Estimation method and device of battery cell capacity, server and storage medium

Abstract

This application relates to the field of battery management system technology, and particularly to a method, apparatus, server, and storage medium for estimating cell capacity. The method includes: obtaining the actual internal resistance of the cell under arbitrary charging conditions; querying a pre-established internal resistance database using the arbitrary charging conditions as an index, and outputting a reference internal resistance of the cell under the arbitrary charging conditions; and calculating the actual capacity of the cell based on the actual internal resistance and the reference internal resistance under the arbitrary charging conditions. This solves the problems in existing technologies where cell capacity acquisition methods are affected by the battery system and user charging conditions, making it impossible to accurately calculate the cell capacity in a battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and specifically to a method, apparatus, server, and storage medium for estimating battery cell capacity. Background Technology

[0002] Obtaining the capacity of the cells within a battery pack is crucial for accurately estimating the true SOC (State of Charge) of each cell and assessing the aging and degradation of each cell. However, because each cell in a battery pack cannot simultaneously reach full charge or full discharge during actual charging and discharging processes, obtaining the cell capacity becomes extremely difficult.

[0003] The methods used in related technologies to obtain cell capacity employ the mapping relationship ΔV~ΔQ between voltage and capacity during constant current charging (discharging) to calculate the cell capacity. This method is only applicable to constant current charging scenarios and is only effective for battery systems such as NMC (ternary lithium-ion batteries). In practical applications, users often use fast charging, where the charging current varies with the state of charge (SOC), making the above method unsuitable for accurately calculating cell capacity. Furthermore, for lithium iron phosphate (LiFePO4, LFP) systems, the above method cannot be used to calculate cell capacity in either constant current or stepped charging modes. Summary of the Invention

[0004] One objective of this invention is to provide a method for estimating battery cell capacity, thereby addressing the problem that existing methods for obtaining battery cell capacity are affected by the battery system and user charging conditions, making it impossible to accurately calculate the battery cell capacity in a battery pack. A second objective is to provide another method for estimating battery cell capacity. A third objective is to provide a device for estimating battery cell capacity. A fourth objective is to provide another device for estimating battery cell capacity. A fifth objective is to provide a server. A sixth objective is to provide a computer-readable storage medium.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for estimating the capacity of a battery cell, applied to a server, wherein the method includes the following steps: obtaining the actual internal resistance of the battery cell under arbitrary charging conditions; querying a pre-established internal resistance database using the arbitrary charging conditions as an index, and outputting the reference internal resistance of the battery cell under the arbitrary charging conditions; and calculating the actual capacity of the battery cell based on the actual internal resistance and the reference internal resistance of the battery cell under the arbitrary charging conditions.

[0007] Based on the above technical means, the embodiments of this application can calculate the actual capacity of any cell by comparing the actual internal resistance of the cell with the reference internal resistance. Since a pre-established internal resistance database is used, the reference internal resistance of the cell under any charging condition can be obtained, avoiding the influence of the user's charging conditions and battery system on the calculation results. It is effectively applicable to a variety of battery systems and realizes accurate calculation of the cell capacity.

[0008] Furthermore, the actual capacity of the battery cell is calculated based on the actual internal resistance and reference internal resistance of the battery cell under any charging condition, including: calculating the root mean square error between the actual internal resistance of the battery cell and the reference internal resistance under any charging condition; determining the objective function based on the root mean square error; and finding the optimal solution of the objective function using the least squares method to obtain the actual capacity of the arbitrary battery cell.

[0009] Based on the above technical means, the embodiments of this application can calculate the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell, determine the objective function using the root mean square error, and achieve accurate calculation of the actual capacity of the battery cell by optimization using the least squares method.

[0010] Furthermore, the calculation of the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under any charging condition includes: establishing an expression for the relationship between the internal resistance, SOC, and capacity of the battery cell; and calculating the root mean square error between the actual internal resistance and the reference internal resistance based on the expression.

[0011] Based on the above technical means, the embodiments of this application can establish the relationship between the cell internal resistance, the charging SOC and the cell capacity, thereby calculating the root mean square error between the actual internal resistance and the reference internal resistance of the cell.

[0012] Furthermore, before querying the pre-established internal resistance database, the process includes: constructing a characterization test matrix of temperature and charging current; obtaining charging characterization curves under different charging conditions based on the characterization test matrix; performing linear regression processing on the charging characterization curves to obtain an electromotive force curve; calculating the overpotential of the cell based on the charging characterization curve and the electromotive force curve; calculating the reference internal resistance under different charging conditions based on the overpotential; and establishing the internal resistance database based on the reference internal resistance under different charging conditions.

[0013] Based on the above technical means, the embodiments of this application can obtain charging characterization curves under different charging conditions by constructing a characterization test matrix, calculate the battery electromotive force curve using a linear regression algorithm, calculate the overpotential of the cell based on the charging characterization curve and the electromotive force curve, and finally calculate the reference internal resistance based on the overpotential to establish an internal resistance database so that the reference internal resistance under different charging conditions can be queried in the subsequent internal resistance database.

[0014] Furthermore, the step of performing linear regression processing on the charging characterization curve to obtain the electromotive force curve includes: obtaining the maximum charging capacity corresponding to the charging characterization curve; determining the step size of the capacity interval corresponding to each charging voltage curve based on the maximum charging capacity; dividing the capacity interval into multiple grids based on the step size; calculating the voltage value when the current in each grid is a preset value by interpolation extrapolation; and generating the electromotive force curve after normalizing all voltage values.

[0015] Based on the above technical means, the embodiments of this application can divide the charging voltage curve into capacity intervals according to the step size of the maximum charging capacity, divide the capacity interval into multiple grids by the step size, calculate the voltage value by interpolation extrapolation, and normalize it to obtain the electromotive force curve.

[0016] Furthermore, the step of obtaining the charging characterization curves under different charging conditions based on the characterization test matrix includes: discharging to a preset cutoff voltage under a first charging condition, resting for a first preset time, discharging to a preset cutoff voltage under a second charging condition, recording the charging characterization curve, and resting for a second preset time; discharging to a preset cutoff voltage under the first charging condition, and performing a charging test under the next charging condition, until all charging conditions have been tested, thereby obtaining the charging characterization curves under different charging conditions.

[0017] Based on the above technical means, the embodiments of this application can obtain corresponding charging characterization curves by testing different charging conditions.

[0018] Furthermore, the charging conditions include one or more of temperature, charging rate, current, and SOC.

[0019] A method for estimating the capacity of a battery cell, applied to a server, comprising the following steps: constructing a characterization test matrix of temperature and charging current; obtaining charging characterization curves under different charging conditions based on the characterization test matrix, and performing linear regression processing on the charging characterization curves to obtain an electromotive force curve; calculating the overpotential of the battery cell based on the charging characterization curve and the electromotive force curve, calculating the reference internal resistance under different charging conditions based on the overpotential, and establishing an internal resistance database based on the reference internal resistance under different charging conditions; querying the reference internal resistance of the battery cell under any charging condition using the internal resistance database, and calculating the actual capacity of the battery cell based on the actual internal resistance of the battery cell under any charging condition and the reference internal resistance.

[0020] A battery cell capacity estimation device, applied to a server, includes: an acquisition module for acquiring the actual internal resistance of the battery cell under arbitrary charging conditions; an output module for querying a pre-established internal resistance database using the arbitrary charging conditions as an index, and outputting a reference internal resistance of the battery cell under the arbitrary charging conditions; and a first calculation module for calculating the actual capacity of the battery cell based on the actual internal resistance and the reference internal resistance of the battery cell under the arbitrary charging conditions.

[0021] Furthermore, the first calculation module is further used to: calculate the root mean square error between the actual internal resistance of the cell and the reference internal resistance under any charging condition; determine the objective function based on the root mean square error, and find the optimal solution for the objective function using the least squares method to obtain the actual capacity of the arbitrary cell.

[0022] Furthermore, the first calculation module is further used to: establish an expression relating the cell's internal resistance, state of charge (SOC), and capacity; and calculate the root mean square error between the actual internal resistance and the reference internal resistance based on the expression.

[0023] Furthermore, it also includes: a third calculation module, used to construct a characterization test matrix of temperature and charging current before querying the pre-established internal resistance database; obtain charging characterization curves under different charging conditions according to the characterization test matrix; perform linear regression processing on the charging characterization curves to obtain an electromotive force curve; calculate the overpotential of the cell according to the charging characterization curve and the electromotive force curve; calculate the reference internal resistance under different charging conditions according to the overpotential; and establish the internal resistance database according to the reference internal resistance under different charging conditions.

[0024] Furthermore, the third calculation module is further used to: obtain the maximum charging capacity corresponding to the charging characterization curve; determine the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity; divide the capacity interval into multiple grids according to the step size; calculate the voltage value when the current in each grid is a preset value by interpolation extrapolation; and generate the electromotive force curve after normalizing all voltage values.

[0025] Furthermore, the third calculation module is further used to: discharge to a preset cutoff voltage under the first charging condition, and then let it stand for a first preset time; discharge to a preset cutoff voltage under the second charging condition, and then record the charging characterization curve and let it stand for a second preset time; discharge to a preset cutoff voltage under the first charging condition, and then perform a charging test under the next charging condition, until all charging conditions have been tested, and obtain the charging characterization curves under different charging conditions.

[0026] Furthermore, the charging conditions include one or more of temperature, charging rate, current, and SOC.

[0027] A battery cell capacity estimation device, applied to a server, comprising: a construction module for constructing a characterization test matrix of temperature and charging current; a processing module for obtaining charging characterization curves under different charging conditions based on the characterization test matrix, and performing linear regression processing on the charging characterization curves to obtain an electromotive force curve; an establishment module for calculating the overpotential of the battery cell based on the charging characterization curve and the electromotive force curve, calculating a reference internal resistance under different charging conditions based on the overpotential, and establishing an internal resistance database based on the reference internal resistance under different charging conditions; and a second calculation module for querying the reference internal resistance of the battery cell under any charging condition using the internal resistance database, and calculating the actual capacity of the battery cell based on the actual internal resistance under any charging condition and the reference internal resistance.

[0028] A server includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement a cell capacity estimation method as described in any of the above embodiments.

[0029] A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement a cell capacity estimation method as described in any of the above embodiments.

[0030] The beneficial effects of this invention are:

[0031] (1) The embodiments of this application can calculate the actual capacity of any cell by comparing the actual internal resistance of the cell with the reference internal resistance. Since the internal resistance database is used, the reference internal resistance of the cell under any charging condition can be obtained, avoiding the influence of the user's charging conditions and battery system on the calculation results. It is effectively applicable to a variety of battery systems and realizes accurate calculation of the cell capacity.

[0032] (2) The embodiments of this application can calculate the root mean square error between the actual internal resistance and the reference internal resistance of the cell, determine the objective function based on the root mean square error, and achieve accurate calculation of the actual capacity of the cell by optimization through the least squares method.

[0033] (3) The embodiments of this application can establish the relationship between the cell internal resistance, the charging SOC and the cell capacity, thereby calculating the root mean square error between the actual internal resistance and the reference internal resistance of the cell.

[0034] (4) In this embodiment of the application, the charging characterization curves under different charging conditions can be obtained by constructing the characterization test matrix, the battery electromotive force curve can be calculated by using the linear regression algorithm, the overpotential of the cell can be calculated based on the charging characterization curve and the electromotive force curve, and finally the reference internal resistance can be calculated based on the overpotential to establish an internal resistance database so that the reference internal resistance under different charging conditions can be queried in the subsequent internal resistance database.

[0035] (5) In this embodiment, the charging voltage curve can be divided into capacity ranges by step size according to the maximum charging capacity. The capacity range is divided into multiple grids by step size. The voltage value is calculated by interpolation extrapolation and normalized to obtain the electromotive force curve.

[0036] (6) The embodiments of this application can obtain the corresponding charging characterization curve by testing different charging conditions.

[0037] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0038] Figure 1 A flowchart illustrating the cell capacity estimation method provided in an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of charging characterization curves under different charging current conditions provided in an embodiment of the present invention;

[0040] Figure 3 The normalized charging characterization curve and electromotive force curve provided in the embodiments of the present invention;

[0041] Figure 4 The present invention provides a charging current curve and corresponding voltage curve of an LFP cell under actual charging conditions in an embodiment of the present invention.

[0042] Figure 5 A flowchart illustrating a method for estimating battery cell capacity according to an embodiment of the present invention;

[0043] Figure 6 This is a charging voltage curve obtained under different charging current conditions, provided in one embodiment of the present invention;

[0044] Figure 7 Normalized charging characterization curves and electromotive force curves are provided for one embodiment of the present invention;

[0045] Figure 8 Internal resistance curves of an LFP battery at 20°C under different current conditions are provided in one embodiment of the present invention.

[0046] Figure 9 A flowchart of a method for estimating battery cell capacity according to another embodiment of the present invention;

[0047] Figure 10 An example diagram of a cell capacity estimation device provided in an embodiment of the present invention;

[0048] Figure 11 A schematic diagram of a cell capacity estimation device provided for another embodiment of the present invention;

[0049] Figure 12 This is a schematic diagram of the server structure provided in an embodiment of the present invention. Detailed Implementation

[0050] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0051] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0052] Specifically, Figure 1 This is a flowchart illustrating a method for estimating battery cell capacity provided in an embodiment of this application.

[0053] like Figure 1 As shown, the method for estimating the cell capacity, applied to a server, includes the following steps:

[0054] In step S101, the actual internal resistance of the battery cell under any charging condition is obtained.

[0055] The battery cell can be any type of cell, such as a cell from an LFP battery pack, a nickel-metal hydride battery cell, or a lithium battery cell; the charging conditions include one or more of temperature, charging rate, current, and SOC.

[0056] In step S102, the pre-established internal resistance database is queried using any charging condition as an index, and the reference internal resistance of the cell under any charging condition is output.

[0057] The internal resistance database stores the relationship between arbitrary charging conditions and reference internal resistance (standard internal resistance). The specific steps for establishing the database are described in the following embodiments.

[0058] It is understood that, in the embodiments of this application, the corresponding reference internal resistance can be obtained by querying a pre-established internal resistance database based on the charging conditions, so as to calculate the actual capacity of the battery cell in the future.

[0059] In this embodiment of the application, before querying the pre-established internal resistance database, the method further includes: constructing a characterization test matrix of temperature and charging current; obtaining charging characterization curves under different charging conditions based on the characterization test matrix; performing linear regression processing on the charging characterization curves to obtain an electromotive force curve; calculating the overpotential of the cell based on the charging characterization curve and the electromotive force curve; calculating the reference internal resistance under different charging conditions based on the overpotential; and establishing an internal resistance database based on the reference internal resistance under different charging conditions.

[0060] The characterization test matrix is ​​constructed from two dimensions: temperature and charging current, as shown in Table 1. Table 1 is the characterization test matrix table.

[0061] Table 1

[0062]

[0063] It is understood that the embodiments of this application can construct a characterization test matrix from two dimensions: temperature and charging current. The characterization test matrix is ​​used to obtain charging characterization curves under different conditions such as temperature, charging rate, current and SOC. The charging characterization curves are linearly regressed to obtain the electromotive force curve. The overpotential is used to calculate the reference internal resistance of the battery cell under different conditions such as temperature, charging rate, current and SOC, so as to establish an internal resistance database.

[0064] In this embodiment of the application, the charging characterization curves under different charging conditions are obtained according to the characterization test matrix, including: discharging to a preset cutoff voltage under the first charging condition, resting for a first preset time, discharging to a preset cutoff voltage under the second charging condition, recording the charging characterization curves, and resting for a second preset time; discharging to a preset cutoff voltage under the first charging condition, and performing a charging test under the next charging condition, until all charging conditions have been tested, and the charging characterization curves under different charging conditions are obtained.

[0065] The first charging condition, the second charging condition, the first preset duration, and the second preset duration can be set according to specific circumstances, and there are no restrictions on them.

[0066] For example, taking the charging characterization at temperature T1 as an example, the test method is as follows:

[0067] 1. Discharge at a constant current using I01 until the cutoff voltage is reached;

[0068] 2. Let stand for 1 hour;

[0069] 3. Charge the circuit with a constant current of I11 until the cutoff voltage, and record the voltage curve.

[0070] 4. Let stand for 1 hour;

[0071] 5. Perform constant current discharge using I01 until the cutoff voltage is reached;

[0072] 6. Let stand for 1 hour;

[0073] 7. Repeat steps 1-7, using charging currents I21, I31, I41, I51, and I61 in sequence.

[0074] The obtained charging characterization curves under different charging current conditions are as follows: Figure 2 As shown.

[0075] In this embodiment of the application, the electromotive force curve is obtained by performing linear regression processing on the charging characterization curve, including: obtaining the maximum charging capacity corresponding to the charging characterization curve; determining the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, dividing the capacity interval into multiple grids according to the step size; calculating the voltage value when the current in each grid is a preset value by interpolation extrapolation, and generating the electromotive force curve after normalizing all voltage values.

[0076] The charging capacity of a battery is inversely proportional to its charging current; that is, its charging capacity is maximized when the charging current is minimum. This charging capacity is denoted as q. max .

[0077] It is understood that the embodiments of this application calculate the battery electromotive force curve based on the charging characterization curves under different charging conditions using a linear regression algorithm. The specific calculation method is as follows:

[0078] Will Figure 2 The curve is divided into 100 grids according to the maximum capacity, and the width of each grid is Δq=q. max / 100. In each grid cell, the voltage and current satisfy the following relationship:

[0079] AI + B = V, (1)

[0080] The coefficients in formula (1) are solved numerically, and the voltage value when the current is 0 is calculated by interpolation extrapolation. These voltage values ​​are then connected to obtain the equation. Figure 2 The curve shown by the dashed line is the battery electromotive force (EMF) curve. The EMF curve corresponds to a maximum capacity value, denoted as Q. max .

[0081] Based on the maximum charging capacity Figure 2 The curves in the image are normalized to convert the V~Q curve into a V~SOC curve, such as... Figure 3 As shown, the method for transforming the x-axis is as follows:

[0082] (2)

[0083] Based on the above calculation method, the EMF~SOC curves under different temperature conditions can be calculated.

[0084] Therefore, based on the charging characterization curve obtained through characterization testing and the EMF curve obtained through normalization processing as described in the above embodiments, the method for establishing the internal resistance database is as follows:

[0085] Based on the charging characterization curves and EMF curves obtained at different temperatures, the overpotential of the battery is calculated. :

[0086] (3)

[0087] Calculate the battery's internal resistance based on the overpotential value:

[0088] (4)

[0089] The internal resistance values ​​under different temperatures, currents, and SOCs can be calculated using formula (4), and a standard internal resistance database can be established.

[0090] In step S103, the actual capacity of the battery cell is calculated based on the actual internal resistance and reference internal resistance of the battery cell under any charging conditions.

[0091] The process of calculating the actual capacity of a battery cell based on its actual internal resistance and reference internal resistance under any charging condition includes: calculating the root mean square error between the actual internal resistance and reference internal resistance of the battery cell under any charging condition; determining the objective function based on the root mean square error; and finding the optimal solution for the objective function using the least squares method to obtain the actual capacity of any battery cell.

[0092] It is understood that the embodiments of this application can establish a mathematical relationship between the root mean square error and variables such as cell capacity by calculating the root mean square error between the actual internal resistance and the reference internal resistance, determine the objective function with the root mean square error, and optimize the calculation of the actual capacity of the cell by the least squares method.

[0093] In this embodiment of the application, the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under any charging condition is calculated by: establishing an expression for the relationship between the internal resistance, SOC and capacity of the battery cell; and calculating the root mean square error between the actual internal resistance and the reference internal resistance based on the expression.

[0094] Specifically, the steps for calculating the root mean square error between the actual internal resistance and the reference internal resistance are as follows: Figure 4 The figure shows the charging current curve and corresponding voltage curve of a certain LFP cell under actual charging conditions:

[0095] The SOC of cell numbered i at the start of charging is denoted as . Its capacity is denoted as Then, the SOC of cell i during the charging process can be calculated using the following formula:

[0096] (5)

[0097] At this point, the internal resistance of cell i can be calculated using the following formula:

[0098] (6)

[0099] Substituting formula (5) into formula (6), we obtain the internal resistance and capacity of cell i. Functional relationship:

[0100] (7)

[0101] Calculate the root mean square error between the actual internal resistance and the reference internal resistance of cell i:

[0102] (8)

[0103] Where j represents the j-th sampling point of current I, and N represents the total number of sampling points of I.

[0104] Finally, the optimal value can be obtained by finding the minimum value of formula (8). That is, the actual capacity of cell i.

[0105] The following specific embodiment illustrates the cell capacity estimation method of this application. Taking a cell from an LFP battery pack as an example, the process is as follows: Figure 5 As shown, the steps are as follows:

[0106] S1. Calculate the battery electromotive force curve (EMF) based on characterization tests and mathematical linear regression algorithms;

[0107] 1. Constructing the LFP charging characterization test matrix

[0108] Based on the performance reference table of a certain LFP cell, a charging test matrix is ​​constructed from two dimensions: temperature and charging current, as shown in Table 2. Table 2 is the charging characterization test matrix table.

[0109] Table 2

[0110]

[0111] 2. Characterization test methods to obtain charging characterization voltage curves.

[0112] Taking the 20℃ charging characterization as an example, the test method is as follows:

[0113] A. Discharge at a constant current of 0.3C until the cutoff voltage is reached;

[0114] B. Let stand for 1 hour;

[0115] C. Charge at a constant current of 0.1C to the cutoff voltage and record the voltage curve;

[0116] D. Let stand for 1 hour;

[0117] E. Discharge at a constant current of 0.3C until the cutoff voltage;

[0118] F. Let stand for 1 hour;

[0119] G. Repeat CF, with charging currents of 0.2C, 0.3C, 0.5C, 1C, and 2C in sequence;

[0120] H. Calculate the EMF curve.

[0121] After obtaining the voltage curves at different charging rates, the battery electromotive force curve is calculated using a linear regression algorithm. The specific calculation method is as follows:

[0122] The charging voltage curves obtained under different charging current conditions are as follows: Figure 6 As shown. The battery's charging capacity when charged to the cutoff voltage at a current of 0.1C is 92Ah. Figure 6 The curve is divided into 100 grids according to Δq = 92Ah / 100 = 0.92Ah. In each grid, the voltage and current satisfy formula (1). The coefficients in formula (1) are solved numerically, and the voltage value when the current is 0 is calculated by interpolation extrapolation. Connecting these voltage values ​​yields... Figure 6 The curve shown by the dashed line is the battery electromotive force (EMF) curve. The EMF curve corresponds to a maximum capacity value, denoted as Q. max In this embodiment, the Q of the LFP battery max =92.5Ah.

[0123] According to Q max And Figure 6 The curves in the image are normalized to convert the V~Q curve into a V~SOC curve, such as... Figure 7 As shown. Based on the above method, the EMF~SOC curves under all temperature conditions can be calculated.

[0124] S2. Obtain the internal resistance under different SOC, temperature, current and other conditions through characterization tests and internal resistance calculation methods, and establish a standard internal resistance database.

[0125] Based on the charging characterization curves and EMF curves obtained at different temperatures, the overpotential η (SOC) of the battery is calculated according to formula (3), and the internal resistance values ​​at different temperatures, currents, and SOCs are calculated according to formula (4). A standard internal resistance database is then established. Figure 8 The internal resistance curves of LFP batteries under different current conditions at 20℃ are shown.

[0126] S3. Calculate the internal resistance of the battery cell under actual charging conditions and establish the mathematical relationship between the internal resistance of the battery cell and the charging SOC and the battery cell capacity.

[0127] The initial SOC of cell 1 in the LFP battery pack is denoted as... Its capacity is denoted as The SOC of cell 1 during charging can be calculated using the following formula:

[0128] (9)

[0129] According to formula (7), the mathematical relationship between the internal resistance of cell No. 1 and the SOC and cell capacity can be expressed as:

[0130] (10)

[0131] S4. Calculate the root mean square error between the actual internal resistance and the standard internal resistance (reference internal resistance), establish the mathematical relationship between the root mean square error and variables such as cell capacity, and use the root mean square error as the objective function to calculate the cell capacity through least squares mathematical optimization.

[0132] Calculate the root mean square error between the actual internal resistance and the standard internal resistance of cell No. 1:

[0133] (11)

[0134] Where j represents the j-th sampling point of current I, and N represents the total number of sampling points of I. The cell capacity can be obtained by finding the minimum value of formula (11). The optimal solution.

[0135] Using the same method, the capacity of cells 1-104 in the LFP battery pack can be calculated, as shown in Table 3.

[0136] Table 3 shows the capacity of all cells in the LFP battery pack.

[0137] Table 3

[0138]

[0139] According to the cell capacity estimation method proposed in the embodiments of this application, the actual capacity of any cell can be calculated by comparing the actual internal resistance of the cell with the reference internal resistance. Since a pre-established internal resistance database is used, the reference internal resistance of the cell under any charging condition can be obtained, avoiding the influence of user charging conditions and battery system on the calculation results. It is effectively applicable to a variety of battery systems and achieves accurate calculation of cell capacity.

[0140] Furthermore, embodiments of this application also disclose a method for estimating cell capacity. The previous embodiment mainly focused on explaining the cell capacity estimation process from the perspective of online applications, while the next embodiment focuses on explaining the cell capacity estimation process from the perspective of offline applications. Any details not covered in the various embodiments can be referred to each other.

[0141] Figure 9 This is a flowchart of a method for estimating the capacity of a battery cell according to another embodiment of this application.

[0142] like Figure 9 As shown, this method for estimating battery cell capacity is applied to a server, and the method includes the following steps:

[0143] In step S201, a characterization test matrix for temperature and charging current is constructed.

[0144] The method for constructing the characterization test matrix has been described in the above embodiments and can be referred to the relevant representations in the above embodiments. To avoid redundancy, it will not be repeated here.

[0145] In step S202, charging characterization curves under different charging conditions are obtained based on the characterization test matrix, and the electromotive force curve is obtained by linear regression processing of the charging characterization curves.

[0146] It is understood that the embodiments of this application can obtain charging characterization curves under different charging conditions through the characterization test matrix, and use a linear regression algorithm to process them to obtain the electromotive force curve. The calculation method has been described in the above embodiments and will not be repeated here.

[0147] In step S203, the overpotential of the battery cell is calculated based on the charging characterization curve and the electromotive force curve. The reference internal resistance under different charging conditions is calculated based on the overpotential. An internal resistance database is established based on the reference internal resistance under different charging conditions.

[0148] The calculation methods for the overpotential of the battery cell and the reference internal resistance under different charging conditions have been described in the above embodiments and will not be repeated here.

[0149] In step S204, the reference internal resistance under any charging condition is queried using the internal resistance database, and the actual capacity of any cell is calculated based on the actual internal resistance and the reference internal resistance under any charging condition.

[0150] It is understood that the actual capacity of any battery cell can be calculated using the actual internal resistance and the reference internal resistance in the embodiments of this application.

[0151] According to the cell capacity estimation method proposed in the embodiments of this application, the actual capacity of any cell can be calculated by comparing the actual internal resistance of the cell with the reference internal resistance. It utilizes a pre-established internal resistance database to obtain the reference internal resistance of the cell under any charging conditions, avoiding the influence of user charging conditions and battery system on the calculation results. It is effectively applicable to a variety of battery systems and achieves accurate calculation of cell capacity.

[0152] Next, the cell capacity estimation device according to an embodiment of this application is described with reference to the accompanying drawings.

[0153] Figure 10 This is a block diagram of a cell capacity estimation device according to an embodiment of this application.

[0154] like Figure 10 As shown, the battery cell capacity estimation device 10 is applied to a server and includes: an acquisition module 101, an output module 102, and a first calculation module 103.

[0155] The acquisition module 101 is used to acquire the actual internal resistance of the battery cell under any charging condition; the output module 102 is used to query the pre-established internal resistance database with any charging condition as the index and output the reference internal resistance under any charging condition; the first calculation module 103 is used to calculate the actual capacity of the battery cell based on the actual internal resistance and reference internal resistance of the battery cell under any charging condition.

[0156] In this embodiment of the application, the first calculation module 103 is further used to: calculate the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under any charging condition; determine the objective function based on the root mean square error, and find the optimal solution of the objective function through the least squares method to obtain the actual capacity of the arbitrary battery cell.

[0157] In this embodiment, the first calculation module 103 is further used to: establish an expression between the internal resistance, SOC and capacity of the battery cell; and calculate the root mean square error between the actual internal resistance and the reference internal resistance based on the expression.

[0158] In this embodiment of the application, the apparatus 10 further includes a third computing module.

[0159] The third calculation module is used to construct a characterization test matrix of temperature and charging current before querying the pre-established internal resistance database; obtain the charging characterization curves under different charging conditions based on the characterization test matrix; perform linear regression processing on the charging characterization curves to obtain the electromotive force curve; calculate the overpotential of the cell based on the charging characterization curve and the electromotive force curve; calculate the reference internal resistance under different charging conditions based on the overpotential; and establish an internal resistance database based on the reference internal resistance under different charging conditions.

[0160] In this embodiment, the third calculation module is further configured to: obtain the maximum charging capacity corresponding to the charging characterization curve; determine the step size of the capacity interval corresponding to each charging voltage curve based on the maximum charging capacity; divide the capacity interval into multiple grids based on the step size; calculate the voltage value when the current in each grid is a preset value by interpolation extrapolation; and generate an electromotive force curve after normalizing all voltage values.

[0161] In this embodiment of the application, the third calculation module is further configured to: discharge to a preset cutoff voltage under the first charging condition, then remain stationary for a first preset time, discharge to a preset cutoff voltage under the second charging condition, and record the charging characterization curve; after remaining stationary for a second preset time, discharge to a preset cutoff voltage under the first charging condition, and perform a charging test under the next charging condition, until all charging conditions have been tested and the charging characterization curves under different charging conditions are obtained.

[0162] In the embodiments of this application, charging conditions include one or more of temperature, charging rate, current, and SOC.

[0163] It should be noted that the explanation of the aforementioned method embodiment for estimating cell capacity also applies to the cell capacity estimation device of this embodiment, and will not be repeated here.

[0164] According to the cell capacity estimation device proposed in the embodiments of this application, the actual capacity of any cell can be calculated by comparing the actual internal resistance of the cell with the reference internal resistance. Since a pre-established internal resistance database is used, the reference internal resistance of the cell under any charging conditions can be obtained, avoiding the influence of user charging conditions and battery system on the calculation results. It is effectively applicable to a variety of battery systems and achieves accurate calculation of cell capacity.

[0165] Figure 11 This is a schematic diagram of a cell capacity estimation device according to another embodiment of this application.

[0166] like Figure 11 As shown, the battery cell capacity estimation device 20 is applied to a server and includes: a construction module 201, a processing module 202, an establishment module 203, and a second calculation module 204.

[0167] The module 201 is used to construct a characterization test matrix for temperature and charging current; the processing module 202 is used to obtain charging characterization curves under different charging conditions based on the characterization test matrix, and to perform linear regression processing on the charging characterization curves to obtain an electromotive force curve; the establishment module 203 is used to calculate the overpotential of the cell based on the charging characterization curve and the electromotive force curve, to calculate the reference internal resistance under different charging conditions based on the overpotential, and to establish an internal resistance database based on the reference internal resistance under different charging conditions; the second calculation module 204 is used to query the reference internal resistance of the cell under any charging condition using the internal resistance database, and to calculate the actual capacity of the cell based on the actual internal resistance of the cell and the reference internal resistance under any charging condition.

[0168] It should be noted that the explanation of the aforementioned method embodiment for estimating cell capacity also applies to the cell capacity estimation device of this embodiment, and will not be repeated here.

[0169] The cell capacity estimation device proposed in the embodiments of this application can calculate the actual capacity of any cell by comparing the actual internal resistance of the cell with the reference internal resistance. It utilizes a pre-established internal resistance database to obtain the reference internal resistance of the cell under any charging conditions, avoiding the influence of user charging conditions and battery system on the calculation results. It is effectively applicable to various battery systems and achieves accurate calculation of cell capacity.

[0170] Figure 12 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0171] The memory 1201, the processor 1202, and the computer program stored on the memory 1201 and executable on the processor 1202.

[0172] When the processor 1202 executes the program, it implements the cell capacity estimation method provided in the above embodiments.

[0173] Furthermore, the server also includes:

[0174] Communication interface 1203 is used for communication between memory 1201 and processor 1202.

[0175] The memory 1201 is used to store computer programs that can run on the processor 1202.

[0176] The memory 1201 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0177] If the memory 1201, processor 1202, and communication interface 1203 are implemented independently, then the communication interface 1203, memory 1201, and processor 1202 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0178] Optionally, in a specific implementation, if the memory 1201, processor 1202, and communication interface 1203 are integrated on a single chip, then the memory 1201, processor 1202, and communication interface 1203 can communicate with each other through an internal interface.

[0179] The processor 1202 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0180] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for estimating battery cell capacity.

[0181] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0182] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0183] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0184] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0185] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0186] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method of estimating a capacity of an electric cell, characterized by, The method is applied to a server, and the method includes the following steps: Obtain the actual internal resistance of the battery cell under any charging condition; Using the arbitrary charging conditions as an index, query the pre-established internal resistance database and output the reference internal resistance of the cell under the arbitrary charging conditions; The actual capacity of the battery cell is calculated based on the actual internal resistance and reference internal resistance of the battery cell under any charging conditions. The calculation of the actual capacity of the battery cell based on the actual internal resistance and reference internal resistance under any charging conditions includes: Calculate the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under any charging condition; Using the root mean square error as the objective function, and finding the optimal solution for the objective function using the least squares method, the actual capacity of the battery cell is obtained; Before querying the pre-established internal resistance database, the following steps are also included: Construct a characterization test matrix for temperature and charging current; Based on the characterization test matrix, charging characterization curves under different charging conditions are obtained, and the electromotive force curve is obtained by linear regression processing of the charging characterization curves. The overpotential of the battery cell is calculated based on the charging characterization curve and the electromotive force curve. The reference internal resistance under different charging conditions is calculated based on the overpotential. The internal resistance database is established based on the reference internal resistance under different charging conditions.

2. The method of estimating the capacity of the battery cell according to claim 1, wherein, The calculation of the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under arbitrary charging conditions includes: Establish expressions relating the internal resistance, state of charge (SOC), and capacity of a battery cell; The root mean square error between the actual internal resistance and the reference internal resistance is calculated based on the expression.

3. The method of estimating the capacity of the battery cell according to claim 1, wherein, The process of obtaining the electromotive force curve by performing linear regression processing on the charging characterization curve includes: Obtain the maximum charging capacity corresponding to the charging characterization curve; The step size of the capacity interval corresponding to each charging voltage curve is determined based on the maximum charging capacity, and the capacity interval is divided into multiple grids based on the step size. The voltage value when the current in each grid is a preset value is calculated by interpolation extrapolation, and the electromotive force curve is generated after normalizing all voltage values.

4. The method of estimating the capacity of the battery cell according to claim 1, wherein, The step of obtaining charging characterization curves under different charging conditions based on the characterization test matrix includes: After discharging to a preset cutoff voltage under the first charging condition, the device is left to stand for a first preset time. After discharging to a preset cutoff voltage under the second charging condition, record the charging characterization curve and let it stand for a second preset time. Discharge to a preset cutoff voltage under the first charging condition, and then perform a charging test under the next charging condition until all charging conditions have been tested, thereby obtaining charging characterization curves under different charging conditions.

5. The method for estimating cell capacity according to any one of claims 1-4, characterized in that, The charging conditions include one or more of temperature, charging rate, current, and SOC.

6. An apparatus for estimating a capacity of an electric cell, characterized by comprising: The device is used in a server, wherein the device includes: The acquisition module is used to acquire the actual internal resistance of the battery cell under any charging conditions; The output module is used to query a pre-established internal resistance database using the arbitrary charging condition as an index, and output the reference internal resistance of the cell under the arbitrary charging condition. The first calculation module is used to calculate the actual capacity of the battery cell based on the actual internal resistance and reference internal resistance of the battery cell under any charging conditions. The calculation of the actual capacity of the battery cell based on the actual internal resistance and reference internal resistance under any charging conditions includes: Calculate the root mean square error between the actual internal resistance and the reference internal resistance of the battery cell under any charging condition; The objective function is determined based on the root mean square error, and the optimal solution of the objective function is found by the least squares method to obtain the actual capacity of the battery cell.

7. A server, characterized by include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the cell capacity estimation method as described in any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the cell capacity estimation method as described in any one of claims 1-5.

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