CN114035052B - SOC interval calibration method, system and medium based on energy window - Google Patents
SOC interval calibration method, system and medium based on energy window Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- 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 application discloses an SOC interval calibration method, system and medium based on an energy window, comprising the steps of charging and discharging energy storage systems with different powers, and recording charging and discharging test data; wh integration is carried out on the charge and discharge test data, and a Wh-OCV curve is obtained through fitting; taking an energy integral E of the Wh-OCV curve as an energy window reference, moving an energy window with a width coefficient P in a charge-discharge interval of the Wh-OCV curve, and calculating a voltage standard deviation Vol_SD and a voltage average value Vol_mean of each energy window; determining an optimal charge-discharge energy window E under the energy window with the width coefficient of P according to the sequencing result best And then mapped to a voltage interval at a specified power of the energy storage system. The application can calibrate the whole SOC section of the energy storage station based on the chargeable and dischargeable power and chargeable and dischargeable energy, ensures a relatively accurate assessment means for the power and the capacity of the energy storage station, and provides effective reference for scheduling operation.
Description
Technical Field
The application belongs to the technical field of electrochemical energy storage, and particularly relates to an SOC interval calibration method, system and medium based on an energy window.
Background
The large-scale development of electrochemical energy storage puts higher and higher requirements on the functions of control equipment, the state of charge (SOC) calibration serves as a core function of a Battery Management System (BMS), the key effects of evaluating the battery state, reasonably distributing power of a Power Conversion System (PCS) for an Energy Management System (EMS) and providing accurate energy data for a dispatching department are played, and SOC calibration algorithms are increasingly paid attention. At present, the requirements of an energy storage SOC calibration algorithm are not clear in the national standard and the industry standard, and each equipment manufacturer respectively provides the SOC calibration algorithm suitable for the characteristics of the self-product. From the existing literature, SOC calibration is commonly used in the field of power automobiles, and an SOC calibration algorithm is mainly based on an ampere-hour integration method, an open circuit voltage curve (OCV) method, a kalman filtering method and the like, and is often corrected by using the ampere-hour integration method as a basic method and using other methods. The common practice of the algorithm is that a battery model is firstly set in the SOC calibration process, then constant current is used for charging and discharging, then test data and model simulation data are compared, and the effectiveness of the model is checked. The algorithm considers the resistance and capacitance effects of the battery model, and has a first-order model and a second-order model according to the parallel relation between the capacitance and the resistance, and the higher the order, the more the adjustable dimension is, so that the test data can be fitted more accurately. The test object of the algorithm is usually fewer battery cells, the consistency of the battery cells is better, and the overall test condition is stable by adopting a constant-current charging and discharging mode.
The current energy storage station has tens of thousands of battery cores, which are several orders of magnitude higher than the battery cores in the power automobile industry, the abrupt increase of the battery cores brings higher control complexity, and the changed application scene brings different demands. For the energy storage station, the requirements of the power grid on power and energy need to be considered, and the power battery does not need to be considered, so that different requirements on SOC calibration calculation are met. The SOC calibration algorithm in the laboratory is not entirely suitable for application in energy storage power stations.
First, the applicable battery model is different. The number of the battery cells of the energy storage station is huge, the parameter difference of the single battery cell model is weakened by the collective effect of a large number of battery cells, and the battery cells show smooth external characteristics. The first-order and second-order models of the single-cell battery are not suitable for the whole charging and discharging process of the energy storage station, and the simplified resistance model can reflect the battery characteristics of the energy storage station. Second, the power and energy accuracy requirements are different. The energy storage power station mainly faces to a power grid scene, and the main requirement is that the maximum charging and discharging power and the charging and discharging energy (time) of the energy storage power station can be provided based on the SOC so as to meet the scheduling operation requirement. The current power battery SOC calibration method is basically based on charge angle and is not considered from energy angle, the chemical action of the battery in the charge and discharge process is considered to be capable of releasing charge quantity, but the complete release of energy is impossible, and the released energy is changed along with the external working condition, so that the practical application is relatively less. However, in actual power grid dispatching operation, the charge quantity is not referenced, but the reference power and the energy are referenced, and even if the charge and discharge energy changes along with the external working condition, the feasible operation reference can be provided only by giving corresponding margin. Finally, the SOC definitions will be different corresponding to the application scenarios. The power grid scene calculates the SOC by using energy integration, unlike the integration of power in the power battery domain. The power grid is used for determining the power and energy requirements, calibration of the SOC usable range is needed to be considered, and the power battery field is not required.
Therefore, according to different application scenes and requirements of the power grid demand and the power battery industry, an SOC calibration algorithm is needed to calibrate the whole SOC section of the energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy, so that a relatively accurate assessment means for the power and the capacity of the energy storage station is ensured, and an effective reference is provided for scheduling operation.
Disclosure of Invention
The application aims to solve the technical problems: aiming at the problems in the prior art, the application provides the SOC interval calibration method, the system and the medium based on the energy window, which can calibrate the whole SOC interval of the energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy, ensure that a more accurate assessment means can be provided for the power and the capacity of the energy storage station, and provide effective reference for scheduling operation.
In order to solve the technical problems, the application adopts the following technical scheme:
an SOC interval calibration method based on an energy window comprises the following steps:
1) Charging and discharging with different powers are carried out on the energy storage system, and charging and discharging test data are recorded;
2) Wh integration is carried out on the charge and discharge test data, and a Wh-OCV curve is obtained through fitting;
3) Taking an energy integral E of the Wh-OCV curve as an energy window reference, moving an energy window with a width coefficient P in a charge-discharge interval of the Wh-OCV curve, and calculating a voltage standard deviation Vol_SD and a voltage average value Vol_mean of each energy window;
4) Sequencing the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, and determining the optimal charge-discharge energy window E under the energy window with the width coefficient of P according to the sequencing result best ；
5) Energy window E for optimal charge and discharge best Mapping to a voltage interval under the appointed power of the energy storage system, and completing the SOC interval calibration of the energy storage system.
Optionally, step 1) further includes power limitation caused by upper and lower limits of the SOC in the process of releasing the EMS for charging and discharging, only voltage, current and temperature limitation is reserved, and the battery capacity is completely released before charging and discharging the energy storage system with different power.
Optionally, in step 1), when the energy storage system is charged and discharged with different powers, the charged and discharged with different powers include three active powers of 10% PN, 50% PN and 100% PN, where PN refers to rated active power.
Optionally, step 1) when charging and discharging the energy storage system with different powers, the charging and discharging process with the same power is uninterrupted and the power is kept constant, and the charging and discharging test data is recorded by adopting uniform time intervals, and the period is not more than 1 second.
Optionally, after the Wh-OCV curves are obtained by fitting in the step 2), the method further comprises the step of comparing Wh-OCV curves corresponding to different powers on the same graph, and determining the correctness of the battery charge and discharge data and the internal resistance model by checking the coincidence degree of the three curves.
Optionally, taking the energy integral E of the Wh-OCV curve as the energy window reference in step 3) includes: the energy integral E of the Wh-OCV curve is first calculated according to the following formula:
in the above formula, E is the energy integral of the Wh-OCV curve, N is the number of segments of the Wh-OCV curve discretized in time axis, i n For the current of each segment after discretization, u n The delta t is the basic time of discretization of the Wh-OCV curve on the time axis for the current voltage of each section after discretization; the energy integral E of the Wh-OCV curve is multiplied by different scaling factors P to obtain the energy PE contained within the left and right boundary of the energy window W.
Optionally, step 4) includes:
4.1 Calculating the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, adding the voltage standard deviation Vol_SD of each energy window into a voltage standard deviation sequence Vol_SD_seq, and adding the average value Vol_mean of each energy window into an average value sequence Vol_mean_seq;
4.2 For the voltage standard deviation sequence Vol_SD_seq, ascending order sorting is carried out, and an energy window corresponding to the minimum value item is taken as an optimal charging and discharging energy window E best The method comprises the steps of carrying out a first treatment on the surface of the If there are a plurality of optimal charge-discharge energy windows E best The average value sequence Vol_mean_Seq is ordered in a descending order, and then the average value sequence Vol_mean_Seq is ordered in a plurality of optimal charge and discharge energy windows E best Optimal charge-discharge energy window E corresponding to the middle maximum value item best As a final optimum charge-discharge energy window E best 。
Optionally, step 5) is to optimize the charge-discharge energy window E best Mapping to a voltage interval at a specified power of the energy storage system includes an optimal charge-discharge energy window E best The start point of the window is 0% of the SOC, the end point of the window is 100% of the SOC, the start point and the end point of the SOC are determined by recording the charge-discharge real-time equivalent single-core voltage value at a specified power, and the value of the SOC at any point between the start point and the end point of the SOC is determined according to the following formula to window the optimal charge-discharge energy E best Mapping to storageVoltage intervals at the system-specified power:
in the above, PE is the optimal charge-discharge energy window E best The energy contained in the left and right boundary ranges of (a), N is the number of segments of the Wh-OCV curve discretized by the time axis, i n For the current of each segment after discretization, u n For the current voltage of each segment after discretization, Δt is the basic time of discretization of the Wh-OCV curve on the time axis.
In addition, the application also provides an SOC interval calibration system based on the energy window, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the SOC interval calibration method based on the energy window.
Furthermore, the present application provides a computer-readable storage medium having stored therein a computer program programmed or configured to perform the aforementioned energy window-based SOC interval calibration method.
Compared with the prior art, the application has the following advantages: aiming at the problem that the existing calibration algorithm of the SOC section of the energy storage station can not calibrate the whole SOC section of the energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy, the application comprises the steps of charging and discharging the energy storage system with different powers and recording charging and discharging test data; wh integration is carried out on the charge and discharge test data, and a Wh-OCV curve is obtained through fitting; taking an energy integral E of the Wh-OCV curve as an energy window reference, moving an energy window with a width coefficient P in a charge-discharge interval of the Wh-OCV curve, and calculating a voltage standard deviation Vol_SD and a voltage average value Vol_mean of each energy window; sequencing the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, and determining the optimal charge-discharge energy window E under the energy window with the width coefficient of P according to the sequencing result best The method comprises the steps of carrying out a first treatment on the surface of the Energy window E for optimal charge and discharge best Mapping to a voltage interval under the appointed power of the energy storage system, and completing the SOC interval calibration of the energy storage system. By the means, the whole SO of the energy storage station can be obtained based on the chargeable and dischargeable power and the chargeable and dischargeable energyAnd C, the interval is calibrated, so that a relatively accurate assessment means can be provided for the power and capacity of the energy storage station, and an effective reference is provided for scheduling operation.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of the sliding of an energy window in a method according to an embodiment of the application.
Fig. 3 is a graph showing charge and discharge curves of different powers according to an embodiment of the present application.
Fig. 4 is a graph of the charging and discharging interval defining an energy window width factor of 0.95 at 10% pn power.
Detailed Description
As shown in fig. 1, the SOC interval calibration method based on the energy window of the present embodiment includes:
1) Charging and discharging with different powers are carried out on the energy storage system, and charging and discharging test data are recorded;
2) Wh integration is carried out on the charge and discharge test data, and a Wh-OCV curve is obtained through fitting;
3) Taking an energy integral E of the Wh-OCV curve as an energy window reference, moving an energy window with a width coefficient P in a charge-discharge interval of the Wh-OCV curve, and calculating a voltage standard deviation Vol_SD and a voltage average value Vol_mean of each energy window;
4) Sequencing the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, and determining the optimal charge-discharge energy window E under the energy window with the width coefficient of P according to the sequencing result best ；
5) Energy window E for optimal charge and discharge best Mapping to a voltage interval under the appointed power of the energy storage system, and completing the SOC interval calibration of the energy storage system.
In this embodiment, step 1) further includes power limitation caused by upper and lower limits of SOC in the process of releasing EMS charge and discharge from the energy storage system before charging and discharging the energy storage system with different powers, only voltage, current and temperature limitation are reserved, and the battery capacity is completely discharged.
It should be noted that, different powers may be selected according to actual needs, for example, in step 1) in this embodiment, when the energy storage system is charged and discharged with different powers, the charging and discharging with different powers includes three active powers of 10% PN, 50% PN, and 100% PN, where PN refers to the rated active power.
In this embodiment, step 1) performs charging and discharging with different powers on the energy storage system, the charging and discharging process with the same power is uninterrupted, the power is kept constant, charging and discharging test data are recorded at uniform time intervals, and the period is not more than 1 second.
In this embodiment, step 2) further includes the step of comparing Wh-OCV curves corresponding to different powers on the same graph after the Wh-OCV curves are obtained by fitting, and determining the correctness of the battery charge and discharge data and the internal resistance model by checking the coincidence ratio of the three curves. Wh integration is carried out on active power charge and discharge test data of 10% PN, 50% PN and 100% PN, wh-OCV curves under three powers are obtained through fitting, the three Wh-OCV curves are compared on the same graph, and the correctness of the charge and discharge data and the internal resistance model of the battery is determined by checking the coincidence degree of the three curves.
In this embodiment, the step 3) of taking the energy integral E of the Wh-OCV curve as the energy window reference comprises: the energy integral E of the Wh-OCV curve is first calculated according to the following formula:
in the above formula, E is the energy integral of the Wh-OCV curve, N is the number of segments of the Wh-OCV curve discretized in time axis, i n For the current of each segment after discretization, u n The delta t is the basic time of discretization of the Wh-OCV curve on the time axis for the current voltage of each section after discretization; the energy integral E of the Wh-OCV curve is multiplied by different scaling factors P to obtain the energy PE contained within the left and right boundary of the energy window W. According to the above formula, the energy integral E of the Wh-OCV curve is the sum of the energy of the Wh-OCV curve after discretization in time axis.
In this embodiment, step 4) includes:
4.1 Calculating the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, adding the voltage standard deviation Vol_SD of each energy window into a voltage standard deviation sequence Vol_SD_seq, and adding the voltage average value Vol_mean of each energy window into an average value sequence Vol_mean_seq;
4.2 For the voltage standard deviation sequence Vol_SD_seq, ascending order sorting is carried out, and an energy window corresponding to the minimum value item is taken as an optimal charging and discharging energy window E best The method comprises the steps of carrying out a first treatment on the surface of the If there are a plurality of optimal charge-discharge energy windows E best The average value sequence Vol_mean_Seq is ordered in a descending order, and then the average value sequence Vol_mean_Seq is ordered in a plurality of optimal charge and discharge energy windows E best Optimal charge-discharge energy window E corresponding to the middle maximum value item best As a final optimum charge-discharge energy window E best 。
In this embodiment, step 5) is to optimize the charge-discharge energy window E best Mapping to a voltage interval at a specified power of the energy storage system includes an optimal charge-discharge energy window E best The start point of the window is 0% of the SOC, the end point of the window is 100% of the SOC, the start point and the end point of the SOC are determined by recording the charge-discharge real-time equivalent single-core voltage value at a specified power, and the value of the SOC at any point between the start point and the end point of the SOC is determined according to the following formula to window the optimal charge-discharge energy E best Mapping to voltage intervals at a specified power of the energy storage system:
in the above, PE is the optimal charge-discharge energy window E best The energy contained in the left and right boundary ranges of (a), N is the number of segments of the Wh-OCV curve discretized by the time axis, i n For the current of each segment after discretization, u n For the current voltage of each segment after discretization, Δt is the basic time of discretization of the Wh-OCV curve on the time axis. Energy window E for optimal charge and discharge best The voltage interval mapped to the specified power corresponds to Vmax at the right end of the energy window and Vmin at the left end of the energy window, and the schematic diagram is shown in FIG. 2. And recording the charge-discharge real-time equivalent single-core voltage value under the specified power, and determining the start point and the end point of the SOC.
In the embodiment, the scale of the energy storage power station is 10MW/20MWh, an electrochemical energy storage technology is adopted, a lithium iron phosphate battery is selected as a battery, and parameters of single batteries are shown in table 1. And multiplying the charging and discharging process of the whole station by the corresponding proportional coefficient, converting the charging and discharging process of the equivalent single battery, and calibrating the SOC interval by utilizing the data of the equivalent single battery.
Table 1 basic parameters of the battery.
| Parameters (parameters) | Numerical value |
| Nominal capacity (0.5 c,25±3℃) (nominal capacity) | 120(Ah) |
| Rated voltage (rated voltage) | 3.2(V) |
| Maximum charging voltage (max charge voltage) | 3.65(V) |
| Discharge Cut-off voltage (Cut-off voltage) | 2.5(V) |
| Standard charging current (standard charge current) | 120(A) |
| Standard discharge current (standard discharge current) | 120(A) |
And charging and discharging with active power of 10% PN, 50% PN and 100% PN, adopting Wh integration instead of Ah integration, fitting a test curve to obtain Wh-OCV curves under different powers, and checking the correctness of internal resistance models and data in the charging and discharging processes. The figures show different power charge-discharge curves and fitted Wh-OCV curves. In the figure, the Wh-OCV curves fitted by the three charge and discharge powers are basically overlapped, the charge and discharge curves are respectively positioned at two sides of the Wh-OCV curves, and the effectiveness of charge and discharge data can be checked by adopting an internal resistance model. According to the Wh-OCV curve, calculating the energy integral E of the Wh-OCV curve, setting the width coefficient to be 0.95, and calculating to obtain the optimal charge-discharge interval under the corresponding energy window. The calculation results are shown in fig. 3 to 4. Fig. 3 is a graph showing charge and discharge curves of different powers according to an embodiment of the present application. Fig. 4 is a graph of the charging and discharging interval defining an energy window width factor of 0.95 at 10% pn power. Referring to fig. 3 to fig. 4, it can be known that the SOC interval calibration method based on the energy window in this embodiment can calibrate the whole SOC interval of the energy storage station based on the chargeable/dischargeable power and the chargeable/dischargeable energy, and can provide an accurate evaluation means for the power and the capacity of the energy storage station, so as to provide an effective reference for the scheduling operation.
In addition, the embodiment also provides an SOC interval calibration system based on an energy window, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the SOC interval calibration method based on the energy window.
Furthermore, the present embodiment also provides a computer-readable storage medium having stored therein a computer program programmed or configured to perform the energy window-based SOC interval calibration method.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the protection scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (8)
1. The SOC interval calibration method based on the energy window is characterized by comprising the following steps of:
1) Charging and discharging with different powers are carried out on the energy storage system, and charging and discharging test data are recorded;
2) Wh integration is carried out on the charge and discharge test data, and a Wh-OCV curve is obtained through fitting;
3) Integration of energy in Wh-OCV curveEAs the energy window reference, the width coefficient is set asPThe energy window of (2) moves in the charge-discharge interval of the Wh-OCV curve, and the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window are calculated;
4) Sequencing the standard deviation Vol_SD and average voltage Vol_mean of each energy window, and determining the width coefficient asPOptimal charge-discharge energy window under energy window of (a)E best Comprising: 4.1 Calculating the voltage standard deviation Vol_SD and the voltage average value Vol_mean of each energy window, adding the voltage standard deviation Vol_SD of each energy window into a voltage standard deviation sequence Vol_SD_seq, and adding the voltage average value Vol_mean of each energy window into an average value sequence Vol_mean_seq;4.2 For the voltage standard deviation sequence Vol_SD_seq, ascending order sorting is carried out, and an energy window corresponding to the minimum value item is taken as an optimal charging and discharging energy windowE best The method comprises the steps of carrying out a first treatment on the surface of the If there are a plurality of optimal charge-discharge energy windowsE best The average value sequence Vol_mean_seq is ordered in a descending order, and then the average value sequence Vol_mean_seq is subjected to a plurality of optimal charge and discharge energy windowsE best Optimal charging and discharging energy window corresponding to middle maximum value itemE best As a final optimum charge-discharge energy windowE best ；
5) Energy window for optimal charging and dischargingE best Mapping to a voltage interval under the appointed power of the energy storage system to finish the SOC interval calibration of the energy storage system; energy window for optimal charging and dischargingE best Mapping to voltage intervals at a specified power of an energy storage system includes windowing optimal charge and discharge energyE best The start point of the window is 0% of the SOC, the end point of the window is 100% of the SOC, the start point and the end point of the SOC are determined by recording the charge-discharge real-time equivalent single-core voltage value at a specified power, and the value of the SOC at any point between the start point and the end point of the SOC is determined according to the following formula to window the optimal charge-discharge energyE best Mapping to voltage intervals at a specified power of the energy storage system:
，
in the above-mentioned method, the step of,PEenergy window for optimal charge and dischargeE best Energy contained in the left and right boundary ranges of (c),Nfor the number of segments the Wh-OCV curve discretizes on the time axis,i n for the current of each segment after discretization,u n for the current voltage of each segment after discretization,the basic time for discretization of the Wh-OCV curve in the time axis.
2. The method for calibrating the SOC interval based on the energy window according to claim 1, wherein the step 1) further comprises the step of releasing power limitation caused by the upper and lower limits of the SOC in the EMS charging and discharging process to the energy storage system before charging and discharging the energy storage system with different powers, wherein only voltage, current and temperature limitation are reserved, and the battery capacity is completely discharged.
3. The method for calibrating the SOC interval based on the energy window according to claim 2, wherein in the step 1), when the energy storage system is charged and discharged with different powers, the charged and discharged with different powers include three active powers of 10% PN, 50% PN and 100% PN, wherein PN refers to rated active power.
4. The SOC interval calibration method based on an energy window as claimed in claim 3, wherein, in the step 1), when charging and discharging of different powers are performed on the energy storage system, the charging and discharging process of the same power is uninterrupted and the power is kept constant, and the charging and discharging test data is recorded by adopting uniform time intervals, and the period is not more than 1 second.
5. The SOC interval calibration method based on the energy window of claim 1, wherein step 2) after the Wh-OCV curves are obtained by fitting, further comprising a step of comparing Wh-OCV curves corresponding to different powers on the same graph, and determining correctness of battery charge and discharge data and an internal resistance model by checking coincidence of the three curves.
6. The energy window-based SOC interval calibration method of claim 1, wherein the energy integration in step 3) with a Wh-OCV curveEAs energy window references include: first, the energy integral of the Wh-OCV curve is calculated according to the following formulaE：
，
In the above-mentioned method, the step of,Efor the energy integration of the Wh-OCV curve,Nfor the number of segments the Wh-OCV curve discretizes on the time axis,i n for the current of each segment after discretization,u n for the current voltage of each segment after discretization,basic time discretized by the Wh-OCV curve on the time axis; integrating the energy of the Wh-OCV curveEMultiplying by different scaling factorsPObtaining energy windowWThe energy contained in the left and right boundary ranges of (2) is +.>。
7. An energy window based SOC interval calibration system comprising a microprocessor and a memory interconnected, characterized in that the microprocessor is programmed or configured to perform the steps of the energy window based SOC interval calibration method of any of claims 1 to 6.
8. A computer-readable storage medium, wherein the computer-readable storage medium has stored therein a computer program programmed or configured to perform the energy window-based SOC interval calibration method of any of claims 1 to 6.
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