WO2020238583A1 - Soc修正方法和装置、电池管理系统和存储介质 - Google Patents

Soc修正方法和装置、电池管理系统和存储介质 Download PDF

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WO2020238583A1
WO2020238583A1 PCT/CN2020/089181 CN2020089181W WO2020238583A1 WO 2020238583 A1 WO2020238583 A1 WO 2020238583A1 CN 2020089181 W CN2020089181 W CN 2020089181W WO 2020238583 A1 WO2020238583 A1 WO 2020238583A1
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Prior art keywords
state
preset
steady
soc
voltage
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English (en)
French (fr)
Inventor
阮见
李世超
杜明树
汤慎之
卢艳华
张伟
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Contemporary Amperex Technology Co Ltd
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Contemporary Amperex Technology Co Ltd
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Priority to KR1020207034112A priority Critical patent/KR102301406B1/ko
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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]
    • G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
    • 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
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current 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]
    • G01R31/385—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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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

  • This application relates to the field of battery technology, and in particular to an SOC correction method and device, a battery management system, and a storage medium.
  • SOC State of Charge, state of charge
  • SOC estimation is one of the most important functions of the battery management system. It is used to realize the battery management system's power indicator, remaining mileage, overcharge and overdischarge protection, battery equalization, charge control, and battery health prediction.
  • the open circuit voltage method is mainly used to estimate the SOC. Specifically, the corresponding relationship between the open circuit voltage OCV and the SOC in the steady state of the cell is used to obtain the steady state battery SOC (ie, steady state SOC).
  • the purpose of this application is to provide an SOC correction method and device, a battery management system, and a storage medium that can quickly estimate the steady-state OCV through the external circuit characteristics of the battery when the battery is left for a short time, and improve the applicability of the open circuit voltage method.
  • an embodiment of the present application provides a SOC correction method, and the SOC correction method includes:
  • the preset is determined according to the cell state data The first group of undetermined parameters of the steady-state battery model
  • the steady-state OCV estimated value is calculated, and the preset corresponding relationship between steady-state OCV and SOC is used to determine the SOC correction value corresponding to the steady-state OCV estimated value to correct the current SOC.
  • an embodiment of the present application provides a SOC correction device, which includes:
  • the data collection module is used to collect battery state data that meets the preset static conditions
  • the first group of undetermined parameter determination modules is used if the length of the voltage data in the cell state data is greater than the first preset length and less than the second preset length, and the change value of the voltage data in the cell state data is greater than the preset change threshold ,
  • the first group of undetermined parameters of the preset steady-state battery model is determined according to the battery state data;
  • the second group of undetermined parameter prediction modules are used to predict the second group of undetermined parameters of the preset steady state battery model according to the first group of undetermined parameters;
  • the SOC correction module is used to calculate the steady-state OCV estimated value according to the second group of undetermined parameters, and use the preset corresponding relationship between steady-state OCV and SOC to determine the SOC correction value corresponding to the steady-state OCV estimated value, To correct the current SOC.
  • an embodiment of the present application provides a battery management system, which includes the above-mentioned SOC correction device.
  • an embodiment of the present application provides a storage medium on which a program is stored, and when the program is executed by a processor, the above SOC correction method is implemented.
  • the embodiment of the present application first determines the undetermined parameters of the steady-state battery model according to the cell state data under the preset resting conditions of the cell to characterize The trend of OCV changes with time when the steady state is reached, and then the steady-state time threshold is processed by the undetermined parameters to obtain the steady-state OCV estimated value, and then the preset steady-state OCV and SOC corresponding relationship are used to determine the corresponding steady-state OCV estimated value SOC to correct the current SOC.
  • the embodiment of the present application can use the external circuit characteristics of the battery when the battery is left for a short time to determine the first steady-state battery model used to characterize the steady-state open circuit voltage OCV over time.
  • a group of undetermined parameters is then used to estimate the steady-state OCV using the first group of undetermined parameters, thereby reducing the time required to obtain a steady-state OCV, thereby increasing the opportunity for SOC correction, and improving the applicability of the open circuit voltage method.
  • the embodiments of the present application also limit the conditions of the cell state data participating in the calculation of the steady state battery model from the perspective of whether the amount of voltage data is sufficient and whether the change value of the voltage data is credible.
  • the embodiment of the present application can predict the second set of undetermined parameters of the preset steady state battery model under the second preset length according to the first undetermined parameter, and then use the first set of The undetermined parameters estimate the steady-state OCV, thereby avoiding the problem of low accuracy of the undetermined parameters of the preset steady-state battery model caused by insufficient cell state data, and further improving the estimation accuracy and applicability of the open circuit voltage method.
  • FIG. 1 is a schematic flowchart of the SOC correction method provided by the first embodiment of this application;
  • FIG. 2 is a schematic diagram of a voltage versus time curve obtained based on a time sequence and its corresponding voltage sequence according to an embodiment of the application;
  • FIG. 3 is a schematic diagram of a voltage versus time curve obtained based on a time sequence and its corresponding voltage sequence according to another embodiment of the application;
  • FIG. 6 is a schematic flowchart of the SOC correction method provided by the fourth embodiment of this application.
  • FIG. 7 is a schematic flowchart of the SOC correction method provided by the fifth embodiment of this application.
  • FIG. 8 is a schematic structural diagram of a SOC correction device provided by an embodiment of the application.
  • the embodiment of the application provides a SOC correction method and device, a battery management system, and a storage medium.
  • a model when the battery open circuit voltage tends to a steady state can be established.
  • the external circuit characteristics estimate the steady-state open-circuit voltage, thereby reducing the time required to obtain the steady-state open-circuit voltage, and overcoming the problem of too long time required to obtain the open-circuit voltage to reach the steady state, thereby increasing the chance of SOC correction and improving the open-circuit voltage method Applicability.
  • Open circuit voltage OCV refers to the terminal voltage of the cell in the open circuit state or when the external current is close to 0.
  • Steady-state OCV refers to the terminal voltage when the cell is in an open circuit state for a long time or the external current is close to 0, and the voltage change rate is less than the preset threshold.
  • Unsteady-state OCV refers to the estimated open-circuit voltage when the cell is in an open circuit state for a short time or the external current is close to 0, and the polarization voltage compensation value is added.
  • the steady-state OCV refers to the voltage in the process that the voltage tends to the steady-state OCV when the cell is in an open circuit state or the current is close to zero after a period of current excitation.
  • FIG. 1 is a schematic flowchart of the SOC correction method provided by the first embodiment of this application. As shown in FIG. 1, the SOC correction method includes steps 101 to 104.
  • step 101 cell state data that meets a preset resting condition is collected.
  • the battery state data includes: SOH (State of Health), that is, the percentage of the battery's full charge capacity to the rated capacity, which is used to indicate the battery's ability to store charge.
  • SOH State of Health
  • the SOH of the new factory battery is 100%. With the extension of the use time, the SOH gradually decreases, and the SOH value can be considered unchanged in a short period of time.
  • the battery state data further includes: voltage, current, temperature, and so on.
  • the preset resting condition is that the current of the battery cell is less than the preset current threshold.
  • the preset current threshold can be obtained by looking up the mapping relationship between the preset SOC, temperature and the preset current threshold according to the SOC and temperature of the cell at the current moment.
  • FIG. 2 is a schematic diagram of a voltage variation curve with time obtained based on a time sequence and its corresponding voltage sequence according to an embodiment of the application.
  • the abscissa in FIG. 2 is time and the ordinate is voltage.
  • the voltage corresponding to time t1 is V1
  • the voltage corresponding to time t2 is V2
  • the voltage corresponding to time t12 is V12
  • the voltage corresponding to time t13 is V13.
  • a steady-state battery model can be used to characterize the change curve in FIG. 2.
  • V(t) is the voltage that the battery changes with time when it is in a steady state:
  • a 1 , b 1 , c 1 , c′ 1 , d 1 are the undetermined parameters of the model M1, and e is the natural base.
  • a 1 , b 1 , c 1 , d 1 are the undetermined parameters of the model M2, and e is the natural base.
  • a 1 , b 1 ,..., b n , c 1 ,...c n , d 1 ,...d n are the undetermined parameters of the model M3, and e is the natural base.
  • a 3 , b 3 , and c 3 are the model parameters to be determined, and e is the natural base.
  • steady-state battery models involved in the embodiments of the present application are not limited to the above three types, and also include simplifications and deformations of the models, which are not limited here.
  • FIG. 3 is a schematic diagram of a voltage variation curve with time obtained based on a time sequence and its corresponding voltage sequence according to another embodiment of the application.
  • Fig. 3 shows the voltage curve with time obtained based on the voltage sequence at t1-t7.
  • Fig. 2 shows the voltage curve with time obtained based on the voltage sequence at t1-t7.
  • the embodiment of the present application uses the length of the voltage data to indicate the number of collected voltage data, that is, the data amount of the voltage data participating in the calculation, and also reflects the length of the resting time.
  • the first length interval 0 to the first preset length. If the length of the collected voltage data is in the first length interval, it indicates that the amount of data is too small and the cell resting time is too short. At this time, the current SOC correction strategy is not implemented.
  • the second length interval the first preset length to the second preset length. If the length of the collected voltage data is in the second length interval, it means that the amount of data is not very sufficient and the cell resting time is not long enough. At this time, if the static OCV is directly estimated, the accuracy will be reduced.
  • the third length interval the second preset length to + ⁇ . If the length of the collected voltage data is in the third length interval, it indicates that the amount of data is sufficient and the cell resting time is long enough. At this time, the static OCV can be directly estimated with high accuracy.
  • the change value of the voltage data is greater than the preset change threshold, it indicates that the trend of the voltage change curve with time in FIG. 2 or FIG. 3 is relatively obvious, and the static OCV can be directly estimated with high accuracy.
  • the change value of the voltage data is not greater than the preset change threshold, it means that the trend of the voltage change curve with time in FIG. 2 or FIG. 3 is not obvious. At this time, if the static OCV is directly estimated, the accuracy will be reduced.
  • both the first preset length and the second preset length can be determined by the current of the battery cell. If the current of the battery cell is small, it means that the voltage slope changes less. At this time, the first preset length and the second preset length The value of can be appropriately reduced; if the cell current is small, the change in the voltage slope is small. At this time, the values of the first preset length and the second preset length must be increased appropriately to ensure sufficient data volume , To ensure the accuracy of the undetermined parameters of the preset steady-state battery model.
  • step 102 to step 104 can be performed.
  • step 102 if the length of the voltage data in the cell state data is greater than the first preset length and less than the second preset length, and the change value of the voltage data in the cell state data is greater than the preset change threshold, then The state data determines the first set of undetermined parameters of the preset steady-state battery model.
  • V(t) is the voltage that the battery changes with time when it is in a steady state:
  • a 1 , b 1 , c 1 , c′ 1 , d 1 are the undetermined parameters of the model M1, and e is the natural base.
  • a 1 , b 1 , c 1 , d 1 are the undetermined parameters of the model M2, and e is the natural base.
  • a 1 , b 1 ,..., b n , c 1 ,...c n , d 1 ,...d n are the undetermined parameters of the model M3, and e is the natural base.
  • a 3 , b 3 , and c 3 are model parameters to be determined, and e is a natural base.
  • steady-state battery models involved in the embodiments of the present application are not limited to the above three types, and also include simplifications and deformations of the models, which are not limited here.
  • the method for obtaining the first set of undetermined parameters of the steady-state battery model is described below.
  • At least part of the parameters of the first group of undetermined parameters can be determined by the mapping relationship between the preset SOH, voltage, current, temperature and the part of the parameters, and the current SOH, voltage, current, and temperature.
  • the mapping relationship between the calibrated SOH, current, temperature, and voltage under the meter line and the at least part of the parameters is obtained.
  • At least some of the parameters of the first group of undetermined parameters are obtained by fitting the voltage data up to the current moment. It can be understood that at least some of the parameters in the embodiment of the present application include some parameters and all parameters.
  • At least part of the parameters of the first group of undetermined parameters may be determined by the preset mapping relationship between SOH, voltage, current, temperature and the part of the parameters, and the current SOH, voltage, current, and temperature.
  • Fitting algorithms include but are not limited to least squares method and its variations, genetic algorithm or other parameter fitting methods, etc.
  • step 102 The determination of the preset steady-state battery model in step 102 will be described below.
  • those skilled in the art can select appropriate multiple steady-state battery models based on experience, and use the sum of the multiple steady-state battery models as the preset steady-state battery model;
  • those skilled in the art can also fit m sub-stationary battery models respectively according to the voltage data up to the current moment to obtain m fitting curves and their corresponding fitting differences.
  • the m sub-stationary-state battery models are sorted according to the fitting difference, and the sum of the first n sub-stationary-state battery models with the smallest fitting difference is used as the preset tessellation-state battery model, m ⁇ n ⁇ 1.
  • the fitting difference the more the sub-stationary-state battery model matches the actual operating condition data, that is, the more accurate the sub-stationary-state battery model is, and the higher the estimation accuracy of the SOC.
  • the fitted variance can be the variance, standard deviation, cumulative sum of absolute error values, sum of errors, absolute maximum error, etc., which are not limited here.
  • the step of calculating the undetermined parameters of the steady-state battery model can be performed in real time, that is, the undetermined parameters of the steady-state battery model are continuously updated with the extension of the battery cell standing time, or can be performed at intervals of a certain time period.
  • step 103 according to the first group of undetermined parameters, predict the second group of undetermined parameters of the preset steady-state battery model.
  • the second group of undetermined parameters of the preset stable-state battery model can be predicted according to the corresponding relationship between the first group of undetermined parameters and the preset first group of undetermined parameters and the second group of undetermined parameters based on the voltage data length.
  • one of the first pending parameters and the second set of pending parameters of M1 under the second preset length may be pre-calibrated according to the first pending parameters of M1.
  • the mapping relationship between M1 and the second group of undetermined parameters are determined.
  • step 104 the steady-state OCV estimated value is calculated according to the second set of undetermined parameters, and the preset corresponding relationship between steady-state OCV and SOC is used to determine the SOC correction value corresponding to the steady-state OCV estimated value to Correct the current SOC.
  • the steady-state time threshold Tt corresponding to the battery state data can be determined, and then the steady-state time threshold Tt can be processed by the steady-state battery model determined by the second set of undetermined parameters, that is, the steady-state time threshold Tt is substituted by The steady-state battery model determined by the second set of undetermined parameters is calculated to obtain the steady-state OCV estimated value.
  • the steady-state time threshold Tt can be obtained by looking up the mapping relationship between the pre-calibrated SOH, temperature and the steady-state time threshold according to the SOH and temperature of the cell at the current moment.
  • step 105 to step 106 can be performed, see FIG. 4.
  • step 105 if the length of the voltage data in the cell state data is greater than the second preset length, and the change value of the voltage data in the cell state data is greater than the preset change threshold, then the preset stabilization is determined according to the cell state data The first group of undetermined parameters of the state battery model.
  • step 106 the steady-state OCV estimated value is obtained according to the first set of undetermined parameters, and the preset corresponding relationship between steady-state OCV and SOC is used to determine the SOC correction value corresponding to the steady-state OCV estimated value to modify Current SOC.
  • the embodiment of the present application first determines the steady state according to the cell state data when the cell meets the preset resting condition
  • the undetermined parameters of the battery model are used to characterize the trend of OCV changes over time when the steady state is approaching, and then use the undetermined parameters to process the steady-state time threshold to obtain the steady-state OCV estimated value, and then use the preset corresponding relationship between steady-state OCV and SOC to determine
  • the SOC corresponding to the estimated steady-state OCV is used to correct the current SOC.
  • the embodiment of the present application can use the external circuit characteristics of the battery when the battery is left for a short time to determine the first steady-state battery model used to characterize the steady-state open circuit voltage OCV over time.
  • a group of undetermined parameters is then used to estimate the steady-state OCV using the first group of undetermined parameters, thereby reducing the time required to obtain a steady-state OCV, thereby increasing the opportunity for SOC correction, and improving the applicability of the open circuit voltage method.
  • the embodiments of the present application also limit the conditions of the cell state data participating in the calculation of the steady state battery model from the perspective of whether the amount of voltage data is sufficient and whether the change value of the voltage data is credible.
  • the embodiment of the present application can predict the second set of the preset steady-state battery model under the second preset length according to the first pending parameter
  • the undetermined parameters are then used to estimate the steady-state OCV using the first group of undetermined parameters, thereby avoiding the problem of low accuracy of the undetermined parameters of the preset steady-state battery model caused by insufficient cell state data, and further improving the open circuit voltage
  • the estimation accuracy and applicability of the method can be used to estimate the steady-state OCV using the first group of undetermined parameters, thereby avoiding the problem of low accuracy of the undetermined parameters of the preset steady-state battery model caused by insufficient cell state data, and further improving the open circuit voltage.
  • the embodiment of the present application may also perform step 107 for the second length interval, see FIG. 5 .
  • step 108 may also be performed, see FIG. 6.
  • step 107 if the length of the voltage data in the cell state data is greater than the first preset length and less than the second preset length, and the change value of the voltage data in the cell state data is less than the preset change threshold, then The state data determines the unsteady-state OCV estimated value, and uses the preset corresponding relationship between unsteady-state OCV and SOC to obtain the SOC correction value corresponding to the unsteady-state OCV estimated value to correct the current SOC.
  • step 108 if the length of the voltage data in the cell state data is greater than the second preset length, and the change value of the voltage data in the cell state data is less than the preset change threshold, the unsteady OCV is determined according to the cell state data
  • the estimated value uses the preset corresponding relationship between the unsteady OCV and the SOC to obtain the SOC correction value corresponding to the unsteady OCV estimated value to correct the current SOC.
  • the non-steady-state OCV estimated value is the difference between the voltage of the cell at the last moment of resting and the compensation value of the polarization voltage.
  • the mapping relationship between the current, temperature and the polarization voltage compensation value calibrated under the meter line can be checked to obtain the cell at the end of the resting The voltage at a time and the polarization voltage compensation value at that time.
  • the polarization compensation value can also be determined according to the statistical characteristics (such as root mean square value) of the voltage and temperature of the cell during this resting period, and the mapping relationship between the statistical characteristics of the table and the polarization compensation value. .
  • the embodiment of the present application adopts the unsteady state when the SOC accuracy according to the preset steady-state battery model is low due to the unreliable change value of the voltage data.
  • the state OCV estimation method further increases the chance of SOC correction and improves the applicability of the open circuit voltage method.
  • the direction of voltage rebound refers to the direction of cell voltage change when the current decreases.
  • the direction of the voltage rebound is the voltage increase, that is, the voltage curve with time during the rest period is monotonously increasing, indicating that the SOC corresponding to the unsteady OCV is the lower limit of the credible SOC.
  • the unsteady OCV The corresponding SOC is greater than the current SOC, and then the SOC corresponding to the unsteady OCV is used to correct the current SOC.
  • the direction of voltage rebound is voltage decrease, that is, the voltage curve with time during the rest period is monotonously decreasing, indicating that the SOC corresponding to the unsteady OCV is the upper limit of the credible SOC.
  • the corresponding SOC is less than the current SOC, and then the SOC corresponding to the unsteady OCV is used to correct the current SOC.
  • the average value of the current SOC and the SOC corresponding to the non-steady OCV can be calculated, and the current SOC can be corrected by using the average value.
  • FIG. 7 is a schematic flowchart of a SOC correction method provided by another embodiment of this application.
  • the SOC correction method shown in FIG. 7 includes steps 701 to 713, which are used to illustrate the SOC correction method of the embodiment of the present application.
  • step 701 it is determined whether the battery cell is in a resting condition, if yes, step 502 is executed, otherwise, step 501 is returned.
  • step 702 if the battery cell satisfies the resting condition, the voltage sequence, current sequence, temperature sequence, and time sequence of the battery cell are recorded.
  • step 703 it is determined whether the length of the voltage sequence is less than a first preset length. If yes, go back to step 702, otherwise go to step 703.
  • step 704 it is determined whether the length of the voltage sequence is greater than a first preset length and less than a second preset length. If yes, go to step 705; otherwise, go to step 712.
  • step 705 it is determined whether the variable voltage change value of the voltage sequence is greater than the preset change threshold value, if yes, step 706 is executed, otherwise, step 709 is executed.
  • step 706 the first set of pending parameters of the steady-state battery model is determined.
  • step 707 the second group of undetermined parameters of the steady-state battery model are predicted according to the first group of undetermined parameters, and the steady-state OCV is estimated according to the second group of undetermined parameters.
  • step 708 according to the estimated steady-state OCV, the mapping relationship between the steady-state OCV and the SOC is checked, and the SOC corresponding to the estimated steady-state OCV is determined to correct the current SOC.
  • step 709 the unsteady OCV is estimated.
  • step 710 according to the unsteady OCV, the mapping relationship of the unsteady OCV-SOC is looked up, and the SOC corresponding to the estimated unsteady OCV is determined.
  • step 711 the current SOC is corrected according to the SOC corresponding to the estimated unsteady OCV.
  • step 712 it is determined whether the length of the voltage sequence is greater than the second preset length, and step 705 is executed; if yes, step 713 is executed; otherwise, step 709 is executed.
  • step 713 the first group of undetermined parameters of the steady-state battery model is determined and the steady-state OCV is estimated based on the first group of undetermined parameters, and then step 708 is executed.
  • FIG. 8 is a schematic structural diagram of an SOC correction device provided by an embodiment of the application.
  • the SOC correction device includes: a data acquisition module 801, a first group of pending parameter determination module 802, and a second group of pending parameter prediction module 803 And SOC correction module 804.
  • the data collection module 801 is used to collect battery cell state data that meets a preset resting condition.
  • the first group of undetermined parameter determination module 802 is used for if the length of the voltage data in the cell state data is greater than the first preset length and less than the second preset length, and the change value of the voltage data in the cell state data is greater than the preset change threshold ,
  • the first set of undetermined parameters of the preset steady-state battery model is determined according to the battery state data.
  • the second group of undetermined parameter prediction module 803 is configured to predict the second group of undetermined parameters of the preset steady-state battery model according to the first group of undetermined parameters.
  • the SOC correction module 804 is used to calculate the steady-state OCV estimated value according to the second group of undetermined parameters, and use the preset corresponding relationship between steady-state OCV and SOC to determine the SOC correction value corresponding to the steady-state OCV estimated value, To correct the current SOC.
  • An embodiment of the present application also provides a battery management system, which includes the above-mentioned SOC correction device.
  • the embodiment of the present application also provides a storage medium on which a program is stored, and when the program is executed by a processor, the above SOC correction method is implemented.

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Abstract

一种SOC修正方法和装置、电池管理系统和存储介质。SOC修正方法包括：采集满足预设静置条件下的电芯状态数据（101）；若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数（102）；根据第一组待定参数，预测预设趋稳态电池模型的第二组待定参数（103）；根据第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC（104）。SOC修正方法能够通过电池短时间静置时的外电路特性，快速估算稳态OCV，提高开路电压法的适用性。

Description

SOC修正方法和装置、电池管理系统和存储介质
相关申请的交叉引用
本申请要求享有于2019年05月24日提交的名称为“SOC修正方法和装置、电池管理系统和存储介质”的中国专利申请第201910441422.5号的优先权，该申请的全部内容通过引用并入本文中。
技术领域
本申请涉及电池技术领域，具体涉及一种SOC修正方法和装置、电池管理系统和存储介质。
背景技术
SOC(State of Charge，荷电状态)代表电池使用一段时间或长期搁置不用后剩余容量与其完全充电状态的容量的比值，当SOC＝0时表示电池放电完全，当SOC＝1时表示电池完全充满。SOC估算是电池管理系统最重要的功能之一，用于实现电池管理系统的电量指示、剩余里程、过充过放保护、电池均衡、充电控制及电池健康状况预测。
现有技术中主要采用开路电压法进行SOC估算，具体为利用电芯稳态下开路电压OCV和SOC的对应关系，得到稳定状态的电池SOC(即稳态SOC)。
但是，稳态OCV的获得通常需要静置较长时间(数小时以上)，而实际使用工况中，电芯长时间静置的机会较少，因此，获取稳态OCV的机会极少，降低了开路电压法的适用性。
发明内容
本申请的目的是提供一种SOC修正方法和装置、电池管理系统和存储介质，能够通过电池短时间静置时的外电路特性，快速估算稳态OCV，提 高开路电压法的适用性。
第一方面，本申请实施例提供一种SOC修正方法，该SOC修正方法包括：
采集满足预设静置条件下的电芯状态数据；
若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数；
根据第一组待定参数，预测预设趋稳态电池模型的第二组待定参数；
根据第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。
第二方面，本申请实施例提供一种SOC修正装置，该装置包括：
数据采集模块，用于采集满足预设静置条件下的电芯状态数据；
第一组待定参数确定模块，用于若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数；
第二组待定参数预测模块，用于根据第一组待定参数，预测预设趋稳态电池模型的第二组待定参数；
SOC修正模块，用于根据第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。
第三方面，本申请实施例提供一种电池管理系统，该电池管理系统包括如上所述的SOC修正装置。
第四方面，本申请实施例提供一种存储介质，其上存储有程序，程序被处理器执行时实现如上所述的SOC修正方法。
为了避免SOC估算时稳态OCV需要静置较长时间的问题，本申请实施例首先根据电芯满足预设静置条件下的电芯状态数据确定出趋稳态电池模型的待定参数，以表征趋稳态时OCV随时间变化趋势，然后利用待定参 数处理稳态时间阈值得到稳态OCV预估值，接着利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC，以修正当前SOC。
与现有技术中的开路电压法相比，本申请实施例能够利用电池短时间静置时的外电路特性，确定用于表征趋稳态时开路电压OCV随时间变化的趋稳态电池模型的第一组待定参数，然后利用第一组待定参数预估稳态OCV，从而减小获取稳态OCV所需时间，进而增加了SOC的修正机会，提高了开路电压法的适用性。
此外，本申请实施例还从电压数据量是否充分，以及电压数据的变化值是否可信的角度出发，限定了参与趋稳态电池模型运算的电芯状态数据的条件。比如，为解决电压数据的长度不充分的问题，本申请实施例能够根据第一待定参数，预测第二预设长度下预设趋稳态电池模型的第二组待定参数，然后利用第一组待定参数预估稳态OCV，从而避免因电芯状态数据不充分而导致的预设趋稳态电池模型的待定参数的准确度低的问题，进一步提高了开路电压法的估算精度和适用性。
附图说明
下面将参考附图来描述本申请示例性实施例的特征、优点和技术效果，其中的附图并未按照实际的比例绘制。
图1为本申请第一实施例提供的SOC修正方法的流程示意图；
图2为本申请一实施例提供的基于时间序列及其对应的电压序列得到的电压随时间的变化曲线示意图；
图3为本申请另一实施例提供的基于时间序列及其对应的电压序列得到的电压随时间的变化曲线示意图；
图4为本申请第二实施例提供的SOC修正方法的流程示意图；
图5为本申请第三实施例提供的SOC修正方法的流程示意图；
图6为本申请第四实施例提供的SOC修正方法的流程示意图；
图7为本申请第五实施例提供的SOC修正方法的流程示意图；
图8为本申请实施例提供的SOC修正装置的结构示意图。
具体实施方式
下面将详细描述本申请的各个方面的特征和示例性实施例。在下面的详细描述中，提出了许多具体细节，以便提供对本申请的全面理解。
本申请实施例提供一种SOC修正方法和装置、电池管理系统和存储介质，采用本申请实施例中的技术方案，能够建立电池开路电压趋于稳态时的模型，通过电池短时间静置时的外电路特性估算稳态时的开路电压，从而减小获取稳态开路电压所需时间，克服获取开路电压达到稳态所需时间过长的问题，进而增加SOC的修正机会，提高开路电压法的适用性。
下面对本申请实施例涉及的几个概念进行简要说明。
开路电压OCV，指的是电芯在开路状态或外电流接近0时的端电压。
稳态OCV，指的是电芯在长时间处于开路状态或外电流接近0时，电压变化率小于预设阈值时的端电压。
非稳态OCV，指的是电芯在短时间处于开路状态或外电流接近0时，并加上极化电压补偿值后的预估开路电压。
趋稳态OCV，指的是电芯经历一段时间电流激励后，处于开路状态或者电流接近零时，电压趋向于稳态OCV过程中的电压。
图1为本申请第一实施例提供的SOC修正方法的流程示意图。如图1所示，该SOC修正方法包括步骤101至步骤104。
在步骤101中，采集满足预设静置条件下的电芯状态数据。
其中，电芯状态数据包括：SOH(State of Health)，即电池满充容量相对额定容量的百分比，用于表示电池可存储电荷的能力。新出厂电池的SOH为100％，随着使用时间的延长，SOH逐渐下降，短时间段内SOH的值可以认为不变。
在一示例中，电芯状态数据还包括：电压、电流和温度等。
在一示例中，预设静置条件为电芯的电流小于预设电流阈值。
其中，预设电流阈值可以根据当前时刻电芯的SOC和温度，查表预标定的SOC、温度与预设电流阈值的映射关系得到。
具体实施时，当电芯满足预设的静置条件后，可以记录电芯的SOH，电压序列UList＝[V1,V2,…Vn]、电流序列IList＝[I1,I2,…,In]、温度序列 TList＝[T1,T2,…Tn]和时间序列TimeList＝[t1,t2,…,tn]，并累计满足静置条件的时间Te。
图2为本申请一实施例提供的基于时间序列及其对应的电压序列得到的电压随时间的变化曲线示意图。
图2中的横坐标为时间，纵坐标为电压，t1时刻对应的电压为V1，t2时刻对应的电压为V2，t12时刻对应的电压为V12，t13时刻对应的电压为V13。
在一示例中，可以利用趋稳态电池模型表征图2中的变化曲线。
下面示出四种趋稳态电池模型：
其中，V(t)为趋稳态时电池随时间变化的电压：
模型M1：
Figure PCTCN2020089181-appb-000001
其中，a 1,b 1,c 1,c′ 1,d 1为模型M1的待定参数，e为自然底数。
模型M2：
Figure PCTCN2020089181-appb-000002
其中，a 1,b 1,c 1,d 1为模型M2的待定参数，e为自然底数。
模型M3：
Figure PCTCN2020089181-appb-000003
其中，a 1,b 1,…,b n,c 1,…c n,d 1,…d n为模型M3的待定参数，e为自然底数。
模型M4：
Figure PCTCN2020089181-appb-000004
其中，a 3,b 3,c 3为模型待定参数，e为自然底数。
需要说明的是，本申请实施例涉及的趋稳态电池模型不局限于上述三种，还包括各模型的简化及其变形，此处不进行限定。
图3为本申请另一实施例提供的基于时间序列及其对应的电压序列得到的电压随时间的变化曲线示意图。
图3与图2的不同之处在于，图3中示出基于t1-t7时刻的电压序列得到的电压随时间的变化曲线，对于图2中的数据，可能存在数据量不充分或者静置时间短的问题，因此仅基于t1-t7时刻的电压序列无法准确反映出 t7时刻之后的电压随时间的变化情况，从而降低了预设趋稳态电池模型的待定参数的准确度。
基于此，本申请实施例采用电压数据的长度表示采集到的电压数据的个数，即参与计算的电压数据的数据量，同时也反映静置时间的长短。
在本申请实施例中，为了描述电压数据量是否充分的问题，可以按照电压数据的长度划分出三个区间：
第一长度区间：0至第一预设长度。若采集到的电压数据的长度位于第一长度区间，说明数据量过少，电芯静置时间太短，此时不执行对当前SOC的修正策略。
第二长度区间：第一预设长度至第二预设长度。若采集到的电压数据的长度位于第二长度区间，说明数据量不十分充足，电芯静置时间也不足够长，此时若直接预估静态OCV，准确度会降低。
第三长度区间：第二预设长度至+∞。若采集到的电压数据的长度位于第三长度区间，说明数据量充足，电芯静置时间也足够长，此时可以直接预估静态OCV，准确度较高。
进一步地，为了描述电压数据是否可信的问题，还需要确认电芯状态数据中电压数据的变化值。
若电压数据的变化值大于预设变化阈值，说明图2或图3中的电压随时间的变化曲线的趋势比较明显，可以直接预估静态OCV，准确度较高。
若电压数据的变化值不大于预设变化阈值，说明图2或图3中的电压随时间的变化曲线的趋势不明显，此时若直接预估静态OCV，准确度会降低。
其中，第一预设长度和第二预设长度均可以由电芯的电流确定，如果电芯电流较小，说明电压斜率的变化较小，此时第一预设长度和第二预设长度的取值可以适当减小；如果电芯电流较小，说明电压斜率的变化较小，此时第一预设长度和第二预设长度的取值必须适当增大，才能够确保数据量充分，保证预设趋稳态电池模型的待定参数的准确度。
针对第二长度区间，可以执行步骤102-步骤104。
在步骤102中，若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数。
下面示出四种趋稳态电池模型：
其中，V(t)为趋稳态时电池随时间变化的电压：
模型M1：
Figure PCTCN2020089181-appb-000005
其中，a 1,b 1,c 1,c′ 1,d 1为模型M1的待定参数，e为自然底数。
模型M2：
Figure PCTCN2020089181-appb-000006
其中，a 1,b 1,c 1,d 1为模型M2的待定参数，e为自然底数。
模型M3：
Figure PCTCN2020089181-appb-000007
其中，a 1,b 1,…,b n,c 1,…c n,d 1,…d n为模型M3的待定参数，e为自然底数。
模型M4：
Figure PCTCN2020089181-appb-000008
其中，a 3,b 3,c 3为模型待定参数，e为自然底数。
需要说明的是，本申请实施例涉及的趋稳态电池模型不局限于上述三种，还包括各模型的简化及其变形，此处不进行限定。
下面对获得趋稳态电池模型的第一组待定参数的方式进行说明。
在一示例中，第一组待定参数的至少部分参数可以由预设的SOH、电压、电流、温度与所述部分参数的映射关系，与当前时刻的SOH、电压、电流和温度确定，即查表线下的标定的SOH、电流、温度、电压与该至少部分参数的映射关系得到。
在一示例中，第一组待定参数的至少部参数由截至所述当前时刻的电压数据拟合得到，可以理解的是，本申请实施例中的至少部分参数包括部分参数和全部参数的情况。
在一示例中，第一组待定参数的至少部分参数可以由预设的SOH、电压、电流、温度与所述部分参数的映射关系，与当前时刻的SOH、电压、 电流和温度确定，剩余待定参数可以由从满足预设静置条件开始至当前时刻的电压序列UList＝[V1,V2,…Vn]拟合得到。拟合算法包括但不限于最小二乘法及其变化形式，遗传算法或其他参数拟合方法等。
比如，针对上文中的趋稳态电池模型3，可以通过当前的电芯SOH、电流、温度和电压和线下标定的电流、温度、电压、SOH与模型参数c 3的映射关系表，查表确定模型参数c 3值，然后采用递推最小二乘法拟合UList＝[V1,V2,…Vn]获得待定参数a 3和b 3。
下面对步骤102中的预设趋稳态电池模型的确定进行说明。
在一示例中，基于电芯电压数据，本领域技术人员可以根据经验选择合适的一个趋稳态电池模型作为预设趋稳态电池模型。
在一示例中，基于电芯电压数据，本领域技术人员可以根据经验选择合适的多个趋稳态电池模型，将这多个趋稳态电池模型的和作为预设趋稳态电池模型；
在一示例中，本领域技术人员也可以根据截至当前时刻的电压数据分别拟合m个子趋稳态电池模型，得到m条拟合曲线及其对应的拟合差值。
然后，根据拟合差值对m个子趋稳态电池模型进行排序，将拟合差值最小的前n个子趋稳态电池模型的和作为预设趋稳态电池模型，m≥n≥1。该示例中，拟合差值越小，说明子趋稳态电池模型与实际工况数据越匹配，即子趋稳态电池模型越准确，对SOC的估算精度更高。这里，拟合方差可以是方差、标准差、误差绝对值累计和、误差和、最大误差绝对值等等，此处不做限定。
需要说明的是，本领域技术人员可以根据需要选择合适的模型待定参数确定方法，此处不进行限定。此外，计算趋稳态电池模型的待定参数的步骤可以实时进行，即随着电芯静置时间的延长不断更新趋稳态电池模型的待定参数，也可以是按照一定时间段间隔进行。
在步骤103中，根据第一组待定参数，预测预设趋稳态电池模型的第二组待定参数。
具体实施时，可以根据第一组待定参数以及预设的第一组待定参数和 第二组待定参数的基于电压数据长度的对应关系，预测预设趋稳态电池模型的第二组待定参数。
示例性地，对于确定的趋稳态电池模型(比如M1)，可以根据M1的第一待定参数，查表预标定第二预设长度下的M1的第一待定参数和第二组待定参数之间的映射关系，确定M1的第二组待定参数。
在步骤104中，根据第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。
具体实施时，可以通过确定与电芯状态数据对应的稳态时间阈值Tt，然后利用由第二组待定参数确定的趋稳态电池模型处理稳态时间阈值Tt，即将稳态时间阈值Tt代入由第二组待定参数确定的趋稳态电池模型，计算得到稳态OCV预估值。
其中，稳态时间阈值Tt可以根据当前时刻的电芯的SOH和温度，查表预标定的SOH、温度与稳态时间阈值的映射关系得到。
针对第三长度区间，可以执行步骤105-步骤106，参见图4。
在步骤105中，若电芯状态数据中电压数据的长度大于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数。
在步骤106中，根据第一组待定参数，得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。
结合步骤101-步骤106可知，为了避免SOC估算时稳态OCV需要静置较长时间的问题，本申请实施例首先根据电芯满足预设静置条件下的电芯状态数据确定出趋稳态电池模型的待定参数，以表征趋稳态时OCV随时间变化趋势，然后利用待定参数处理稳态时间阈值得到稳态OCV预估值，接着利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC，以修正当前SOC。
与现有技术中的开路电压法相比，本申请实施例能够利用电池短时间静置时的外电路特性，确定用于表征趋稳态时开路电压OCV随时间变化的 趋稳态电池模型的第一组待定参数，然后利用第一组待定参数预估稳态OCV，从而减小获取稳态OCV所需时间，进而增加了SOC的修正机会，提高了开路电压法的适用性。
此外，本申请实施例还从电压数据量是否充分，以及电压数据的变化值是否可信的角度出发，限定了参与趋稳态电池模型运算的电芯状态数据的条件。
比如，为解决电压数据的长度处于第二长度区间，导致数据不充分的问题，本申请实施例能够根据第一待定参数，预测第二预设长度下预设趋稳态电池模型的第二组待定参数，然后利用第一组待定参数预估稳态OCV，从而避免因电芯状态数据不充分而导致的预设趋稳态电池模型的待定参数的准确度低的问题，进一步提高了开路电压法的估算精度和适用性。
在一个可选实施例中，可以先判断稳态OCV预估值是否超出预设的OCV预估范围；若稳态OCV预估值未超出预设的OCV预估范围，再利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。否则，说明稳态OCV预估值偏离很大或者不在正常运行范围内，此时可以利用非稳态OCV方法去修正或者不修正。
考虑到因电压数据的变化值不可信而导致基于预设趋稳态电池模型计算得到的SOC准确度低的问题时，本申请实施例针对第二长度区间，还可以执行步骤107，参见图5。针对第三长度区间，还可以执行步骤108，参见图6。
在步骤107中，若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值小于预设变化阈值，则根据电芯状态数据确定非稳态OCV预估值，利用预设的非稳态OCV与SOC的对应关系，得到与非稳态OCV预估值对应的SOC修正值，以修正当前SOC。
在步骤108中，若电芯状态数据中电压数据的长度大于第二预设长度，且电芯状态数据中电压数据的变化值小于预设变化阈值，则根据电芯状态数据确定非稳态OCV预估值，利用预设的非稳态OCV与SOC的对应关 系，得到与非稳态OCV预估值对应的SOC修正值，以修正当前SOC。
在一示例中，非稳态OCV预估值为电芯在静置结束最后一个时刻的电压和极化电压补偿值的差值。
在一示例中，可以根据电芯在本次静置结束最后一个时刻的电流和温度，查表线下标定的电流、温度与极化电压补偿值的映射关系，得到电芯在静置结束最后一个时刻的电压和该时刻下的极化电压补偿值。
在一示例中，也可以根据电芯在本次静置期间的电压和温度的统计特征(比如均方根值等)，查表统计特征与极化补偿值的映射关系，确定极化补偿值。
也就是说，与现有技术中的开路电压法相比，本申请实施例针对因电压数据的变化值不可信，导致根据预设趋稳态电池模型的SOC准确度低的问题时，采用非稳态OCV估算方式，进一步增加了SOC的修正机会，提高了开路电压法的适用性。
此外，不同于稳态OCV的直接修正方式，为了避免对SOC修正过度，在利用与非稳态OCV对应的SOC修正当前SOC前，可以采用以下修正策略：
先确定电芯在静置期间的电压回弹方向。电压回弹方向指的是当电流减小时，电芯电压的变化方向。
若电压回弹方向为电压增大，即静置期间电压随时间的变化曲线为单调递增，说明与非稳态OCV对应的SOC为可信SOC的下限，此时只有在确定与非稳态OCV对应的SOC大于当前SOC，再利用与非稳态OCV对应的SOC修正当前SOC。
若电压回弹方向为电压减小，即静置期间电压随时间的变化曲线为单调递减，说明与非稳态OCV对应的SOC为可信SOC的上限，此时只有在确定与非稳态OCV对应的SOC小于当前SOC，再利用与非稳态OCV对应的SOC修正当前SOC。
另外，为了节约计算资源，在利用与非稳态OCV对应的SOC修正当前SOC前，可以采用以下修正策略：
计算与非稳态OCV对应的SOC和当前SOC的差值，若差值的绝对值大于预设差值阈值，则利用与非稳态OCV对应的SOC修正当前SOC。
进一步地，为了避免修正过度，可以计算当前SOC和与非稳态OCV对应SOC的平均值，利用该平均值修正当前SOC。
图7为本申请又一实施例提供的SOC修正方法的流程示意图。
图7中示出的SOC修正方法包括步骤701至步骤713，用于对本申请实施例的SOC修正方法进行举例说明。
在步骤701中，判断电芯是否处于静置条件，若是，则执行步骤502，否则返回步骤501。
在步骤702中，若电芯满足静置条件，记录电芯的电压序列、电流序列、温度序列和时间序列。
在步骤703中，判断电压序列的长度是否小于第一预设长度。若是，则返回步骤702，否则执行步骤703。
在步骤704中，判断电压序列的长度是否大于第一预设长度且小于第二预设长度。若是，则执行步骤705，否则，执行步骤712。
在步骤705中，判断电压序列的变压变化值是否大于预设变化阈值、若是，则执行步骤706，否则，执行步骤709。
在步骤706中，确定趋稳态电池模型的第一组待定参数。
在步骤707中，根据第一组待定参数预测趋稳态电池模型的第二组待定参数，并根据第二组待定参数预估稳态OCV。
在步骤708中，根据预估稳态OCV，查表稳态OCV与SOC的映射关系，确定与预估稳态OCV对应的SOC，以修正当前SOC。
在步骤709中，预估非稳态OCV。
在步骤710中，根据非稳态OCV，查表非稳态OCV-SOC的映射关系，确定与预估非稳态OCV对应的SOC。
在步骤711中，根据与预估非稳态OCV对应的SOC修正当前SOC。
在步骤712中，确定电压序列的长度是否大于第二预设长度，执行步骤705，若是，执行步骤713，否则，执行步骤709。
在步骤713中，确定趋稳态电池模型的第一组待定参数并根据第一组待定参数预估稳态OCV，然后执行步骤708。
图8为本申请实施例提供的SOC修正装置的结构示意图，如图8所示，该SOC修正装置包括：数据采集模块801、第一组待定参数确定模块802、第二组待定参数预测模块803和SOC修正模块804。
其中，数据采集模块801用于采集满足预设静置条件下的电芯状态数据。
第一组待定参数确定模块802用于若电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且电芯状态数据中电压数据的变化值大于预设变化阈值，则根据电芯状态数据确定预设趋稳态电池模型的第一组待定参数。
第二组待定参数预测模块803用于根据第一组待定参数，预测预设趋稳态电池模型的第二组待定参数。
SOC修正模块804用于根据第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与稳态OCV预估值对应的SOC修正值，以修正当前SOC。
本申请实施例还提供一种电池管理系统，该电池管理系统包括如上所述的SOC修正装置。
本申请实施例还提供一种存储介质，其上存储有程序，程序被处理器执行时实现如上所述的SOC修正方法。
虽然已经参考优选实施例对本申请进行了描述，但在不脱离本申请的范围的情况下，可以对其进行各种改进并且可以用等效物替换其中的部件。尤其是，只要不存在结构冲突，各个实施例中所提到的各项技术特征均可以任意方式组合起来。本申请并不局限于文中公开的特定实施例，而是包括落入权利要求的范围内的所有技术方案。

Claims (15)

  1. 一种SOC修正方法，其中，包括：
    采集满足预设静置条件下的电芯状态数据；
    若所述电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且所述电芯状态数据中电压数据的变化值大于预设变化阈值，则根据所述电芯状态数据确定预设趋稳态电池模型的第一组待定参数；
    根据所述第一组待定参数，预测所述预设趋稳态电池模型的第二组待定参数；
    根据所述第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与所述稳态OCV预估值对应的SOC修正值，以修正当前SOC。
  2. 根据权利要求1所述的方法，其中，所述方法还包括：
    若所述电芯状态数据中电压数据的长度大于所述第二预设长度，且所述电芯状态数据中电压数据的变化值大于所述预设变化阈值，则根据所述电芯状态数据确定所述预设趋稳态电池模型的第一组待定参数；
    根据所述第一组待定参数，得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与所述稳态OCV预估值对应的SOC修正值，以修正当前SOC。
  3. 根据权利要求1所述的方法，其中，所述方法还包括：
    若所述电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且所述电芯状态数据中电压数据的变化值小于所述预设变化阈值，则根据所述电芯状态数据确定非稳态OCV预估值；
    或者，若所述电芯状态数据中电压数据的长度大于所述第二预设长度，且所述电芯状态数据中电压数据的变化值小于所述预设变化阈值，则根据所述电芯状态数据确定非稳态OCV预估值；
    利用预设的非稳态OCV与SOC的对应关系，得到与所述非稳态OCV预估值对应的SOC修正值，以修正当前SOC。
  4. 根据权利要求1所述的方法，其中，所述方法还包括：
    若所述电芯状态数据长度小于所述第一预设长度，则不对当前SOC进 行修正。
  5. 根据权利要求1所述的方法，其中，所述根据所述第一组待定参数，预测所述预设趋稳态电池模型的第二组待定参数，包括：
    根据所述第一组待定参数，以及预设的所述第一组待定参数和所述第二组待定参数的基于电压数据长度的对应关系，预测所述预设趋稳态电池模型的第二组待定参数。
  6. 根据权利要求1所述的方法，其中，所述根据所述第二组待定参数，计算得到稳态OCV预估值，包括：
    确定与所述电芯状态数据对应的稳态时间阈值；
    利用由所述第二组待定参数确定的趋稳态电池模型处理所述稳态时间阈值，计算得到稳态OCV预估值。
  7. 根据权利要求1所述的方法，其中，所述利用预设的稳态OCV与SOC的对应关系，确定与所述稳态OCV预估值对应的SOC修正值，以修正当前SOC，包括：
    判断所述稳态OCV预估值是否超出预设的OCV预估范围；
    若所述稳态OCV预估值未超出所述预设的OCV预估范围，则利用预设的稳态OCV与SOC的对应关系，确定与所述稳态OCV预估值对应的SOC修正值，以修正当前SOC。
  8. 根据权利要求1所述的方法，其中，所述方法还包括：
    确定所述电芯在静置期间的电压回弹方向；
    若所述电压回弹方向为电压增大，且与所述非稳态OCV预估值对应的SOC修正值大于所述当前SOC，则利用与所述非稳态OCV预估值对应的SOC修正值修正所述当前SOC；
    若所述电压回弹方向为电压减小，且与所述非稳态OCV预估值对应的SOC修正值小于所述当前SOC，则利用与所述非稳态OCV预估值对应的SOC修正值修正所述当前SOC。
  9. 根据权利要求1所述的方法，其中，
    所述电芯状态数据包括：SOH、电压、电流和温度；
    所述第一组待定参数的至少部分参数由预设的SOH、电压、电流、温 度与所述部分参数的映射关系，与所述当前时刻的SOH、电压、电流、温度确定。
  10. 根据权利要求1或9所述的方法，其中，
    所述电芯状态数据包括：SOH、电压和温度；
    所述第一组待定参数的至少部分参数由截至所述当前时刻的电压数据拟合得到。
  11. 根据权利要求10所述的方法，其中，所述方法还包括：
    根据所述电芯状态数据中的电压数据分别拟合m个子趋稳态电池模型，得到m条拟合曲线及其对应的拟合差值；
    根据所述拟合差值对m个所述子趋稳态电池模型进行排序，将所述拟合差值最小的前n个子趋稳态电池模型的和作为所述预设趋稳态电池模型。
  12. 根据权利要求1所述的方法，其中，所述预设静置条件为所述电芯的电流小于预设电流阈值。
  13. 一种SOC修正装置，其中，包括：
    数据采集模块，用于采集满足预设静置条件下的电芯状态数据；
    第一组待定参数确定模块，用于若所述电芯状态数据中电压数据的长度大于第一预设长度且小于第二预设长度，且所述电芯状态数据中电压数据的变化值大于预设变化阈值，则根据所述电芯状态数据确定预设趋稳态电池模型的第一组待定参数；
    第二组待定参数预测模块，用于根据所述第一组待定参数，预测所述预设趋稳态电池模型的第二组待定参数；
    SOC修正模块，用于根据所述第二组待定参数，计算得到稳态OCV预估值，并利用预设的稳态OCV与SOC的对应关系，确定与所述稳态OCV预估值对应的SOC修正值，以修正当前SOC。
  14. 一种电池管理系统，其中，包括如权利要求13所述的SOC修正装置。
  15. 一种存储介质，其上存储有程序，其中，程序被处理器执行时实现如权利要求1-12任一项所述的SOC修正方法。
PCT/CN2020/089181 2019-05-24 2020-05-08 Soc修正方法和装置、电池管理系统和存储介质 Ceased WO2020238583A1 (zh)

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