoothing at least one relative health coefficient to obtain a target health coefficient for at least one battery cell includes: weighting at least one first relative health coefficient and a corresponding second relative health coefficient to obtain an intermediate relative health coefficient for at least one battery cell; weighting at least one intermediate relative health coefficient and a target health coefficient for a historical target state of charge interval to obtain a target health coefficient corresponding to at least one target state of charge interval; the historical target state of charge interval is the target state of charge interval in the previous time period corresponding to multiple sampling times.

[0135] In this embodiment of the application, the target health coefficient of the i-th cell The implementation can be represented as follows: ,in, The first relative health coefficient, The second relative health coefficient is β, which is a smoothing coefficient between 0 and 1. The target health coefficient for the historical target state of charge interval of the i-th cell can be initially set to 1.

[0136] Among them, the weighting coefficient corresponding to the first relative health coefficient is greater than the weighting coefficient corresponding to the second relative health coefficient, that is... ,in, This approach ensures that the high-sensitivity results in the low-charge state range are the primary data driving the estimation results, while supplementary data in the medium- and high-charge state ranges are used to improve continuity and stability.

[0137] In this embodiment, at least one first relative health coefficient and its corresponding second relative health coefficient are weighted to obtain an intermediate relative health coefficient for at least one cell; the at least one intermediate relative health coefficient and the target health coefficient for a historical target state of charge interval are then weighted to obtain a target health coefficient corresponding to at least one target state of charge interval. This embodiment employs an exponential moving average method to smooth the relative health coefficients of each cell over time, improving the accuracy of cell health status determination.

[0138] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0139] Based on the same inventive concept, this application also provides an energy storage system for implementing the aforementioned cell health status assessment method. The solution provided by this energy storage system is similar to the implementation described in the above method; therefore, the specific limitations in the energy storage system embodiments provided below can be found in the limitations of the cell health status assessment method described above, and will not be repeated here.

[0140] In one exemplary embodiment, an energy storage system is provided, the energy storage system including a battery management system, the battery management system including:

[0141] The first determining module is used to determine multiple consecutive sampling times that satisfy the constant current discharge condition; the multiple sampling times correspond to multiple states of charge in the target state of charge interval, the target state of charge interval includes a first state of charge interval and a second state of charge interval, the upper limit of the first state of charge interval is less than the lower limit of the second state of charge interval.

[0142] The second determining module is used to determine the relative health coefficient of at least one cell in the battery within the target state of charge range based on the voltage of each cell collected at multiple sampling times.

[0143] The third determining module is used to determine the health status of at least one battery cell based on the relative health coefficient of at least one battery cell.

[0144] In an exemplary embodiment, the second determining module is specifically used to determine the maximum voltage deviation of at least one cell in the target state of charge interval based on the voltage of each cell collected at multiple sampling times; determine the target voltage health index of at least one cell based on the at least one maximum voltage deviation and the preset voltage change amount corresponding to the target state of charge interval; and determine the relative health coefficient of at least one cell based on the target voltage health indices of multiple cells.

[0145] In an exemplary embodiment, the second determining module is specifically configured to, for each sampling time, determine the average voltage corresponding to the sampling time based on the voltage of each cell collected at the sampling time; determine the voltage deviation between the voltage of at least one cell and the average voltage; and for at least one cell, determine the maximum voltage deviation of at least one cell in the target state of charge range from the voltage deviations of at least one cell at each sampling time.

[0146] In an exemplary embodiment, the second determining module is specifically used to determine the initial voltage health index of at least one cell based on at least one maximum voltage deviation and a preset voltage change corresponding to the target state of charge interval; and to perform a limiting process on the at least one initial voltage health index to obtain the target voltage health index of at least one cell.

[0147] In an exemplary embodiment, the second determining module is specifically used to determine a first ratio of at least one maximum voltage deviation to a preset voltage change; determine the product of a preset influence coefficient and the first ratio, and determine the difference between the preset value and the product; and perform amplitude limiting processing on at least one difference to obtain the initial voltage health index of at least one cell.

[0148] In one exemplary embodiment, the maximum voltage deviation of at least one cell before the start of the first sampling time in a plurality of sampling times is 0.

[0149] In an exemplary embodiment, the second determining module is specifically used to determine the average voltage health index corresponding to the target state of charge range based on the target voltage health index of multiple cells; and for at least one cell, to determine the relative health coefficient of at least one cell based on the target voltage health index and the average voltage health index of at least one cell.

[0150] In an exemplary embodiment, the second determining module is specifically used to determine a second ratio of at least one target voltage health index to an average voltage health index; and to perform a limiting process on the at least one second ratio to obtain a relative health coefficient of at least one cell.

[0151] In an exemplary embodiment, the third determining module is specifically used to smooth the relative health coefficient of at least one cell to obtain the target health coefficient of at least one cell; and to determine the health status of at least one cell based on the battery health status of the battery and the target health coefficient of at least one cell.

[0152] In an exemplary embodiment, the third determining module is specifically used to perform weighted processing on at least one first relative health coefficient and the corresponding second relative health coefficient to obtain at least one intermediate relative health coefficient of the battery cell; and to perform weighted processing on at least one intermediate relative health coefficient and the target health coefficient of the historical target state of charge interval to obtain the target health coefficient corresponding to at least one target state of charge interval; the historical target state of charge interval is the target state of charge interval under the previous time period corresponding to multiple sampling times.

[0153] In one exemplary embodiment, the first state of charge range includes the range where the state of charge is between 0% and 30%.

[0154] In one exemplary embodiment, the battery management system further includes:

[0155] The fourth determination module is used to determine if the sampling time meets the constant current discharge condition if the battery current is less than the preset current, the fluctuation range of the battery current within the preset sliding window is less than the preset fluctuation range, the target equalization circuit is not activated, and the battery's state of charge is within the target state of charge range; the target equalization circuit includes the equalization circuit of each cell and the global equalization circuit of the energy storage system to which the battery belongs.

[0156] The modules in the aforementioned battery management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0157] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above method embodiments.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0159] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above method embodiments.

[0160] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0162] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of state of health estimation of a battery cell, characterized in that, The method for assessing the health status of the battery cell includes: A series of consecutive sampling times that satisfy the constant current discharge condition are determined; the series of sampling times correspond to a series of states of charge in a target state of charge interval, the target state of charge interval including a first state of charge interval and a second state of charge interval, the upper limit of the first state of charge interval being less than the lower limit of the second state of charge interval; For at least one cell in a battery, based on the voltage of each cell collected at multiple sampling times, the maximum voltage deviation of at least one cell corresponding to the target state of charge interval is determined. A first ratio of the at least one maximum voltage deviation to the preset voltage change corresponding to the target state of charge interval is determined. A preset influence coefficient is determined as the product of the first ratio, and the difference between the preset value and the product is determined as the initial voltage health index of at least one cell. The initial voltage health index of at least one cell is limited to obtain the target voltage health index of at least one cell. Based on the target voltage health indices of multiple cells, the average voltage health index corresponding to the target state of charge interval is determined. For at least one cell, based on the second ratio of the target voltage health index of at least one cell to the average voltage health index, the relative health coefficient of at least one cell corresponding to the target state of charge interval is determined. Wherein, at least one cell includes multiple cells, the maximum voltage deviation is the maximum value among the voltage deviations between the voltage of at least one cell and the average voltage of each cell at each sampling time, the preset influence coefficient is a coefficient that controls the degree of influence of the maximum voltage deviation on the initial voltage health index, and the preset value is 1. The health status of at least one of the battery cells is determined based on the relative health coefficient of at least one of the battery cells.

2. The method of state of health estimation of the battery cell according to claim 1, characterized by, At least one of the battery cells has a maximum voltage deviation of 0 before the start of the first sampling time in the plurality of sampling times.

3. The method of state of health estimation of the battery cell according to claim 1, characterized by, Determining the relative health coefficient of at least one of the battery cells based on a second ratio of the target voltage health index of at least one of the battery cells to the average voltage health index includes: A limiting process is applied to at least one of the second ratios to obtain the relative health coefficient of at least one of the battery cells.

4. The method of state of health estimation of a battery cell according to claim 1, characterized by, Determining the health status of at least one of the battery cells based on a relative health coefficient of at least one of the battery cells includes: The relative health coefficient of at least one of the battery cells is smoothed to obtain the target health coefficient of at least one of the battery cells; The health status of at least one of the battery cells is determined based on the product of the battery health status of the battery and the target health coefficient of at least one of the battery cells.

5. The method of state of health estimation of the battery cell according to claim 4, wherein The relative health coefficient includes a first relative health coefficient corresponding to the first state of charge interval and a second relative health coefficient corresponding to the second state of charge interval. The step of smoothing at least one of the relative health coefficients to obtain a target health coefficient for at least one of the battery cells includes: A weighted summation is performed on at least one first relative health coefficient and the corresponding second relative health coefficient to obtain at least one intermediate relative health coefficient of the battery cell; A weighted summation is performed on at least one of the intermediate relative health coefficients and the target health coefficients of the historical target state of charge intervals to obtain the target health coefficients corresponding to at least one of the target state of charge intervals; the historical target state of charge intervals are the target state of charge intervals under the previous time period corresponding to the time periods of the multiple sampling times.

6. The method of state of health estimation of a battery cell according to claim 1, wherein The first state of charge range includes the range where the state of charge is between 0% and 30%.

7. The method of state of health estimation of a battery cell according to claim 1, wherein The method for assessing the health status of the battery cell also includes: If the current of the battery is less than the preset current, the fluctuation range of the battery current within the preset sliding window is less than the preset fluctuation range, the target equalization circuit is not activated, and the state of charge of the battery is within the target state of charge range, then it is determined that the sampling time meets the constant current discharge condition; the target equalization circuit includes the equalization circuit of each cell and the global equalization circuit of the energy storage system to which the battery belongs.

8. An energy storage system characterized by, The energy storage system includes a battery management system, which includes: The first determining module is used to determine a plurality of consecutive sampling times that satisfy the constant current discharge condition; the plurality of sampling times correspond to a plurality of charge states in a target charge state interval, the target charge state interval including a first charge state interval and a second charge state interval, wherein the upper limit of the first charge state interval is less than the lower limit of the second charge state interval. The second determining module is used to, for at least one cell in the battery, determine the maximum voltage deviation of at least one cell corresponding to the target state of charge interval based on the voltage of each cell collected at multiple sampling times, determine a first ratio of the at least one maximum voltage deviation to the preset voltage change corresponding to the target state of charge interval, determine the product of a preset influence coefficient and the first ratio, and determine the difference between the preset value and the product as the initial voltage health index of at least one cell, perform amplitude limiting processing on at least one initial voltage health index to obtain the target voltage health index of at least one cell, and determine the target voltage health index of multiple cells based on the target voltage health index of the cells. The method involves determining the average voltage health index corresponding to the target state of charge interval; for at least one cell, determining the relative health coefficient corresponding to the target state of charge interval based on a second ratio of the target voltage health index of the at least one cell to the average voltage health index; wherein, at least one cell includes multiple cells, the maximum voltage deviation is the maximum value among the voltage deviations between the voltage of at least one cell and the average voltage of each cell at each sampling time, and the preset influence coefficient is a coefficient that controls the degree of influence of the maximum voltage deviation on the initial voltage health index, and the preset value is 1; The third determining module is used to determine the health status of at least one of the battery cells based on the relative health coefficient of at least one of the battery cells.

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