WO2024244252A1 - 电芯容量的估计方法、装置、服务器及存储介质 - Google Patents

电芯容量的估计方法、装置、服务器及存储介质 Download PDF

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Publication number
WO2024244252A1
WO2024244252A1 PCT/CN2023/123733 CN2023123733W WO2024244252A1 WO 2024244252 A1 WO2024244252 A1 WO 2024244252A1 CN 2023123733 W CN2023123733 W CN 2023123733W WO 2024244252 A1 WO2024244252 A1 WO 2024244252A1
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Prior art keywords
charging
charging voltage
voltage curve
capacity
battery cell
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English (en)
French (fr)
Inventor
李东江
李宗华
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Deepal Automobile Technology Co Ltd
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Deepal Automobile Technology Co Ltd
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Priority to EP23939225.1A priority Critical patent/EP4564030A4/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/80—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries including monitoring or indicating arrangements
    • H02J7/82—Control of state of charge [SOC]

Definitions

  • the present application relates to the technical field of battery management systems, and in particular to a method, device, server and storage medium for estimating battery cell capacity.
  • a battery pack generally contains dozens or even hundreds of battery cells.
  • the capacity of the battery pack is determined by the capacity of each battery cell. Accurately calculating the capacity of each battery cell can not only obtain the capacity decay of each battery cell during use, but also calculate the current capacity of the battery pack based on the real-time calculated battery cell capacity, and further accurately calculate the battery's SOH (State of Health).
  • the battery pack is controlled by the maximum voltage and minimum voltage of the battery cell during charge/discharge, when the capacity and SOC (State of charge) of each battery cell in the battery pack are inconsistent, the capacity of each battery cell cannot be obtained by simple charge and discharge.
  • the corresponding relationship between voltage and capacity during constant current charging (discharging) ⁇ V ⁇ Q is used to calculate the total capacity of the battery cell.
  • this method is effective for battery systems such as NMC (ternary materials), but not for lithium iron phosphate (LiFePO4, LFP) battery systems.
  • the voltage curve of the LFP battery system is very flat, which makes the relationship between ⁇ V ⁇ Q in the voltage platform area extremely sensitive. A slight change in voltage will cause a significant change in capacity, resulting in a large error in the estimated capacity.
  • the present application provides a method, device, server and storage medium for estimating the capacity of a battery cell, so as to solve the problems that the method for calculating the capacity of a battery cell in the related art is affected by the battery system and the calculation accuracy is low.
  • a first aspect embodiment of the present application provides a method for estimating the capacity of a battery cell, which is applied to a server, wherein the method includes the following steps: obtaining actual charging data of a battery cell in one or more state of charge (SOC) intervals; generating an actual charging voltage curve for the corresponding SOC interval based on the actual charging data in each SOC interval, and querying a pre-established voltage characteristic database with each SOC interval as an index, and outputting a reference charging voltage curve for each SOC interval; and calculating the actual capacity of the battery cell based on the actual charging voltage curve for each SOC interval and the reference charging voltage curve.
  • SOC state of charge
  • the embodiment of the present application can accurately calculate the actual capacity of the battery cell by comparing the actual charging voltage curve of the battery cell with the corresponding reference charging voltage curve. Since a pre-calibrated standard reference charging voltage curve is used, the actual capacity of the battery cell can be accurately compared according to the voltage curve, avoiding the influence of the battery system on the estimation result. It is effectively applicable to multiple battery systems.
  • the estimation method of the embodiment of the present application can be deployed on a server to improve the efficiency of calculation by using the computing resources of the server.
  • calculating the actual capacity of the battery cell according to the actual charging voltage curve and the reference charging voltage curve in each SOC interval includes: calculating the root mean square error of the battery cell according to the actual charging voltage curve and the reference charging voltage curve in each SOC interval, and determining an objective function according to the root mean square error; and finding an optimal solution for the objective function by using a least squares method to obtain the actual capacity of the battery cell.
  • the embodiment of the present application can calculate the actual capacity of the battery cell by calculating the root mean square error between the actual charging voltage curve and the reference charging voltage curve and using it to determine the objective function, and then calculate the actual capacity of the battery cell by the least squares method.
  • the calculating the root mean square error of the battery cell according to the actual charging voltage curve and the reference charging voltage curve of each SOC interval includes: dividing the total charging time of the reference battery cell into different charging stages; adding an interval identifier to the SOC interval corresponding to each charging stage, and establishing a mathematical expression between the SOC interval, SOC and capacity according to the interval identifier; using the mathematical expression to extract the actual charging voltage curve and the corresponding reference charging voltage curve, and calculating the root mean square error of the battery cell according to the respective mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • the embodiment of the present application can divide the total charging time of the reference battery cell into different charging stages, set interval identifiers for the SOC intervals of different charging stages, establish mathematical expressions for different SOC intervals, and further use mathematical expressions to extract the actual charging voltage curve and the corresponding reference charging voltage curve, and calculate the root mean square error according to the mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • the pre-established voltage characteristic database before querying the pre-established voltage characteristic database, it also includes: constructing a charging test matrix of temperature and charging current; obtaining charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolating the charging voltage curves at different temperatures and/or different charging rates to obtain multiple reference charging voltage curves; constructing the voltage characteristic database according to each SOC interval and the corresponding reference charging voltage curve.
  • the embodiment of the present application can construct a charging test matrix from two dimensions of temperature and charging current, and interpolate the charging voltage curves at different charging temperatures and rates obtained through the charging matrix to obtain multiple reference charging voltage curves, which are then used together with the SOC interval to construct a voltage feature database, so as to subsequently find the corresponding reference charging voltage curve according to the SOC interval.
  • the interpolation processing of the charging voltage curves at different temperatures and/or different charging rates to obtain multiple reference charging voltage curves includes: calculating the maximum charging capacity corresponding to the charging voltage curves at all different rates; determining the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and dividing the capacity interval into multiple grids according to the step size; calculating a first constant according to the voltage value of each grid, obtaining the charging voltage value under any charging current according to the first constant, and generating multiple reference charging voltage curves based on the charging voltage value under the arbitrary charging current.
  • the embodiment of the present application can interpolate the charging voltage curves under different charging rates, and calculate the reference charging voltage curve corresponding to the charging voltage value under any charging current through a formula.
  • the interpolating the charging voltage curves at different temperatures and/or different charging rates to obtain a plurality of reference charging voltage curves includes: calculating the maximum charging capacity corresponding to the charging voltage curves at all different temperatures; The step size of the capacity interval corresponding to each charging voltage curve is determined according to the maximum charging capacity, and the capacity interval is divided into a plurality of grids according to the step size; a second constant is calculated according to the voltage value of each grid, a voltage curve under any temperature condition is obtained according to the second constant, and a plurality of reference charging voltage curves are obtained based on the voltage curve under any temperature condition.
  • the embodiment of the present application can interpolate the charging voltage curves at different temperatures, and calculate the reference charging voltage curve corresponding to the voltage curve under any temperature condition through a formula.
  • obtaining the charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix includes: at a preset temperature, after constant current discharge to a preset cut-off voltage with a first preset current, switching to a second preset current for constant current discharge to the preset cut-off voltage, and after standing for a first preset time, constant current charging to the preset cut-off voltage with a third preset current, and recording the charging voltage curve at the current temperature and/or the current charging rate; after standing for a second preset time, constant current discharge to a preset cut-off voltage with a fourth preset current, switching to a fifth preset current for constant current discharge to the preset cut-off voltage, and re-performing charging tests at other charging rates and/or other charging temperatures after standing for a third preset time.
  • the embodiment of the present application can obtain charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, so as to subsequently obtain a reference charging voltage curve according to the charging voltage curve.
  • a second aspect of the present application provides a method for estimating the capacity of a battery cell, which is applied to a server, wherein the method includes the following steps: constructing a charging test matrix of temperature and charging current; obtaining charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolating the charging voltage curves at different temperatures and/or different charging rates to obtain a reference charging voltage curve for each SOC interval; constructing a voltage characteristic database according to the reference charging voltage curve for each SOC interval, using the voltage characteristic database to query the reference charging voltage curve for each SOC interval, and calculating the actual capacity of the battery cell according to the actual charging voltage curve and the reference charging voltage curve for each SOC interval.
  • a third aspect of the present application provides a device for estimating the capacity of a battery cell, which is applied to a server, wherein the device includes: obtaining actual charging data of the battery cell in one or more state of charge (SOC) intervals; generating an actual charging voltage curve for the corresponding SOC interval based on the actual charging data in each SOC interval, and using each SOC interval as an index to query a pre-established voltage characteristic database, and output a reference charging voltage curve for each SOC interval; and calculating the actual capacity of the battery cell based on the actual charging voltage curve for each SOC interval and the reference charging voltage curve.
  • SOC state of charge
  • the first calculation module is further used to: calculate the root mean square error of the battery cell based on the actual charging voltage curve and the reference charging voltage curve of each SOC interval, and determine the objective function based on the root mean square error; find the optimal solution for the objective function through the least squares method to obtain the actual capacity of the battery cell.
  • the first calculation module is further used to: divide the total charging time of the reference battery cell into different charging stages; add an interval identifier to the SOC interval corresponding to each charging stage, and establish a mathematical expression between the SOC interval, SOC and capacity according to the interval identifier; use the mathematical expression to extract the actual charging voltage curve and the corresponding reference charging voltage curve, and calculate the root mean square error of the battery cell according to the respective mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • a processing module which is used to construct a charging test matrix of temperature and charging current before querying a pre-established voltage characteristic database; obtain charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolate the charging voltage curves at different temperatures and/or different charging rates to obtain multiple reference charging voltage curves; construct the voltage characteristic database according to each SOC interval and the corresponding reference charging voltage curve.
  • the processing module is further used to: calculate the maximum charging capacity corresponding to the charging voltage curves at all different rates; determine the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and divide the capacity interval into multiple grids according to the step size; calculate a first constant according to the voltage value of each grid, obtain the charging voltage value under any charging current according to the first constant, and generate multiple reference charging voltage curves based on the charging voltage value under the arbitrary charging current.
  • the processing module is further used to: calculate the maximum charging capacity corresponding to the charging voltage curves at all different temperatures; determine the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and divide the capacity interval into multiple grids according to the step size; calculate a second constant according to the voltage value of each grid, obtain the voltage curve under any temperature condition according to the second constant, and obtain multiple reference charging voltage curves based on the voltage curve under any temperature condition.
  • the processing module is further used to: at a preset temperature, after constant current discharge with a first preset current to a preset cut-off voltage, switch to a second preset current to perform constant current discharge to the preset cut-off voltage, and after standing for a first preset time, perform constant current charging with a third preset current to the preset cut-off voltage, and record the charging voltage curve at the current temperature and/or the current charging rate; after standing for a second preset time, after constant current discharge with a fourth preset current to a preset cut-off voltage, switch to a fifth preset current to perform constant current discharge to the preset cut-off voltage, and after standing for a third preset time, re-perform charging tests at other charging rates and/or other charging temperatures.
  • a fourth aspect of the present application provides a device for estimating the capacity of a battery cell, which is applied to a server, wherein the device includes: a construction module, which is used to construct a charging test matrix of temperature and charging current; an interpolation module, which is used to obtain charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolate the charging voltage curves at different temperatures and/or different charging rates to obtain a reference charging voltage curve for each SOC interval; a second calculation module, which is used to construct a voltage characteristic database according to the reference charging voltage curve of each SOC interval, use the voltage characteristic database to query the reference charging voltage curve of each SOC interval, and calculate the actual capacity of the battery cell according to the actual charging voltage curve of each SOC interval and the reference charging voltage curve.
  • a construction module which is used to construct a charging test matrix of temperature and charging current
  • an interpolation module which is used to obtain charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolate
  • the fifth aspect of the present application provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating the cell capacity as described in the above embodiment.
  • the sixth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for estimating the capacity of a battery cell as described in the above embodiment.
  • the embodiment of the present application can accurately calculate the actual capacity of the battery cell by comparing the actual charging voltage curve of the battery cell with the corresponding reference charging voltage curve. Since a pre-calibrated standard reference charging voltage curve is used, the actual capacity of the battery cell can be accurately calculated according to The voltage curve accurately compares the actual capacity of the battery cell, avoids the influence of the battery system on the estimation result, and is effectively applicable to multiple battery systems.
  • the estimation method of the embodiment of the present application can be deployed on a server to use the computing resources of the server to improve the computing efficiency.
  • the actual capacity of the battery cell can be calculated by calculating the root mean square error between the actual charging voltage curve and the reference charging voltage curve and using it to determine the objective function, and then the least square method can be used to find the optimal solution.
  • the embodiment of the present application can divide the total charging time of the reference battery cell into different charging stages, set interval identifiers for the SOC intervals of different charging stages, establish mathematical expressions for different SOC intervals, further use the mathematical expressions to extract the actual charging voltage curve and the corresponding reference charging voltage curve, and calculate the root mean square error based on the mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • a charging test matrix can be constructed from two dimensions: temperature and charging current.
  • the charging voltage curves at different charging temperatures and rates obtained through the charging matrix are interpolated to obtain multiple reference charging voltage curves.
  • the voltage characteristic database is constructed by using the reference charging voltage curves and the SOC interval to facilitate the subsequent search for the corresponding reference charging voltage curve according to the SOC interval.
  • the embodiment of the present application can interpolate the charging voltage curves under different charging rates, and calculate the reference charging voltage curve corresponding to the charging voltage value under any charging current through a formula.
  • the embodiment of the present application can interpolate the charging voltage curves at different temperatures and calculate the reference charging voltage curve corresponding to the voltage curve under any temperature condition through a formula.
  • the charging voltage curves at different charging temperatures and/or different charging rates can be obtained according to the charging test matrix, so as to subsequently obtain a reference charging voltage curve according to the charging voltage curve.
  • FIG1 is a flow chart of a method for estimating cell capacity according to an embodiment of the present application.
  • FIG2 is a schematic diagram of charging interval division and interval identification according to an embodiment of the present application.
  • FIG3 is a flow chart of a method for estimating cell capacity according to an embodiment of the present application.
  • FIG4 is a schematic diagram of the division and identification of charging intervals for any battery cell in an LFP battery pack according to an embodiment of the present application
  • FIG5 is a flow chart of a method for estimating cell capacity according to another embodiment of the present application.
  • FIG6 is an exemplary diagram of a device for estimating cell capacity according to an embodiment of the present application.
  • FIG7 is an exemplary diagram of a device for estimating cell capacity according to another embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a server provided according to an embodiment of the present application.
  • the following describes the estimation method, device, server and storage medium of the battery cell capacity of the embodiment of the present application with reference to the accompanying drawings.
  • the corresponding relationship ⁇ V ⁇ Q between voltage and capacity during constant current charging (discharging) is used to calculate the total capacity of the battery cell, but this method is effective for battery systems such as NMC, but not applicable to LFP battery systems, and the estimation error of capacity is very large.
  • the present application provides a method for estimating the capacity of a battery cell. In this method, the actual capacity of the battery cell is accurately calculated by comparing the actual charging voltage curve of the battery cell with the corresponding reference charging voltage curve.
  • the actual capacity of the battery cell can be accurately compared according to the voltage curve, avoiding the influence of the battery system on the estimation result, and it is effectively applicable to multiple battery systems.
  • the problem that the method for calculating the capacity of the battery cell in the related art is affected by the battery system and the calculation accuracy is low is solved.
  • FIG1 is a flow chart of a method for estimating cell capacity provided in an embodiment of the present application.
  • the method for estimating the cell capacity is applied to a server and includes the following steps:
  • step S101 actual charging data of a battery cell in one or more state of charge (SOC) intervals is obtained.
  • SOC state of charge
  • the battery cell may be any battery cell, for example, a battery cell in an LFP battery pack, a nickel-metal hydride battery cell, a lithium battery cell, etc.
  • step S102 an actual charging voltage curve corresponding to each SOC interval is generated according to the actual charging data in each SOC interval, and a pre-established voltage characteristic database is queried with each SOC interval as an index to output a reference charging voltage curve for each SOC interval.
  • the voltage characteristic database stores the relationship between the SOC interval and the reference charging voltage curve (that is, the standard charging voltage curve).
  • the specific establishment method is described in the following embodiment and will not be repeated here.
  • the embodiments of the present application can generate an actual charging voltage curve corresponding to the SOC interval based on the actual charging data of the battery cell in each SOC interval, query the reference charging voltage curve corresponding to each SOC interval in a pre-established voltage characteristic database, and subsequently calculate the required values based on the actual charging voltage curve and the reference charging voltage curve.
  • before querying a pre-established voltage characteristic database it also includes: constructing a charging test matrix of temperature and charging current; obtaining charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolating the charging voltage curves at different temperatures and/or different charging rates to obtain multiple reference charging voltage curves; constructing a voltage characteristic database according to each SOC interval and the corresponding reference charging voltage curve.
  • the embodiment of the present application can construct a charging test matrix from two dimensions of temperature and charging current, as shown in Table 1, and then obtain the charging voltage curves under different temperatures and charging rates according to the charging test matrix, perform interpolation processing to obtain the reference charging voltage curve, and finally construct a voltage feature database according to each SOC interval and the corresponding reference charging voltage curve.
  • Table 1 is a charging test matrix table.
  • a charging voltage curve at different charging temperatures and/or different charging rates is obtained according to a charging test matrix, including: at a preset temperature, after constant current discharge to a preset cut-off voltage with a first preset current, switching to a second preset current for constant current discharge to a preset cut-off voltage, and standing for a first preset time, constant current charging to a preset cut-off voltage with a third preset current, and recording the charging voltage curve at the current temperature and/or the current charging rate; after standing for a second preset time, constant current discharge to a preset cut-off voltage with a fourth preset current, switching to a fifth preset current for constant current discharge to a preset cut-off voltage, and re-performing charging tests at other charging rates and/or other charging temperatures after standing for a third preset time.
  • the preset temperature can be set according to the specific situation. It is a charging and discharging process under constant temperature conditions.
  • the first preset current, the second preset current, the third preset current, the fourth preset current and the fifth preset current can be set according to the specific situation.
  • the first preset time length, the second preset time length and the third preset time length can be set according to the specific situation. There is no limitation on this.
  • the method for obtaining the reference charging voltage curve is as follows:
  • the charging voltage curves at different temperatures and/or different charging rates are interpolated to obtain multiple reference charging voltage curves, including: calculating the maximum charging capacity corresponding to the charging voltage curves at all different rates; determining the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and dividing the capacity interval into multiple grids according to the step size; calculating a first constant according to the voltage value of each grid, obtaining the charging voltage value under any charging current according to the first constant, and generating multiple reference charging voltage curves based on the charging voltage value under any charging current.
  • interpolation processing is required. Taking the charging voltage curves at different rates at temperature T1 as an example, the interpolation method is as follows:
  • the charging voltage curves at different temperatures and/or different charging rates are interpolated to obtain multiple reference charging voltage curves, including: calculating the maximum charging capacity corresponding to the charging voltage curves at all different temperatures; determining the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and dividing the capacity interval into multiple grids according to the step size; calculating a second constant according to the voltage value of each grid, obtaining the voltage curve under any temperature condition according to the second constant, and obtaining multiple reference charging voltage curves based on the voltage curve under any temperature condition.
  • step S103 the actual capacity of the battery cell is calculated according to the actual charging voltage curve and the reference charging voltage curve in each SOC interval.
  • the embodiment of the present application queries the reference charging voltage curve corresponding to each SOC interval in a pre-established voltage characteristic database, and calculates the actual capacity of the battery cell using the actual charging voltage curve and the reference charging voltage curve.
  • the specific calculation method is described as follows.
  • the actual capacity of the battery cell is calculated based on the actual charging voltage curve and the reference charging voltage curve in each SOC interval, including: calculating the root mean square error of the battery cell based on the actual charging voltage curve and the reference charging voltage curve in each SOC interval, and determining the objective function based on the root mean square error; finding the optimal solution for the objective function through the least squares method to obtain the actual capacity of the battery cell.
  • the root mean square error of the battery cell is calculated according to the actual charging voltage curve and the reference charging voltage curve of each SOC interval, including: dividing the total charging time of the reference battery cell into different charging stages; adding an interval identifier to the SOC interval corresponding to each charging stage, and establishing a mathematical expression between the SOC interval, SOC and capacity according to the interval identifier; using the mathematical expression to extract the actual charging voltage curve and the corresponding reference charging voltage curve, and calculating the root mean square error of the battery cell according to the respective mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • the embodiments of the present application can divide the total charging time into different charging stages according to the numerical change of the actual charging current of the reference battery cell, identify the SOC interval corresponding to each charging stage of the battery cell, establish mathematical expressions of the SOC interval, SOC and capacity according to the interval identification, extract the actual charging voltage curve corresponding to the battery cell in the SOC interval and the mathematical expression of the corresponding reference charging voltage curve according to the mathematical expression, calculate the root mean square error of the battery cell, determine the objective function through the root mean square error, use the least squares method to find the optimal solution for the objective function, and obtain the actual capacity of the battery cell.
  • the embodiment of the present application divides the entire charging process into several intervals, wherein t1 is in the first stage charging stage, and its corresponding charging SOC interval is recorded as ⁇ 1 ; t2 is in the second charging stage, and the charging voltage has tended to be stable at time t2 ; t3 is the moment when the second stage charging process is about to end, and the charging SOC interval corresponding to time t2 to time t3 is recorded as ⁇ 2 ; t4 is in the third stage charging stage, and the charging voltage curve has tended to be stable at time t4 ; t5 is the moment when the third stage charging process is about to end.
  • the charging SOC interval corresponding to the time t4 to t5 is recorded as ⁇ 3 ; the time interval divisions and corresponding SOC interval divisions in other stages of the charging process are similar, and are recorded as t6 , t7 , ⁇ 4 ; t8 , t9 , ⁇ 5 , etc., as shown in FIG2 .
  • the actual charging voltage curves corresponding to different SOC intervals in the total charging voltage curve are extracted.
  • the actual charging voltage curves corresponding to different charging stages are extracted and recorded as
  • the reference charging voltage curve (V(I,T,x)) in the voltage characteristic database is queried to calculate the actual charging voltage curve of each battery cell.
  • the root mean square error with the reference charging voltage curve is used to calculate the actual capacity of each battery cell based on the least squares method.
  • the calculation formula is as follows:
  • K 1 , K 2 , K 3 , K 4 . . . are the weights for optimizing each charging stage.
  • V f(I, T, x) of charging voltage, current, temperature, and SOC of an LFP battery cell, that is, construct a characteristic database of a standard charging voltage curve (reference charging voltage curve) of an LFP battery cell;
  • a charging test matrix is constructed from two dimensions: temperature and charging current, as shown in Table 2.
  • Table 2 is an example table of the charging test matrix.
  • the method for obtaining the reference charging voltage curve is as follows:
  • interpolation processing is required to construct a voltage feature database. Taking the charging voltage curves at different rates at 25°C as an example, the interpolation method is as follows:
  • the feature database of the temperature dimension can be obtained by the following method:
  • the maximum charging capacity Q max 98 Ah corresponding to the charging voltage curves at all different temperatures is calculated.
  • the capacity intervals corresponding to all charging curves are divided into 100 grids with a step size of 0.98 Ah. In each grid, formula (2) is satisfied.
  • the values of a and c are obtained by solving equation (2).
  • the voltage curve under any temperature condition can be obtained through the values of a and c.
  • the total charging time is divided into different charging stages t 1 , t 2 , t 3 , t 4 ..., and the SOC intervals corresponding to the different charging stages of each battery cell are marked as
  • Figure 4 shows the actual charging curve of any cell i in a certain type of LFP battery pack.
  • the entire charging process is divided into 7 SOC intervals, among which:
  • the corresponding charging time is [0t 1 ],
  • the corresponding charging time is [t 2 t 3 ],
  • the corresponding charging time is [t 4 t 5 ],
  • the corresponding charging time is [t 6 t 7 ],
  • the corresponding charging time is [t 8 t 9 ],
  • the corresponding charging time is [t 10 t 11 ]
  • the corresponding charging time is [t 12 t 13 ], where the values of t 1 to t 13 are shown in Table 3, in hours.
  • Table 3 shows the time corresponding to different charging processes.
  • the selected LFP battery cell temperature standard capacity is 90Ah, and the SOC (i.e., initial SOC) at the start of charging of the battery cell numbered i is recorded as The capacity is recorded as The expressions for different SOC intervals are:
  • the reference charging voltage curve (V(I,T,x)) in the voltage characteristic database is queried to calculate the actual charging voltage curve of each battery cell
  • the root mean square error with the reference charging voltage curve is used to calculate the actual capacity of each battery cell based on the least squares method.
  • the calculation formula is as follows:
  • K 1 , K 2 , K 3 , K 4 , K 5 , K 6 , and K 7 are the weights for optimizing each charging stage. Calculate the actual capacity of each battery cell As shown in Table 4. Table 4 shows the capacity of each cell in the LFP battery pack.
  • the actual capacity of the battery cell can be accurately calculated by comparing the actual charging voltage curve of the battery cell with the corresponding reference charging voltage curve. Since a pre-calibrated standard reference charging voltage curve is used, the actual capacity of the battery cell can be accurately compared according to the voltage curve, avoiding the influence of the battery system on the estimation result, and being effectively applicable to multiple battery systems.
  • the estimation method of the embodiment of the present application can be deployed on a server, and the computing resources of the server can be used to improve the efficiency of the calculation;
  • the actual capacity of the battery cell can be calculated by calculating the root mean square error between the actual charging voltage curve and the reference charging voltage curve and using it to determine the objective function, and the least square method can be used to find the optimal solution;
  • the total charging time of the reference battery cell can be divided into different charging stages, and interval identifiers are set for the SOC intervals of different charging stages, and mathematical expressions for different SOC intervals are established, and the actual charging voltage curve and the corresponding reference charging voltage curve are further extracted by using the mathematical expressions, and the root mean square error is calculated according to the mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval;
  • a charging test matrix can be constructed from the two dimensions of temperature and charging current, and the charging voltage curves at different charging temperatures and rates obtained through the charging matrix are interpolated to obtain Multiple reference charging voltage curves can be obtained, and a
  • FIG. 5 is a flow chart of another method for estimating cell capacity according to an embodiment of the present application.
  • the method for estimating the cell capacity is applied to a server and includes the following steps:
  • step S201 a charging test matrix of temperature and charging current is constructed.
  • step S202 charging voltage curves at different charging temperatures and/or different charging rates are obtained according to the charging test matrix, and the charging voltage curves at different temperatures and/or different charging rates are interpolated to obtain a reference charging voltage curve for each SOC interval.
  • the embodiments of the present application can obtain the charging voltage curves at different charging temperatures and charging rates according to the charging test matrix, and then perform interpolation processing to obtain the reference charging voltage curve for each SOC interval, wherein the interpolation processing method has been explained in the above embodiments and will not be repeated here.
  • step S203 a voltage characteristic database is constructed according to the reference charging voltage curve of each SOC interval, the reference charging voltage curve of each SOC interval is queried using the voltage characteristic database, and the actual capacity of the battery cell is calculated according to the actual charging voltage curve of each SOC interval and the reference charging voltage curve.
  • the voltage characteristic database stores the relationship between the SOC interval and the reference charging voltage curve.
  • the voltage characteristic database can be used to query the reference charging voltage curve corresponding to the SOC interval.
  • the embodiments of the present application can use the voltage characteristic database to query the reference charging voltage curve of each SOC interval, calculate the actual capacity and initial SOC of the battery cell based on the actual charging voltage curve of each SOC interval and the corresponding reference charging voltage curve, and use the actual capacity and initial SOC to calculate the actual SOC corresponding to the battery cell during the charging process, wherein the specific calculation method has been explained in the above embodiments and will not be repeated here.
  • a charging test matrix is constructed from the two dimensions of temperature and charging current, and charging voltage curves under different charging temperatures and charging rates are obtained.
  • the charging voltage curve is interpolated to obtain a reference charging voltage curve.
  • the actual capacity of the battery cell is calculated based on the reference charging voltage curve based on the actual charging voltage curve of the battery cell in each SOC interval, and the calculation method is deployed on a server. It is not affected by the battery system and can accurately calculate the capacity of each battery cell in the battery pack.
  • FIG. 6 is a block diagram of a device for estimating cell capacity according to an embodiment of the present application.
  • the cell capacity estimation device 10 includes: an acquisition module 101 , an output module 102 and a first calculation module 103 .
  • the acquisition module 101 is used to acquire the actual charging data of the battery cell in one or more state of charge SOC intervals; the output module 102 is used to generate the actual charging data of the corresponding SOC interval according to the actual charging data in each SOC interval.
  • the first calculation module 103 is used to calculate the actual capacity of the battery cell according to the actual charging voltage curve of each SOC interval and the reference charging voltage curve.
  • the first calculation module 103 is further used to: calculate the root mean square error of the battery cell based on the actual charging voltage curve and the reference charging voltage curve in each SOC interval, and determine the objective function based on the root mean square error; find the optimal solution for the objective function through the least squares method to obtain the actual capacity of the battery cell.
  • the first calculation module 103 is further used to: calculate the root mean square error of the battery cell based on the actual charging voltage curve and the reference charging voltage curve in each SOC interval, and determine the objective function based on the root mean square error; find the optimal solution for the objective function through the least squares method to obtain the actual capacity of the battery cell.
  • the device 10 of the present application further includes: a processing module.
  • the processing module is used to construct a charging test matrix of temperature and charging current before querying a pre-established voltage characteristic database; obtain charging voltage curves under different charging temperatures and/or different charging rates according to the charging test matrix, and interpolate the charging voltage curves under different temperatures and/or different charging rates to obtain multiple reference charging voltage curves; construct a voltage characteristic database according to each SOC interval and the corresponding reference charging voltage curve.
  • the processing module is further used to: calculate the maximum charging capacity corresponding to the charging voltage curves at all different rates; determine the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and divide the capacity interval into multiple grids according to the step size; calculate a first constant according to the voltage value of each grid, obtain the charging voltage value under any charging current according to the first constant, and generate multiple reference charging voltage curves based on the charging voltage value under any charging current.
  • the processing module is further used to: calculate the maximum charging capacity corresponding to the charging voltage curves at all different temperatures; determine the step size of the capacity interval corresponding to each charging voltage curve according to the maximum charging capacity, and divide the capacity interval into multiple grids according to the step size; calculate a second constant according to the voltage value of each grid, obtain the voltage curve under any temperature condition according to the second constant, and obtain multiple reference charging voltage curves based on the voltage curve under any temperature condition.
  • the processing module is further used to: at a preset temperature, after constant current discharge to a preset cut-off voltage with a first preset current, switch to a second preset current for constant current discharge to a preset cut-off voltage, and after standing for a first preset time, perform constant current charging to a preset cut-off voltage with a third preset current, and record the charging voltage curve at the current temperature and/or the current charging rate; after standing for a second preset time, after constant current discharge to a preset cut-off voltage with a fourth preset current, switch to a fifth preset current for constant current discharge to a preset cut-off voltage, and after standing for a third preset time, re-perform charging tests at other charging rates and/or other charging temperatures.
  • the actual capacity of the battery cell can be accurately calculated by comparing the actual charging voltage curve of the battery cell with the corresponding reference charging voltage curve. Since a pre-calibrated standard reference charging voltage curve is used, the actual capacity of the battery cell can be accurately compared according to the voltage curve, avoiding the influence of the battery system on the estimation result. It is effectively applicable to multiple battery systems.
  • the estimation method of the embodiment of the present application can be deployed in the service The computing resources of the server can be used to improve the computing efficiency.
  • the actual capacity of the battery cell can be calculated by calculating the root mean square error between the actual charging voltage curve and the reference charging voltage curve and using it to determine the objective function, and the least square method can be used to find the best solution.
  • the total charging time of the reference battery cell can be divided into different charging stages, and interval identifiers can be set for the SOC intervals of different charging stages.
  • Mathematical expressions for different SOC intervals can be established, and the actual charging voltage curve and the corresponding reference charging voltage curve can be extracted by mathematical expressions.
  • the root mean square error can be calculated according to the mathematical expressions of the actual charging voltage curve and the reference charging voltage curve of each SOC interval.
  • a charging test matrix is constructed from the two dimensions of temperature and charging current.
  • the charging voltage curves at different charging temperatures and rates obtained through the charging matrix are interpolated to obtain multiple reference charging voltage curves, which are used together with the SOC interval to construct a voltage feature database so that the corresponding reference charging voltage curve can be found according to the SOC interval later; the charging voltage curves at different charging rates can be interpolated, and the reference charging voltage curve corresponding to the charging voltage value at any charging current can be calculated by a formula; the charging voltage curves at different temperatures can be interpolated, and the reference charging voltage curve corresponding to the voltage curve under any temperature conditions can be calculated by a formula.
  • FIG. 7 is a block diagram of a device for estimating cell capacity according to another embodiment of the present application.
  • the cell capacity estimation device 20 is applied to a server and includes: a construction module 201 , an interpolation module 202 and a second calculation module 203 .
  • the construction module 201 is used to construct a charging test matrix of temperature and charging current;
  • the interpolation module 202 is used to obtain the charging voltage curves at different charging temperatures and/or different charging rates according to the charging test matrix, and interpolate the charging voltage curves at different temperatures and/or different charging rates to obtain the reference charging voltage curve of each SOC interval;
  • the second calculation module 203 is used to construct a voltage characteristic database according to the reference charging voltage curve of each SOC interval, use the voltage characteristic database to query the reference charging voltage curve of each SOC interval, and calculate the actual capacity of the battery cell according to the actual charging voltage curve of each SOC interval and the reference charging voltage curve
  • a charging test matrix is constructed from two dimensions of temperature and charging current, charging voltage curves under different charging temperatures and charging rates are obtained, and the charging voltage curves are interpolated to obtain a reference charging voltage curve.
  • the actual capacity of the battery cell is calculated based on the reference charging voltage curve based on the actual charging voltage curve of the battery cell in each SOC interval, and the calculation method is deployed on a server. It is not affected by the battery system and can accurately calculate the capacity of each battery cell in the battery pack.
  • FIG8 is a schematic diagram of the structure of a server provided in an embodiment of the present application.
  • the server may include:
  • a memory 801 a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .
  • the server also includes:
  • the communication interface 803 is used for communication between the memory 801 and the processor 802 .
  • the memory 801 is used to store computer programs that can be executed on the processor 802 .
  • Memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.
  • RAM Random Access Memory
  • the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other.
  • the bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component) bus or an EISA (Extended Industry Standard Architecture) bus.
  • the bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in FIG8, but it does not mean that there is only one bus or one type of bus.
  • the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.
  • Processor 802 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.
  • CPU Central Processing Unit
  • ASIC Application Specific Integrated Circuit
  • An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned method for estimating the capacity of a battery cell when executed by a processor.
  • first and second are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as “first” or “second” may explicitly or implicitly include at least one of the features.
  • N means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
  • Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.
  • the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof.
  • the N steps or methods can be implemented by software or software stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

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Abstract

一种电芯容量的估计方法、装置、服务器及存储介质，其中，方法包括：获取电芯在一个或多个荷电状态SOC区间内的实际充电数据（S101）；根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，并以每个SOC区间为索引，查询预先建立的电压特征数据库，输出每个SOC区间的参考充电电压曲线（S102）；根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量（S103）。由此，解决了相关技术中计算电芯容量的方法受到电池体系的影响，计算准确度较低等问题。

Description

电芯容量的估计方法、装置、服务器及存储介质
相关申请的交叉引用
本申请基于申请号为号202310640090.X，申请日为2023年05月31日申请的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及电池管理系统技术领域，特别涉及一种电芯容量的估计方法、装置、服务器及存储介质。
背景技术
电池包一般都含有数十个甚至数百只电芯，电池包的容量是由各个电芯的容量决定的，准确计算出每个电芯的容量不但可以获取每个电芯在使用过程中的容量衰退情况，还可以依据实时计算得到的电芯容量计算电池包的当前容量，并进一步准确计算电池的SOH(State of Health，健康状态)。
由于电池包在充/放电时，通过电芯的最高电压与最低低电压控制的，因此当电池包中各个电芯的容量和SOC(State of charge，荷电状态)不一致时，无法通过简单充放电的方式获取每一个电芯的容量。
相关技术中采用恒流充电(放电)时电压与容量的对应关系ΔV～ΔQ来计算电芯总的容量。但是这种方法对于NMC(三元材料)等电池体系是有效的，对于磷酸铁锂(LiFePO4，LFP)电池体系并不适用。LFP电池体系的电压曲线十分平坦，这就使得电压平台区ΔV～ΔQ的关系异常敏感，电压的微小变化会造成容量的显著变化，使得容量的估算误差很大。
发明内容
本申请提供一种电芯容量的估计方法、装置、服务器及存储介质，以解决相关技术中计算电芯容量的方法受到电池体系的影响，计算准确度较低等问题。
本申请第一方面实施例提供一种电芯容量的估计方法，所述方法应用于服务器，其中，所述方法包括以下步骤：获取电芯在一个或多个荷电状态SOC区间内的实际充电数据；根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，并以所述每个SOC区间为索引，查询预先建立的电压特征数据库，输出所述每个SOC区间的参考充电电压曲线；根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
根据上述技术手段，本申请实施例可以通过对比电芯的实际充电电压曲线和对应参考充电电压曲线准确计算电芯的实际容量，由于利用了预先标定的标准的参考充电电压曲线，因此可以根据电压曲线准确对比出电芯的实际容量，避免电池体系对于估算结果的影响， 有效适用于多个电池体系，同时本申请实施例的估算方式可以部署于服务器上，利用服务器的计算资源提升计算的效率。
可选地，所述根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量，包括：根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的均方根误差，并根据所述均方根误差确定目标函数；通过最小二乘法对所述目标函数寻找最优解，得到所述电芯的实际容量。
根据上述技术手段，本申请实施例可以通过将实际充电电压曲线和参考充电电压曲线的计算均方根误差并利用其确定目标函数，通过最小二乘法寻优计算电芯的实际容量。
可选地，所述根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的均方根误差，包括：将参考电芯的总充电时间划分为不同的充电阶段；对每个充电阶段对应的SOC区间添加区间标识，根据所述区间标识建立SOC区间、SOC和容量之间的数学表达式；利用所述数学表达式提取所述实际充电电压曲线和对应参考充电电压曲线，根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算所述电芯的均方根误差。
根据上述技术手段，本申请实施例可以将参考电芯的总充电时间划分为不同的充电阶段，对不同充电阶段的SOC区间设置区间标识，建立不同SOC区间的数学表达式，进一步利用数学表达式提取实际充电电压曲线和对应参考充电电压曲线，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算均方根误差。
可选地，在查询预先建立的电压特征数据库之前，还包括：构造温度与充电电流的充电测试矩阵；根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线；根据每个SOC区间和对应参考充电电压曲线构造所述电压特征数据库。
根据上述技术手段，本申请实施例可以从温度和充电电流两个维度构造充电测试矩阵，对通过充电矩阵获取得到不同充电温度、倍率下的充电电压曲线进行插值处理，得到多个参考充电电压曲线，利用其与SOC区间构造电压特征数据库，以便后续根据SOC区间查找对应的参考充电电压曲线。
可选地，所述对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：计算所有不同倍率下的充电电压曲线对应的最大充电容量；根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；根据每个网格电压值计算第一常数，根据所述第一常数获取任意充电电流下的充电电压值，基于所述任意充电电流下的充电电压值生成多个参考充电电压曲线。
根据上述技术手段，本申请实施例可以对不同充电倍率下的充电电压曲线进行插值处理，通过公式计算任意充电电流下的充电电压值对应的参考充电电压曲线。
可选地，所述对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：计算所有不同温度下的充电电压曲线对应的最大充电容量； 根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；根据每个网格电压值计算第二常数，根据所述第二常数获取任意温度条件下的电压曲线，基于所述任意温度条件下的电压曲线得到多个参考充电电压曲线。
根据上述技术手段，本申请实施例可以对不同温度下的充电电压曲线进行插值处理，通过公式计算任意温度条件下的电压曲线对应的参考充电电压曲线。
可选地，所述根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，包括：在预设温度下，以第一预设电流进行恒流放电至预设截止电压后，切换第二预设电流进行恒流放电至所述预设截止电压，静置第一预设时长后，以第三预设电流进行恒流充电至所述预设截止电压，并记录当前温度和/或当前充电倍率下的充电电压曲线；静置第二预设时长后，以第四预设电流进行恒流放电至预设截止电压后，切换第五预设电流进行恒流放电至所述预设截止电压，并在静置第三预设时长后重新进行其他充电倍率和/或其他充电温度下的充电测试。
根据上述技术手段，本申请实施例可以根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，以便后续根据充电电压曲线获取参考充电电压曲线。
本申请第二方面实施例提供一种电芯容量的估计方法，所述方法应用于服务器，其中，所述方法包括以下步骤：构造温度与充电电流的充电测试矩阵；根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线；根据所述每个SOC区间的参考充电电压曲线构造电压特征数据库，利用所述电压特征数据库查询所述每个SOC区间的参考充电电压曲线，并根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
本申请第三方面实施例提供一种电芯容量的估计装置，所述装置应用于服务器，其中，所述装置包括：获取电芯在一个或多个荷电状态SOC区间内的实际充电数据；根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，并以所述每个SOC区间为索引，查询预先建立的电压特征数据库，输出所述每个SOC区间的参考充电电压曲线；根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
可选地，所述第一计算模块进一步用于：根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的均方根误差，并根据所述均方根误差确定目标函数；通过最小二乘法对所述目标函数寻找最优解，得到所述电芯的实际容量。
可选地，所述第一计算模块进一步用于：将参考电芯的总充电时间划分为不同的充电阶段；对每个充电阶段对应的SOC区间添加区间标识，根据所述区间标识建立SOC区间、SOC和容量之间的数学表达式；利用所述数学表达式提取所述实际充电电压曲线和对应参考充电电压曲线，根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算所述电芯的均方根误差。
可选地，还包括：处理模块，用于在查询预先建立的电压特征数据库之前，构造温度与充电电流的充电测试矩阵；根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线；根据每个SOC区间和对应参考充电电压曲线构造所述电压特征数据库。
可选地，所述处理模块进一步用于：计算所有不同倍率下的充电电压曲线对应的最大充电容量；根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；根据每个网格电压值计算第一常数，根据所述第一常数获取任意充电电流下的充电电压值，基于所述任意充电电流下的充电电压值生成多个参考充电电压曲线。
可选地，所述处理模块进一步用于：计算所有不同温度下的充电电压曲线对应的最大充电容量；根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；根据每个网格电压值计算第二常数，根据所述第二常数获取任意温度条件下的电压曲线，基于所述任意温度条件下的电压曲线得到多个参考充电电压曲线。
可选地，所述处理模块进一步用于：在预设温度下，以第一预设电流进行恒流放电至预设截止电压后，切换第二预设电流进行恒流放电至所述预设截止电压，静置第一预设时长后，以第三预设电流进行恒流充电至所述预设截止电压，并记录当前温度和/或当前充电倍率下的充电电压曲线；静置第二预设时长后，以第四预设电流进行恒流放电至预设截止电压后，切换第五预设电流进行恒流放电至所述预设截止电压，并在静置第三预设时长后重新进行其他充电倍率和/或其他充电温度下的充电测试。
本申请第四方面实施例提供一种电芯容量的估计装置，所述装置应用于服务器，其中，所述装置包括：构造模块，用于构造温度与充电电流的充电测试矩阵；插值模块，用于根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线；第二计算模块，用于根据所述每个SOC区间的参考充电电压曲线构造电压特征数据库，利用所述电压特征数据库查询所述每个SOC区间的参考充电电压曲线，并根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
本申请第五方面实施例提供一种服务器，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序，以实现如上述实施例所述的电芯容量的估计方法。
本申请第六方面实施例提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行，以用于实现如上述实施例所述的电芯容量的估计方法。
由此，本申请至少具有如下有益效果：
(1)本申请实施例可以通过对比电芯的实际充电电压曲线和对应参考充电电压曲线准确计算电芯的实际容量，由于利用了预先标定的标准的参考充电电压曲线，因此可以根据 电压曲线准确对比出电芯的实际容量，避免电池体系对于估算结果的影响，有效适用于多个电池体系，同时本申请实施例的估算方式可以部署于服务器上，利用服务器的计算资源提升计算的效率。
(2)本申请实施例可以通过将实际充电电压曲线和参考充电电压曲线的计算均方根误差并利用其确定目标函数，通过最小二乘法寻优计算电芯的实际容量。
(3)本申请实施例可以将参考电芯的总充电时间划分为不同的充电阶段，对不同充电阶段的SOC区间设置区间标识，建立不同SOC区间的数学表达式，进一步利用数学表达式提取实际充电电压曲线和对应参考充电电压曲线，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算均方根误差。
(4)本申请实施例可以从温度和充电电流两个维度构造充电测试矩阵，对通过充电矩阵获取得到不同充电温度、倍率下的充电电压曲线进行插值处理，得到多个参考充电电压曲线，利用其与SOC区间构造电压特征数据库，以便后续根据SOC区间查找对应的参考充电电压曲线。
(5)本申请实施例可以对不同充电倍率下的充电电压曲线进行插值处理，通过公式计算任意充电电流下的充电电压值对应的参考充电电压曲线。
(6)本申请实施例可以对不同温度下的充电电压曲线进行插值处理，通过公式计算任意温度条件下的电压曲线对应的参考充电电压曲线。
(7)本申请实施例可以根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，以便后续根据充电电压曲线获取参考充电电压曲线。
由此，解决了相关技术中计算电芯容量的方法受到电池体系的影响，计算准确度较低等技术问题。
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为根据本申请实施例提供的一种电芯容量的估计方法的流程图；
图2为根据本申请实施例提供的充电区间划分和区间标识的示意图；
图3为根据本申请一个实施例提供的电芯容量的估计方法的流程图；
图4为根据本申请实施例提供的LFP电池包中任意电芯充电区间划分和区间标识的示意图；
图5为根据本申请另一个实施例提供的电芯容量的估计方法的流程图；
图6为根据本申请实施例提供的电芯容量的估计装置的示例图；
图7为根据本申请另一个实施例提供的电芯容量的估计装置的示例图；
图8为根据本申请实施例提供的服务器的结构示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
下面参考附图描述本申请实施例的电芯容量的估计方法、装置、服务器及存储介质。针对上述背景技术中提到的相关技术中采用恒流充电(放电)时电压与容量的对应关系ΔV～ΔQ来计算电芯总的容量，但是这种方法对于NMC等电池体系是有效的，对于LFP电池体系并不适用，容量的估算误差很大的问题，本申请提供了一种电芯容量的估计方法，在该方法中，通过对比电芯的实际充电电压曲线和对应参考充电电压曲线准确计算电芯的实际容量，由于利用了预先标定的标准的参考充电电压曲线，因此可以根据电压曲线准确对比出电芯的实际容量，避免电池体系对于估算结果的影响，有效适用于多个电池体系。由此，解决了相关技术中计算电芯容量的方法受到电池体系的影响，计算准确度较低等问题。
具体而言，图1为本申请实施例所提供的一种电芯容量的估计方法的流程示意图。
如图1所示，该电芯容量的估计方法，应用与服务器，包括以下步骤：
在步骤S101中，获取电芯在一个或多个荷电状态SOC区间内的实际充电数据。
其中，电芯可以为任意电芯，比如可以为LFP电池包中某个电芯、镍氢电池电芯、锂电池电芯等。
在步骤S102中，根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，并以每个SOC区间为索引，查询预先建立的电压特征数据库，输出每个SOC区间的参考充电电压曲线。
其中，电压特征数据库存储了SOC区间与参考充电电压曲线(即为标准充电电压曲线)的关系，具体建立方法在下述实施例中阐述，此处不再赘述。
可以理解的是，本申请实施例可以根据电芯在每个SOC区间的实际充电数据生成对应SOC区间的实际充电电压曲线，在预先建立的电压特征数据库查询每个SOC区间对应的参考充电电压曲线，以及后续根据实际充电电压曲线和参考充电电压曲线计算所需要的数值。
在本申请实施例中，在查询预先建立的电压特征数据库之前，还包括：构造温度与充电电流的充电测试矩阵；根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线；根据每个SOC区间和对应参考充电电压曲线构造电压特征数据库。
可以理解的是，本申请实施例可以从温度、充电电流两个维度构造充电测试矩阵，如表1所示，然后根据充电测试矩阵获取不同的温度、充电倍率下的充电电压曲线，进行插值处理得到参考充电电压曲线，最后根据每个SOC区间和对应参考充电电压曲线构造电压特征数据库。其中，表1为充电测试矩阵表。
表1
进一步地，在本申请实施例中，根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，包括：在预设温度下，以第一预设电流进行恒流放电至预设截止电压后，切换第二预设电流进行恒流放电至预设截止电压，静置第一预设时长后，以第三预设电流进行恒流充电至预设截止电压，并记录当前温度和/或当前充电倍率下的充电电压曲线；静置第二预设时长后，以第四预设电流进行恒流放电至预设截止电压后，切换第五预设电流进行恒流放电至预设截止电压，并在静置第三预设时长后重新进行其他充电倍率和/或其他充电温度下的充电测试。
其中，预设温度可以依据具体情况进行设定，为恒温条件下的充放电过程，第一预设电流、第二预设电流、第三预设电流、第四预设电流和第五预设电流可以依据具体情况进行设定，第一预设时长、第二预设时长和第三预设时长可以依据具体情况进行设定，对此不做限定。
以充电温度为T1、充电倍率为I15为例，获取参考充电电压曲线的方法如下：
1、以0.3C进行恒流放电截止到预设截止电压，切换至0.05C继续恒流放电至预设截止电压，静置0.5小时，以I15恒流充电至截止电压，记录电压曲线；
2、静置0.5小时，以0.3C进行恒流放电至截止电压，切换0.05C继续恒流放电至截止电压，静置0.5小时。完成I15充电后，重新开始其他倍率充电测试。
在本申请实施例中，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：计算所有不同倍率下的充电电压曲线对应的最大充电容量；根据最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据步长将容量区间划分为多个网格；根据每个网格电压值计算第一常数，根据第一常数获取任意充电电流下的充电电压值，基于任意充电电流下的充电电压值生成多个参考充电电压曲线。
具体而言，本申请实施例中在获取不同温度、充电倍率下的电压曲线后，要进行插值处理。以T1温度下不同倍率下的充电电压曲线为例，插值方法如下：
计算所有不同倍率下的充电电压曲线对应的最大充电容量Qmax，将所有充电曲线对应的容量区间以Qmax/100为步长，划分为100个网格，在每一个网格中，均满足下述公式：
AB＝V，(1)
其中，A＝(kb)，B＝(I 1)T，V为网格中的电压值，解上述线性方程k、b的值，基于k、b可以获得任意充电电流下的充电电压值。
在本申请实施例中，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：计算所有不同温度下的充电电压曲线对应的最大充电容量；根据最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据步长将容量区间划分为多个网格；根据每个网格电压值计算第二常数，根据第二常数获取任意温度条件下的电压曲线，基于任意温度条件下的电压曲线得到多个参考充电电压曲线。
具体而言，以I11充电倍率下的充电电压曲线为例，任意温度条件下的多个参考充电电压曲线通过以下方法获得：
计算所有不同温度下的充电电压曲线对应的最大充电容量Qmax，将所有充电曲线对应的容量区间以Qmax/100为步长，划分为100个网格，在每一个网格中，均满足以下公式：
V＝aec/T，(2)
解方程(2)求得a、c的值，通过a、c的值可以获取任意温度条件下的电压曲线。
在步骤S103中，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量。
可以理解的是，本申请实施例通过在预先建立的电压特征数据库中，查询到每个SOC区间对应的参考充电电压曲线，利用实际充电电压曲线和参考充电电压曲线计算电芯的实际容量，具体计算方法如下阐述。
在本申请实施例中，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量，包括：根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的均方根误差，并根据均方根误差确定目标函数；通过最小二乘法对目标函数寻找最优解，得到电芯的实际容量。
其中，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的均方根误差，包括：将参考电芯的总充电时间划分为不同的充电阶段；对每个充电阶段对应的SOC区间添加区间标识，根据区间表示区间标识建立SOC区间、SOC和容量之间的数学表达式；利用数学表达式提取实际充电电压曲线和对应参考充电电压曲线，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算电芯的均方根误差。
可以理解的是，本申请实施例可以根据参考电芯实际充电电流的数值变化情况，将总充电时间划分为不同的充电阶段，将电芯每个充电阶段相对应的SOC区间进行标识，根据区间标识建立SOC区间、SOC和容量的数学表达式，根据数学表达式提取电芯在SOC区间对应的实际充电电压曲线和对应参考充电电压曲线的数学表达式计算电芯的均方根误差，通过均方根误差确定目标函数，利用最小二乘法对目标函数寻找最优解，得到电芯的实际容量。
具体而言，本申请实施例将整个充电过程划分为若干个区间，其中t1在第一阶段充电阶段，其对应的充电SOC区间记为θ1；t2位于第二充电阶段，且t2时刻充电电压已趋于平稳；t3为第二阶段充电过程即将结束的时刻，t2至t3时刻对应的充电SOC区间记为θ2；t4位于第三阶充电阶段，且t4时刻充电电压曲线已趋于平稳；t5为第三阶充电过程即将结束的时 刻，t4至t5时刻对应的充电SOC区间记为θ3；其他阶充电过程中的时间区间划分以及对应的SOC区间划划分与此类似，分别记为t6、t7、θ4；t8、t9、θ5……，具体如图2所示。
建立不同SOC充电区间θ1，θ2，θ3，θ4…的区间表达式，编号为i的电芯其充电起始时刻的SOC记为容量记为则不同SOC区间的表达式分别为：




……
根据区间标识和区间表达式提取总充电电压曲线中不同SOC区间对应的实际充电电压曲线，如图2所示，将不同充电阶段对应的实际充电电压曲线提取出来，分别记为
进一步地，在获取实际充电电压曲线后，根据实际充电过程中不同充电阶段的温度、电流值，查询电压特征数据库中的参考充电电压曲线(V(I,T,x))，计算每个电芯实际充电电压曲线与参考充电电压曲线的均方根误差，并基于最小二乘法计算每个电芯的实际容量计算公式如下：
其中，K1、K2、K3、K4……为每个充电阶段寻优时的权重。
下面通过一个具体实施例来阐述本申请实施例的电芯SOC的估计方法，如图3所示，具体步骤如下：
S1：构造LFP电芯充电电压～电流～温度～SOC的特征数据库V＝f(I，T，x)，即构造LFP电芯标准充电电压曲线(参考充电电压曲线)特征数据库；
1、构造LFP充电测试矩阵
根据某LFP电芯的性能参数表，从温度、充电电流两个维度构造充电测试矩阵，如表2所示。其中，表2为充电测试矩阵的示例表。
表2
2、获取标准充电电压曲线
以充电温度为25℃、充电倍率为1C为例，获取参考充电电压曲线的方法如下：
A、以0.3C进行恒流放电截止到预设截止电压，切换至0.05C继续恒流放电至预设截止电压，静置0.5小时，以1C恒流充电至截止电压，记录电压曲线；
B、静置0.5小时，以0.3C进行恒流放电至截止电压，切换0.05C继续恒流放电至截止电压，静置0.5小时。
C、完成1C充电后，重新开始其他倍率充电测试。
3、构造特征数据库
获取不同温度、充电倍率下的电压曲线后，要进行插值处理，构造电压特征数据库。以25℃不同倍率下的充电电压曲线为例，插值方法如下：
计算所有不同倍率下的充电电压曲线对应的最大充电容量Qmax＝100Ah，将所有充电曲线对应的容量区间以1Ah为步长，划分为100个网格，在每一个网格中，均满足公式(1)，解线性方程(1)，获取k、b的值，基于k、b可以获得任意充电电流下的充电电压值。
以0.1C充电倍率下的充电电压曲线为例，温度维度的特征数据库可以通过以下方法获取：
计算所有不同温度下的充电电压曲线对应的最大充电容量Qmax＝98Ah，将所有充电曲线对应的容量区间以0.98Ah为步长，划分为100个网格，在每一个网格中，均满足公式(2)，解方程(2)求得a、c的值，通过a、c的值可以获取任意温度条件下的电压曲线。
S2、根据电池实际充电电流的数值变化情况，将总的充电时间划分为不同的充电阶段t1，t2，t3，t4…，将各电芯不同充电阶段相对应的SOC区间标记为
图4为某型号LFP电池包中任意电芯i的实际充电曲线，将整个充电过程划分为7个SOC区间，其中，对应的充电时间为[0t1]，对应的充电时间为[t2t3]，对应的充电时间为[t4t5]，对应的充电时间为[t6t7]，对应的充电时间为[t8t9]，对应的充电时间为[t10t11]，对应的充电时间为[t12t13]，其中，t1～t13的值见表3，单位为小时。其中，表3为不同充电过程划分对应的时间。
表3
S3、建立不同SOC区间的区间表达式；
所选取的LFP电芯温度标准容量为90Ah，编号为i的电芯其充电起始时刻)的SOC(即初始SOC)记为容量记为则不同SOC区间的表达式分别为：






S4、提取各个电芯(i)在不同充电SOC区间对应的实际充电电压曲线。
如图4所示，将不同充电阶段对应的实际充电电压曲线提取出来，分别记为
S5、通过特征数据库查询标准电压曲线，并以与标准电压曲线的均方根误差为目标函数，通过最小二乘法计算每个电芯的和初始。
根据实际充电过程中不同充电阶段的温度、电流值，查询电压特征数据库中的参考充电电压曲线(V(I,T,x))，计算每个电芯实际充电电压曲线与参考充电电压曲线的均方根误差，并基于最小二乘法计算每个电芯的实际容量计算公式如下：
其中，K1、K2、K3、K4、K5、K6、K7为每个充电阶段寻优时的权重。计算每个电芯的实际容量如表4所示。其中，表4为LFP电池包中各个电芯的容量。
表4

根据本申请实施例提出的电芯容量的估计方法，可以通过对比电芯的实际充电电压曲线和对应参考充电电压曲线准确计算电芯的实际容量，由于利用了预先标定的标准的参考充电电压曲线，因此可以根据电压曲线准确对比出电芯的实际容量，避免电池体系对于估算结果的影响，有效适用于多个电池体系，同时本申请实施例的估算方式可以部署于服务器上，利用服务器的计算资源提升计算的效率；可以通过将实际充电电压曲线和参考充电电压曲线的计算均方根误差并利用其确定目标函数，通过最小二乘法寻优计算电芯的实际容量；可可以将参考电芯的总充电时间划分为不同的充电阶段，对不同充电阶段的SOC区间设置区间标识，建立不同SOC区间的数学表达式，进一步利用数学表达式提取实际充电电压曲线和对应参考充电电压曲线，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算均方根误差；可以从温度和充电电流两个维度构造充电测试矩阵，对通过充电矩阵获取得到不同充电温度、倍率下的充电电压曲线进行插值处理，得 到多个参考充电电压曲线，利用其与SOC区间构造电压特征数据库，以便后续根据SOC区间查找对应的参考充电电压曲线；可以对不同充电倍率下的充电电压曲线进行插值处理，通过公式计算任意充电电流下的充电电压值对应的参考充电电压曲线；可以对不同温度下的充电电压曲线进行插值处理，通过公式计算任意温度条件下的电压曲线对应的参考充电电压曲线。
图5为本申请实施例的另一种电芯容量的估计方法的流程图。
如图5所示，该电芯容量的估计方法，应用于服务器，包括以下步骤：
在步骤S201中，构造温度与充电电流的充电测试矩阵。
其中，充电测试矩阵的构造方法在上述实施例中已经阐述，此处不再赘述。
在步骤S202中，根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线。
可以理解的是，本申请实施例可以根据充电测试矩阵获取不同充电温度、充电倍率下的充电电压曲线，然后进行插值处理得到每个SOC区间的参考充电电压曲线，其中，插值处理的方法在上述实施例中已经阐述，此处不再赘述。
在步骤S203中，根据每个SOC区间的参考充电电压曲线构造电压特征数据库，利用电压特征数据库查询每个SOC区间的参考充电电压曲线，并根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量。
其中，电压特征数据库存储了SOC区间和参考充电电压曲线之间的关系，利用电压特征数据库即可查询SOC区间对应的参考充电电压曲线。
可以理解的是，本申请实施例可以利用电压特征数据库查询每个SOC区间的参考充电电压曲线，基于每个SOC区间的实际充电电压曲线和对应参考充电电压曲线计算电芯的实际容量以及初始SOC，利用实际容量和初始SOC计算电芯在充电过程中对应的实际SOC，其中，具体计算方法在上述实施例中已经阐述，此处不再赘述。
根据本申请实施例提出的电芯容量的估计方法，通过从温度与充电电流两个维度构建充电测试矩阵，获取不同充电温度、充电倍率下的充电电压曲线，对充电电压曲线进行插值处理，得到参考充电电压曲线，根据电芯在每个SOC区间的实际充电电压曲线参考充电电压曲线计算电芯的实际容量，并且将该计算方法部署于服务器上，不受电池体系的影响，可以准确地计算电池包内每个电芯的容量。
其次参照附图描述根据本申请实施例提出的电芯容量的估计装置。
图6是本申请实施例的电芯容量的估计装置的方框示意图。
如图6所示，该电芯容量的估计装置10包括：获取模块101、输出模块102和第一计算模块103。
其中，获取模块101用于获取电芯在一个或多个荷电状态SOC区间内的实际充电数据；输出模块102用于根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电 压曲线，根据实际充电数据生成SOC区间对应的实际充电电压曲线，并以SOC区间每个SOC区间为索引，查询预先建立的电压特征数据库，输出每个SOC区间SOC区间的参考充电电压曲线；第一计算模块103用于根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量。
在本申请实施例中，第一计算模块103进一步用于：根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的均方根误差，并根据均方根误差确定目标函数；通过最小二乘法对目标函数寻找最优解，得到电芯的实际容量。
在本申请实施例中，第一计算模块103进一步用于：根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的均方根误差，并根据均方根误差确定目标函数；通过最小二乘法对目标函数寻找最优解，得到电芯的实际容量。
在本申请实施例中，本申请的装置10还包括：处理模块。
其中，处理模块用于在查询预先建立的电压特征数据库之前，构造温度与充电电流的充电测试矩阵；根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线；根据每个SOC区间和对应参考充电电压曲线构造电压特征数据库。
在本申请实施例中，处理模块进一步用于：计算所有不同倍率下的充电电压曲线对应的最大充电容量；根据最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据步长将容量区间划分为多个网格；根据每个网格电压值计算第一常数，根据第一常数获取任意充电电流下的充电电压值，基于任意充电电流下的充电电压值生成多个参考充电电压曲线。
在本申请实施例中，处理模块进一步用于：计算所有不同温度下的充电电压曲线对应的最大充电容量；根据最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据步长将容量区间划分为多个网格；根据每个网格电压值计算第二常数，根据第二常数获取任意温度条件下的电压曲线，基于任意温度条件下的电压曲线得到多个参考充电电压曲线。
在本申请实施例中，处理模块进一步用于：在预设温度下，以第一预设电流进行恒流放电至预设截止电压后，切换第二预设电流进行恒流放电至预设截止电压，静置第一预设时长后，以第三预设电流进行恒流充电至预设截止电压，并记录当前温度和/或当前充电倍率下的充电电压曲线；静置第二预设时长后，以第四预设电流进行恒流放电至预设截止电压后，切换第五预设电流进行恒流放电至预设截止电压，并在静置第三预设时长后重新进行其他充电倍率和/或其他充电温度下的充电测试。
需要说明的是，前述对电芯容量的估计方法实施例的解释说明也适用于该实施例的电芯容量的估计装置，此处不再赘述。
根据本申请实施例提出的电芯容量的估计装置，可以通过对比电芯的实际充电电压曲线和对应参考充电电压曲线准确计算电芯的实际容量，由于利用了预先标定的标准的参考充电电压曲线，因此可以根据电压曲线准确对比出电芯的实际容量，避免电池体系对于估算结果的影响，有效适用于多个电池体系，同时本申请实施例的估算方式可以部署于服务 器上，利用服务器的计算资源提升计算的效率；可可以通过将实际充电电压曲线和参考充电电压曲线的计算均方根误差并利用其确定目标函数，通过最小二乘法寻优计算电芯的实际容量；可以将参考电芯的总充电时间划分为不同的充电阶段，对不同充电阶段的SOC区间设置区间标识，建立不同SOC区间的数学表达式，进一步利用数学表达式提取实际充电电压曲线和对应参考充电电压曲线，根据每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算均方根误差；可以从温度和充电电流两个维度构造充电测试矩阵，对通过充电矩阵获取得到不同充电温度、倍率下的充电电压曲线进行插值处理，得到多个参考充电电压曲线，利用其与SOC区间构造电压特征数据库，以便后续根据SOC区间查找对应的参考充电电压曲线；可以对不同充电倍率下的充电电压曲线进行插值处理，通过公式计算任意充电电流下的充电电压值对应的参考充电电压曲线；可以对不同温度下的充电电压曲线进行插值处理，通过公式计算任意温度条件下的电压曲线对应的参考充电电压曲线。
图7是本申请另一个实施例的电芯容量的估计装置的方框示意图。
如图7所示，该电芯容量的估计装置20，应用于服务器，包括：构造模块201、插值模块202和第二计算模块203。
其中，构造模块201用于构造温度与充电电流的充电测试矩阵；插值模块202用于根据充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线；第二计算模块203用于根据每个SOC区间的参考充电电压曲线构造电压特征数据库，利用电压特征数据库查询每个SOC区间的参考充电电压曲线，并根据每个SOC区间的实际充电电压曲线和参考充电电压曲线计算电芯的实际容量
需要说明的是，前述对电芯容量的估计方法实施例的解释说明也适用于该实施例的电芯容量的估计装置，此处不再赘述。
需要说明的是，前述对电芯容量的估计方法实施例的解释说明也适用于该实施例的电芯容量的估计装置，此处不再赘述。
根据本申请实施例提出的电芯容量的估计装置，通过从温度与充电电流两个维度构建充电测试矩阵，获取不同充电温度、充电倍率下的充电电压曲线，对充电电压曲线进行插值处理，得到参考充电电压曲线，根据电芯在每个SOC区间的实际充电电压曲线参考充电电压曲线计算电芯的实际容量，并且将该计算方法部署于服务器上，不受电池体系的影响，可以准确地计算电池包内每个电芯的容量。
图8为本申请实施例提供的服务器的结构示意图。该服务器可以包括：
存储器801、处理器802及存储在存储器801上并可在处理器802上运行的计算机程序。
处理器802执行程序时实现上述实施例中提供的电芯容量的估计方法。
进一步地，服务器还包括：
通信接口803，用于存储器801和处理器802之间的通信。
存储器801，用于存放可在处理器802上运行的计算机程序。
存储器801可能包含高速RAM(Random Access Memory，随机存取存储器)存储器，也可能还包括非易失性存储器，例如至少一个磁盘存储器。
如果存储器801、处理器802和通信接口803独立实现，则通信接口803、存储器801和处理器802可以通过总线相互连接并完成相互间的通信。总线可以是ISA(Industry Standard Architecture，工业标准体系结构)总线、PCI(Peripheral Component，外部设备互连)总线或EISA(Extended Industry Standard Architecture，扩展工业标准体系结构)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示，图8中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
可选的，在具体实现上，如果存储器801、处理器802及通信接口803，集成在一块芯片上实现，则存储器801、处理器802及通信接口803可以通过内部接口完成相互间的通信。
处理器802可能是一个CPU(Central Processing Unit，中央处理器)，或者是ASIC(Application Specific Integrated Circuit，特定集成电路)，或者是被配置成实施本申请实施例的一个或多个集成电路。
本申请实施例还提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如上的电芯容量的估计方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不是必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或N个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“N个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或N个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，N个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或 固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列，现场可编程门阵列等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。

Claims (12)

  1. 一种电芯容量的估计方法，其特征在于，所述方法应用于服务器，其中，所述方法包括以下步骤：
    获取电芯在一个或多个荷电状态SOC区间内的实际充电数据；
    根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，并以所述每个SOC区间为索引，查询预先建立的电压特征数据库，输出所述每个SOC区间的参考充电电压曲线；
    根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
  2. 根据权利要求1所述的电芯容量的估计方法，其特征在于，所述根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量，包括：
    根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的均方根误差，并根据所述均方根误差确定目标函数；
    通过最小二乘法对所述目标函数寻找最优解，得到所述电芯的实际容量。
  3. 根据权利要求2所述的电芯容量的估计方法，其特征在于，所述根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的均方根误差，包括：
    将参考电芯的总充电时间划分为不同的充电阶段；
    对每个充电阶段对应的SOC区间添加区间标识，根据所述区间标识建立SOC区间、SOC和容量之间的数学表达式；
    利用所述数学表达式提取所述实际充电电压曲线和对应参考充电电压曲线，根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线各自的数学表达式计算所述电芯的均方根误差。
  4. 根据权利要求1所述的电芯容量的估计方法，其特征在于，在查询预先建立的电压特征数据库之前，还包括：
    构造温度与充电电流的充电测试矩阵；
    根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线；
    根据每个SOC区间和对应参考充电电压曲线构造所述电压特征数据库。
  5. 根据权利要求4所述的电芯容量的估计方法，其特征在于，所述对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：
    计算所有不同倍率下的充电电压曲线对应的最大充电容量；
    根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；
    根据每个网格电压值计算第一常数，根据所述第一常数获取任意充电电流下的充电电 压值，基于所述任意充电电流下的充电电压值生成多个参考充电电压曲线。
  6. 根据权利要求4所述的电芯容量的估计方法，其特征在于，所述对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到多个参考充电电压曲线，包括：
    计算所有不同温度下的充电电压曲线对应的最大充电容量；
    根据所述最大充电容量确定每个充电电压曲线对应的容量区间的步长，根据所述步长将所述容量区间划分为多个网格；
    根据每个网格电压值计算第二常数，根据所述第二常数获取任意温度条件下的电压曲线，基于所述任意温度条件下的电压曲线得到多个参考充电电压曲线。
  7. 根据权利要求4所述的电芯容量的估计方法，其特征在于，所述根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，包括：
    在预设温度下，以第一预设电流进行恒流放电至预设截止电压后，切换第二预设电流进行恒流放电至所述预设截止电压，静置第一预设时长后，以第三预设电流进行恒流充电至所述预设截止电压，并记录当前温度和/或当前充电倍率下的充电电压曲线；
    静置第二预设时长后，以第四预设电流进行恒流放电至预设截止电压后，切换第五预设电流进行恒流放电至所述预设截止电压，并在静置第三预设时长后重新进行其他充电倍率和/或其他充电温度下的充电测试。
  8. 一种电芯容量的估计方法，其特征在于，所述方法应用于服务器，其中，所述方法包括以下步骤：
    构造温度与充电电流的充电测试矩阵；
    根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线；
    根据所述每个SOC区间的参考充电电压曲线构造电压特征数据库，利用所述电压特征数据库查询所述每个SOC区间的参考充电电压曲线，并根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
  9. 一种电芯容量的估计装置，其特征在于，所述装置应用于服务器，其中，所述装置包括：
    获取模块，用于获取电芯在一个或多个荷电状态SOC区间内的实际充电数据；
    输出模块，用于根据每个SOC区间内的实际充电数据生成对应SOC区间的实际充电电压曲线，根据所述实际充电数据生成所述SOC区间对应的实际充电电压曲线，并以所述SOC区间所述每个SOC区间为索引，查询预先建立的电压特征数据库，输出所述每个SOC区间的参考充电电压曲线；
    第一计算模块，用于根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
  10. 一种电芯容量的估计装置，其特征在于，所述装置应用于服务器，其中，所述装置包括：
    构造模块，用于构造温度与充电电流的充电测试矩阵；
    插值模块，用于根据所述充电测试矩阵获取不同充电温度和/或不同充电倍率下的充电电压曲线，对所述不同温度和/或不同充电倍率下的充电电压曲线进行插值处理得到每个SOC区间的参考充电电压曲线；
    第二计算模块，用于根据所述每个SOC区间的参考充电电压曲线构造电压特征数据库，利用所述电压特征数据库查询所述每个SOC区间的参考充电电压曲线，并根据所述每个SOC区间的实际充电电压曲线和参考充电电压曲线计算所述电芯的实际容量。
  11. 一种服务器，其特征在于，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序，以实现如权利要求1-8任一项所述的电芯容量的估计方法。
  12. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行，以用于实现如权利要求1-8任一项所述的电芯容量的估计方法。
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