CN112763917B - Method and system for correcting SOC (state of charge) of battery pack of energy storage power station in real time - Google Patents
Method and system for correcting SOC (state of charge) of battery pack of energy storage power station in real time Download PDFInfo
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- 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
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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]
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Abstract
The invention discloses a real-time correction method and a real-time correction system for the SOC of a battery pack of an energy storage power station. According to the method, an SOC reference point is selected firstly by using historical operation data of the battery pack, then the actual capacity and the cycle number of the battery pack are subjected to regression analysis, the actual capacity of the battery pack in each cycle is calculated by using a regression equation, finally, the current SOC value of the battery pack is calculated, and then the predicted SOC value of the BMS is corrected. The method can be used after some operation data are collected from the BMS which is put into operation, can be used for evaluating the prediction accuracy of the BMS, and is convenient for a user to observe the actual operation condition of the battery pack and the BMS; the SOC correction module algorithm can be written into the BMS, so that the utilization rate of the battery is improved, and the service life of the battery pack is prolonged.
Description
Technical Field
The invention relates to a method for checking SOC in real time, in particular to a method and a system for correcting the SOC of a battery pack of an energy storage power station in real time based on historical operation data.
Background
The method has the advantages that the SOC (State of Charge) of the battery pack of the energy storage power station is correctly estimated, and the method has important significance for prolonging the service life of the battery pack of the energy storage power station and improving the utilization rate of the battery. In the current BMS (battery management system), an ampere-hour integration method, kalman filtering, and the like are mainly used as an SOC prediction algorithm for practical use.
The ampere-hour integration method is a widely used algorithm at present due to simple and reliable algorithm, but the error is increased along with the use time due to the error of current measurement or data loss; the Kalman filtering prediction SOC requires a very accurate battery model, but accurate parameters are often difficult to obtain, and prediction deviation can also be caused.
Aiming at the problem of SOC prediction error of the conventional BMS, a method for correcting the SOC of the battery pack of the energy storage power station in real time needs to be designed, so that the utilization rate of the battery is improved, and meanwhile, the accuracy of judging the BMS by a user is facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art, and provide a method and a system for correcting the SOC of a battery pack of an energy storage power station in real time based on historical operation data.
In order to achieve the purpose, the invention adopts a technical scheme that: the method for correcting the SOC of the battery pack of the energy storage power station in real time based on historical operation data comprises the following steps:
s1, acquiring historical operation data of the battery pack BMS of the energy storage power station in a time series format;
s2, selecting an SOC reference point from the historical operation data obtained in the step S1, and calculating a battery pack historical capacity reference value of each charge-discharge cycle;
s3, performing regression analysis on the historical capacity reference values of the charging and discharging battery packs obtained in the step S2 respectively, and predicting the actual capacity of the battery packs in each charging and discharging cycle;
and S4, calculating the current SOC value of the battery pack by combining the SOC reference point in the step S2 by using the regression analysis result in the step S3, and correcting the predicted SOC value of the BMS.
Further, in step S1, the historical operation data of the energy storage power station battery pack BMS is collected in a time-series format, which means as follows: uploading data once every delta t time by BMS, and at t moment, the BMS transmitting the voltage V of the battery packtBattery pack current ItAnd SOC of the battery packtCumulative charge Q of battery packCtAnd the accumulated discharge QDtAnd (6) uploading the data.
Further, in step S2, the SOC reference point is selected by: comprehensively considering ampere-hour integral error and attenuation condition of battery capacity, and taking time t before p charge-discharge cyclesbAnd its SOC value SOCtbTaken as SOC reference point, denoted as (t)b，SOCtb)。
Furthermore, the p value can be selected by combining the actual data situation, and the preferable range is 20-60.
Further, in step S2, the method for calculating the battery pack historical capacity reference value for each charge/discharge cycle includes:
1) for charging, the calculation formula is as follows:
Qkc＝(Qkcmax-Qkcmin)/(SOCkcmax-SOCkcmin)，
in the formula, QkcThe battery pack historical capacity reference value at the k charging cycle; qkcmaxAnd QkcminRespectively representing the maximum value and the minimum value of the accumulated charge amount in the k-th charging, namely the accumulated charge amount value after the charging is finished and before the charging is started; SOC (system on chip)kcmaxAnd SOCkcminRespectively representing the maximum SOC value and the minimum SOC value during the k-th charging, namely the SOC values after the charging is finished and before the charging is started;
2) for discharge, the calculation formula is as follows:
Qkd＝(Qkdmax-Qkdmin)/(SOCkdmax-SOCkdmin)，
in the formula, QkdThe historical capacity reference value of the battery pack at the k discharge cycle; qkdmaxAnd QkdminRespectively representing the maximum value and the minimum value of the accumulated discharge amount during the k-th discharge, namely the accumulated discharge amount after the discharge is finished and before the discharge is started; SOCkdmaxAnd SOCkdminThe maximum SOC value and the minimum SOC value at the time of the k-th discharge, that is, the SOC values before the start of discharge and after the end of discharge are indicated.
Further, in step S3, the regression analysis is performed by using a least squares regression method using the regression equation:
Qk＝a+b·ecx，
in the formula: qkThe predicted actual capacity of the battery pack when the cycle number is k, wherein k is the cycle number, e is a natural base number, and a, b and c are undetermined parameters.
Further, in step S3, the step of predicting the actual capacity of the battery pack for each charge-discharge cycle means obtaining a regression equation, and then sequentially substituting the number of cycles from 1 to n into the regression equation to calculate the predicted actual capacity of the battery pack, and the predicted charge capacity and discharge capacity of the battery pack are respectively denoted as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre。
Further, in step S4, the method for calculating the current SOC value of the battery pack includes:
in the formula: SOC (system on chip)tChecking the value of SOC at time ttbFor the SOC value, Q, of the SOC reference point selected in step S2CiAnd QDiRespectively representing the accumulated charge quantity and the accumulated discharge quantity of the battery pack at the moment i, QCkpreRepresents QCiWithin the cycle, the cycle predicted by step S3Actual capacity of middle battery pack charging, QDkpreRepresents QDiIn the cycle, the step S3 predicts the actual capacity of battery discharge in the cycle.
The other technical scheme adopted by the invention is as follows: a real-time correction system of energy storage power station battery pack SOC comprises:
the historical operating data acquisition module is used for acquiring historical operating data of the battery pack BMS of the energy storage power station in a time series format;
the battery pack historical capacity reference value calculating module is used for selecting an SOC reference point from historical operation data obtained by the historical operation data acquisition module and calculating a battery pack historical capacity reference value of each charge-discharge cycle;
the battery pack actual capacity prediction module is used for respectively carrying out regression analysis on the historical capacity reference values of the charging and discharging battery packs obtained in the battery pack historical capacity reference value calculation module and predicting the battery pack actual capacity of each charging and discharging cycle;
and the SOC value calculating and modifying module is used for calculating the current SOC value of the battery pack by utilizing the regression analysis result in the battery pack actual capacity predicting module and combining the SOC reference point in the battery pack historical capacity reference value calculating module, and then correcting the predicted SOC value of the BMS.
Further, the regression analysis is performed in the actual capacity prediction module of the battery pack by using a least square regression method using the regression equation:
Qk＝a+b·ecx
in the formula: qkThe predicted actual capacity of the battery pack when the cycle number is k, wherein k is the cycle number, e is a natural base number, and a, b and c are undetermined parameters;
the step of predicting the actual capacity of the battery pack in each charge-discharge cycle refers to the step of sequentially substituting the cycle times from 1 to n into a regression equation after obtaining the regression equation to calculate the predicted actual capacity of the battery pack, and recording the predicted charging capacity and discharging capacity of the battery pack as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre；
In the SOC value calculating and modifying module, the calculation method for calculating the current SOC value of the battery pack is as follows:
in the formula: SOCtChecking the value of SOC at time ttbCalculating SOC value, Q, of SOC reference point selected from module for calculating historical capacity reference value of battery packCiAnd QDiRespectively representing the accumulated charge and discharge of the battery pack at i moment, QCkpreRepresents QCiThe battery pack actual capacity prediction module predicts the battery pack charging actual capacity, Q, in the cycle during which the battery pack is chargedDkpreRepresents QDiAnd in the cycle period, the actual battery pack capacity prediction module predicts the actual battery pack discharge capacity in the cycle period.
The invention has the following beneficial effects: the invention aims at the problem of SOC prediction error of the battery pack BMS of the current energy storage power station, and fully utilizes the historical data of the battery pack operation to calculate and correct the SOC value. The method can be used after some operation data are collected from the BMS which is put into operation, can be used for evaluating the prediction accuracy of the BMS, and is convenient for a user to observe the actual operation condition of the battery pack and the BMS; the SOC correction module algorithm can be written into the BMS, so that the utilization rate of the battery is improved, and the service life of the battery pack is prolonged.
Drawings
FIG. 1 is a flow chart of a method of SOC correction in an embodiment of the present invention;
fig. 2 is a graph showing a regression curve of the capacity of the battery cluster 1 according to the embodiment of the present invention;
fig. 3 is a graph comparing the SOC correction value of the battery cluster 1 with the estimated SOC value of the battery cell system according to the embodiment of the present invention;
fig. 4 is a graph showing a regression curve of the capacity of the battery cluster 2 according to the embodiment of the present invention;
fig. 5 is a comparison graph of the SOC correction value of the battery cluster 2 and the predicted SOC value of the battery cell system according to the embodiment of the present invention;
fig. 6 is a block diagram of an SOC correction system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the drawings and the detailed description.
Example 1
The embodiment provides a method for correcting the SOC of a battery pack of an energy storage power station in real time based on historical operation data. The present embodiment uses 27 days of actual operating data of a certain energy storage power station. The BMS uploads data including the battery cluster voltage, current, temperature, accumulated charge, accumulated discharge, etc. every 5 minutes. And (3) taking data of 2 battery clusters, cleaning the data, taking the data of the previous 25 days as historical data, and correcting and evaluating the SOC value of the last 2 days. Fig. 1 is a flowchart showing the SOC correction method in the present embodiment.
Analysis was performed on cell cluster 1: since there are only 27 charge-discharge cycles in the data, the 1 st time node can be taken as the SOC reference point. And then calculating a historical charge-discharge capacity reference value according to the following formula:
1) for charging, the calculation formula is as follows:
Qkc＝(Qkcmax-Qkcmin)/(SOCkcmax-SOCkcmin)，
in the formula, QkcThe historical capacity reference value of the battery pack at the k-th charging cycle is obtained; qkcmaxAnd QkcminRespectively representing the maximum value and the minimum value of the accumulated charge amount in the k-th charging, namely the accumulated charge amount value after the charging is finished and before the charging is started; SOCkcmaxAnd SOCkcminRespectively representing the maximum SOC value and the minimum SOC value during the k-th charging, namely the SOC values after the charging is finished and before the charging is started;
2) for discharge, the calculation formula is as follows:
Qkd＝(Qkdmax-Qkdmin)/(SOCkdmax-SOCkdmin)，
in the formula, QkdThe historical capacity reference value of the battery pack at the k discharge cycle; qkdmaxAnd QkdminRespectively representing the maximum value and the minimum value of the accumulated discharge amount during the k-th discharge, namely the accumulated discharge amount after the discharge is finished and before the discharge is started; SOC (system on chip)kdmaxAnd SOCkdminThe maximum SOC value and the minimum SOC value at the time of the kth discharge, that is, the SOC values before the start of discharge and after the end of discharge are indicated.
Using the data of the previous 25 days as historical data, wherein, 25 charging and discharging cycles exist, calculating to obtain a charging historical capacity reference value QC1,...,QC25Total 25, discharge history capacity reference value QD1,...,QD25And the number of the channels is 25. The least square index regression analysis is respectively carried out on the charging and discharging times and the charging and discharging historical capacity reference value, two regression curves can be obtained, and a regression curve chart is given in figure 2.
After obtaining the regression equation, sequentially substituting the cycle times into the regression equation from 1 to 25, calculating to obtain the predicted capacity, and respectively recording the predicted charge capacity and discharge capacity as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre. And then, in combination with the SOC reference point determined before, the SOC values of the next 2 days are respectively calculated, and the calculation method comprises the following steps:
in the formula: SOC (system on chip)tChecking the SOC value at time tbSOC value, Q, of the SOC reference point selected in step S2CiAnd QDiRespectively represent the accumulated charge quantity and the accumulated discharge quantity of the battery cluster at the moment i, and QCkpreRepresents QCiWithin the cycle, the actual charging capacity, Q, of the battery cluster in the cycle predicted by the step S3DkpreRepresents QDiWithin the cycle period, the actual capacity of the battery cluster discharged in the cycle predicted by step S3.
Fig. 3 shows a map of the SOC correction result for the last 2 days and the SOC prediction result of the original BMS.
Similarly, the battery cluster 2 may be analyzed, and fig. 4 shows a graph of the capacity regression of the battery cluster 2, and fig. 5 shows a graph of the SOC correction result of the last 2 days and the SOC prediction result of the original BMS.
The SOC values of 542 points were evaluated for each of the battery clusters 1 and 2, and the absolute value of the difference between the SOC correction result and the SOC prediction result of the original BMS was taken as an absolute error, and part of the statistical results are shown in table 1.
Table 1 analysis table of evaluation results of battery cluster 1 and cluster 2:
| maximum of absolute error | Mean of absolute error | The absolute error is large (>3) In a ratio of | |
| Battery cluster 1 | 4.44 | 1.89 | 28.41％ |
| Battery cluster 2 | 33.54 | 5.82 | 66.61％ |
From this evaluation, it can be seen that the BMS prediction result of the battery cluster 1 is better, and the BMS prediction result of the battery cluster 2 is worse, in terms of SOC prediction accuracy. The reason for this is that the BMS of the battery cluster 2 generates a jump in the SOC value in the prediction process, and as can be seen from fig. 5, a jump occurs in the latter half of the curve, resulting in a deviation in the prediction of the SOC. The correction method provided by the invention can better find and correct the error.
Example 2
The embodiment provides a system for correcting the SOC of a battery pack of an energy storage power station in real time based on historical operation data, as shown in FIG. 6.
A real-time correction system of energy storage power station battery pack SOC comprises:
the historical operating data acquisition module is used for acquiring historical operating data of the battery pack BMS of the energy storage power station in a time series format;
the battery pack historical capacity reference value calculating module is used for selecting an SOC reference point from historical operation data obtained by the historical operation data acquisition module and calculating a battery pack historical capacity reference value of each charge-discharge cycle;
the battery pack actual capacity prediction module is used for respectively carrying out regression analysis on the historical capacity reference values of the charging and discharging battery packs obtained in the battery pack historical capacity reference value calculation module and predicting the battery pack actual capacity of each charging and discharging cycle;
and the SOC value calculating and modifying module is used for calculating the current SOC value of the battery pack by utilizing the regression analysis result in the battery pack actual capacity predicting module and combining the SOC reference point in the battery pack historical capacity reference value calculating module, and then correcting the predicted SOC value of the BMS.
In the historical operation data acquisition module, the historical operation data of the energy storage power station battery pack BMS is acquired in a time series format, and the historical operation data acquisition module has the following meanings: uploading data once every delta t time by BMS, and at t moment, the BMS transmitting the voltage V of the battery packtBattery current ItAnd SOC of battery packtCumulative charge Q of battery packCtAnd the accumulated discharge QDtAnd (6) uploading the data.
In the battery pack historical capacity reference value calculation module, the selection method of the SOC reference point is as follows: comprehensively considering ampere-hour integral error and attenuation condition of battery capacity, and taking time t before p charge-discharge cyclesbAnd its SOC value SOCtbTaken as SOC reference point, denoted as (t)b，SOCtb). The describedThe p value of (A) is selected by combining the actual data condition, and the range is 20-60.
In the module for calculating the historical capacity reference value of the battery pack, the method for calculating the historical capacity reference value of the battery pack in each charge-discharge cycle comprises the following steps:
1) for charging, the calculation formula is as follows:
Qkc＝(Qkcmax-Qkcmin)/(SOCkcmax-SOCkcmin)，
in the formula, QkcThe historical capacity reference value of the battery pack at the k-th charging cycle is obtained; qkcmaxAnd QkcminRespectively representing the maximum value and the minimum value of the accumulated charge amount in the k-th charging, namely the accumulated charge amount value after the charging is finished and before the charging is started; SOC (system on chip)kcmaxAnd SOCkcminRespectively representing the maximum SOC value and the minimum SOC value during the k-th charging, namely the SOC values after the charging is finished and before the charging is started;
2) for discharge, the calculation formula is as follows:
Qkd＝(Qkdmax-Qkdmin)/(SOCkdmax-SOCkdmin)，
in the formula, QkdThe historical capacity reference value of the battery pack at the k discharge cycle; qkdmaxAnd QkdminRespectively representing the maximum value and the minimum value of the accumulated discharge amount during the k-th discharge, namely the accumulated discharge amount after the discharge is finished and before the discharge is started; SOC (system on chip)kdmaxAnd SOCkdminThe maximum SOC value and the minimum SOC value at the time of the k-th discharge, that is, the SOC values before the start of discharge and after the end of discharge are indicated.
The regression analysis is performed in the battery pack actual capacity prediction module by using a least squares regression method using the regression equation:
Qk＝a+b·ecx
in the formula: qkThe predicted actual capacity of the battery pack when the cycle number is k, wherein k is the cycle number, e is a natural base number, and a, b and c are undetermined parameters;
the battery with each charge-discharge cycle predictedAnd the actual capacity of the battery pack is obtained by sequentially substituting the cycle times from 1 to n into the regression equation after the regression equation is obtained, calculating to obtain the predicted actual capacity of the battery pack, and respectively recording the predicted charging capacity and discharging capacity of the battery pack as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre；
In the SOC value calculating and modifying module, the calculation method for calculating the current SOC value of the battery pack is as follows:
in the formula: SOCtChecking the value of SOC at time ttbCalculating SOC value, Q, of SOC reference point selected from a module for reference values of battery pack historical capacityCiAnd QDiRespectively representing the accumulated charge and discharge of the battery pack at i moment, QCkpreRepresents QCiIn the cycle period, the actual battery pack charging capacity in the cycle period is predicted by the battery pack actual capacity prediction moduleDkpreRepresents QDiAnd in the cycle period, the actual battery pack capacity prediction module predicts the actual battery pack discharge capacity in the cycle period.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (6)
1. A real-time correction method for the SOC of a battery pack of an energy storage power station is characterized by comprising the following steps:
s1, acquiring historical operation data of the battery pack BMS of the energy storage power station in a time series format;
s2, selecting an SOC reference point from the historical operation data obtained in the step S1, and calculating a battery pack historical capacity reference value of each charge and discharge cycle;
s3, respectively carrying out regression analysis on the historical capacity reference values of the charging and discharging battery packs obtained in the step S2, and predicting the actual capacity of the battery pack in each charging and discharging cycle;
s4, calculating the current SOC value of the battery pack by using the regression analysis result in the step S3 and combining the SOC reference point in the step S2, and correcting the predicted SOC value of the BMS;
the regression analysis is performed as described in step S3, using a least squares regression method using the regression equation:
Qk＝a+b·eck
in the formula: qkThe predicted actual capacity of the battery pack when the cycle number is k, wherein k is the cycle number, e is a natural base number, and a, b and c are undetermined parameters;
in step S3, the step of predicting the actual capacity of the battery pack for each charge-discharge cycle refers to obtaining a regression equation, then sequentially substituting the cycle numbers from 1 to n into the regression equation, and calculating to obtain the predicted actual capacity of the battery pack, and the predicted charge capacity and discharge capacity of the battery pack are respectively denoted as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre；
In step S4, the method for calculating the current SOC value of the battery pack includes:
in the formula: SOCtChecking the SOC value at time ttbFor the SOC value, Q, of the SOC reference point selected in step S2CiAnd QDiRespectively representing the accumulated charge quantity and the accumulated discharge quantity of the battery pack at the moment i, QCkpreRepresents QCiIn the cycle, the actual charging capacity, Q, of the battery pack in the cycle predicted by the step S3DkpreRepresents QDiIn the cycle, the actual discharge capacity t of the battery pack in the cycle predicted in the step S3bIndicating the time before the charge-discharge cycle.
2. The real-time correction method for the SOC of the energy storage power station battery pack according to claim 1, wherein in step S1, the historical operation data of the energy storage power station battery pack BMS are collected in a time series format, and the meanings are as follows: uploading data once every delta t time by BMS, and at t moment, the BMS transmitting the voltage V of the battery packtBattery pack current ItAnd SOC of the battery packtThe cumulative charge Q of the battery packCtAnd the accumulated discharge QDtAnd (6) uploading the data.
3. The method for correcting the SOC of the energy storage power station battery pack in real time as claimed in claim 1, wherein in step S2, the SOC reference point is selected by the following method: comprehensively considering ampere-hour integral error and attenuation condition of battery capacity, and taking time t before p charge-discharge cyclesbAnd its SOC value SOCtbTaken as SOC reference point, denoted as (t)b，SOCtb)。
4. The real-time correction method for the SOC of the battery pack of the energy storage power station as claimed in claim 3, wherein the p value is selected in combination with actual data conditions, and the range is 20-60.
5. The real-time correction method for the SOC of the battery pack of the energy storage power station as claimed in claim 1, wherein in step S2, the historical capacity reference value of the battery pack for each charging and discharging cycle is calculated by the following method:
1) for charging, the calculation formula is as follows:
Qkc＝(Qkcmax-Qkcmin)/(SOCkcmax-SOCkcmin)，
in the formula, QkcThe battery pack historical capacity reference value at the k charging cycle; qkcmaxAnd QkcminThe maximum value and the minimum value of the cumulative charge amount in the k-th charge, that is, the cumulative charge amount after the end of the charge and before the start of the charge, are shownAn electric quantity value; SOCkcmaxAnd SOCkcminRespectively representing the maximum SOC value and the minimum SOC value during the k-th charging, namely the SOC values after the charging is finished and before the charging is started;
2) for discharge, the calculation formula is as follows:
Qkd＝(Qkdmax-Qkdmin)/(SOCkdmax-SOCkdmin)，
in the formula, QkdThe historical capacity reference value of the battery pack at the k discharge cycle; qkdmaxAnd QkdminRespectively representing the maximum value and the minimum value of the accumulated discharge amount during the k-th discharge, namely the accumulated discharge amount after the discharge is finished and before the discharge is started; SOC (system on chip)kdmaxAnd SOCkdminThe maximum SOC value and the minimum SOC value at the time of the k-th discharge, that is, the SOC values before the start of discharge and after the end of discharge are indicated.
6. The utility model provides a real-time correction system of energy storage power station battery pack SOC which characterized in that includes:
the historical operating data acquisition module is used for acquiring historical operating data of the battery pack BMS of the energy storage power station in a time series format;
the battery pack historical capacity reference value calculating module is used for selecting an SOC reference point from historical operation data obtained by the historical operation data acquisition module and calculating a battery pack historical capacity reference value of each charge and discharge cycle;
the battery pack actual capacity prediction module is used for respectively carrying out regression analysis on the historical capacity reference values of the charging and discharging battery packs obtained in the battery pack historical capacity reference value calculation module and predicting the battery pack actual capacity of each charging and discharging cycle;
the SOC value calculating and modifying module is used for calculating the current SOC value of the battery pack by utilizing the regression analysis result in the actual capacity predicting module of the battery pack and combining an SOC reference point in the historical capacity reference value calculating module of the battery pack, and then correcting the predicted SOC value of the BMS;
the regression analysis is performed in the battery pack actual capacity prediction module by using a least squares regression method using the regression equation:
Qk＝a+b·eck
in the formula: qkThe predicted actual capacity of the battery pack when the cycle number is k, wherein k is the cycle number, e is a natural base number, and a, b and c are undetermined parameters;
the step of predicting the actual capacity of the battery pack in each charging and discharging cycle refers to the step of sequentially substituting the cycle times from 1 to n into a regression equation after obtaining the regression equation, calculating to obtain the predicted actual capacity of the battery pack, and respectively recording the predicted charging capacity and discharging capacity of the battery pack as QC1pre,...,QCnpreAnd QD1pre,...,QDnpre；
In the SOC value calculating and modifying module, the calculation method for calculating the current SOC value of the battery pack is as follows:
in the formula: SOCtChecking the SOC value at time ttbCalculating SOC value, Q, of SOC reference point selected from a module for reference values of battery pack historical capacityCiAnd QDiRespectively representing the accumulated charge and discharge of the battery pack at i moment, QCkpreRepresents QCiThe battery pack actual capacity prediction module predicts the battery pack charging actual capacity, Q, in the cycle during which the battery pack is chargedDkpreRepresents QDiIn the cycle period, the actual battery pack discharge capacity t in the cycle period is predicted by the battery pack actual capacity prediction modulebIndicating the time before the charge-discharge cycle.
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