CN114035052B - A SOC interval calibration method, system and medium based on energy window - Google Patents

Abstract

The invention discloses an SOC interval calibration method, system and medium based on energy windows. The invention includes charging and discharging the energy storage system with different powers, recording the charging and discharging test data; performing Wh integration on the charging and discharging test data, and fitting Obtain the Wh-OCV curve; use the energy integral E of the Wh-OCV curve as the energy window benchmark, move the energy window with the width coefficient P in the charge and discharge interval of the Wh-OCV curve, and calculate the voltage standard deviation Vol_SD and voltage of each energy window Average value Vol_Mean; determine the optimal charge and discharge energy window E _best under the energy window with width coefficient P based on the sorting result, and then map it to the voltage range under the specified power of the energy storage system. The present invention can calibrate the overall SOC interval of the energy storage station based on the rechargeable power and rechargeable energy, ensuring a relatively accurate evaluation method for the power and capacity of the energy storage station, and providing an effective reference for dispatching operations.

Description

A SOC interval calibration method, system and medium based on energy window

Technical field

The invention belongs to the technical field of electrochemical energy storage, and specifically relates to an SOC interval calibration method, system and medium based on energy windows.

Background technique

The large-scale development of electrochemical energy storage has put forward increasingly higher requirements for the functions of control equipment. As the core function of the battery management system (BMS), state of charge (SOC) calibration is important for evaluating battery status and providing energy management systems ( EMS) plays a key role in rationally allocating power to the power conversion system (PCS) to control power and providing accurate energy data to the dispatch department. The SOC calibration algorithm has received increasing attention. At present, neither national standards nor industry standards specify the requirements for energy storage SOC calibration algorithms. Each equipment manufacturer has proposed its own SOC calibration algorithm suitable for its own product characteristics. Judging from the existing literature, SOC calibration is often used in the field of power vehicles. The SOC calibration algorithm is mainly based on the ampere-hour integration method, the open circuit voltage curve (OCV) method, the Kalman filter method, etc., and the ampere-hour integration method is often used as the basic method. Use other methods to make corrections. The usual approach of this type of algorithm is to first set a battery model during the SOC calibration process, then charge and discharge with a constant current, and then compare the test data with the model simulation data to verify the validity of the model. This type of algorithm takes into account the resistance and capacitance effects of the battery model. There are first-order and second-order models based on the parallel relationship between capacitance and resistance. The higher the order, the more adjustable dimensions there are and can fit the experimental data more accurately. The test objects of this type of algorithm are usually fewer battery cells, and the consistency of the battery cells is better. In addition, the constant current charging and discharging method is used, and the overall test conditions are relatively stable.

At present, the number of cells in energy storage stations is tens of thousands, which is several orders of magnitude higher than the number of cells in the power vehicle industry. The sharp increase in the number of cells has brought higher control complexity, and changing application scenarios have brought about different needs. For energy storage stations, it is necessary to consider the power and energy needs of the power grid, but power batteries do not need to be considered, which reflects the different requirements for SOC calibration calculations. The SOC calibration algorithm in the laboratory is not completely suitable for application in energy storage power stations.

First, the applicable battery models are different. The energy storage station has a huge number of cells. The difference in parameters of the single cell model will be weakened by the collective effect of a large number of cells, and will show smooth external characteristics. The first-order and second-order models of single-cell batteries are not suitable for the entire charging and discharging process of energy storage stations. The more simplified resistance model can better reflect the battery characteristics of energy storage power stations. Secondly, the accuracy requirements for power and energy are different. Energy storage power stations mainly face power grid scenarios, and their main requirements are that based on SOC, they should be able to provide the maximum charging and discharging power and charging and discharging energy (time) of the energy storage power station to meet the dispatching operation needs. The current power battery SOC calibration method is basically based on the consideration of charge, not the energy perspective. It is believed that the chemical action of the battery during the charge and discharge process can release the amount of charge, but it is impossible to completely release energy, because the released energy changes with time. It changes with external working conditions, so it is rarely used in practice. However, the actual power grid dispatch operation does not refer to the charge amount, but to the power and energy. Even if the charging and discharging energy changes with the external working conditions, as long as the corresponding margin is given, a more feasible operation reference can be provided. Finally, corresponding to different application scenarios, the SOC definition will be different. Different from the power battery field, which uses the integral of electricity to calculate SOC, the power grid scenario uses energy integral to calculate SOC. Considering the grid's deterministic demand for power and energy, the calibration of the SOC's available range needs to be considered. Such a demand has not been seen in the power battery field.

Therefore, according to the different application scenarios and needs of the power grid and the power battery industry, a SOC calibration algorithm is needed to calibrate the overall SOC range of the energy storage station based on rechargeable power and rechargeable energy to ensure that the power and capacity of the energy storage station can be calibrated There are relatively accurate evaluation methods to provide effective reference for scheduling operations.

Contents of the invention

Technical problems to be solved by the present invention: In view of the above-mentioned problems of the existing technology, an SOC interval calibration method, system and medium based on the energy window are provided. The present invention can measure the overall energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy. Calibrating the SOC interval ensures a more accurate evaluation method for the power and capacity of the energy storage station, providing an effective reference for dispatching operations.

In order to solve the above technical problems, the technical solution adopted by the present invention is:

A SOC interval calibration method based on energy windows, including:

1) Charge and discharge the energy storage system with different powers and record the charge and discharge test data;

2) Perform Wh integration on the charge and discharge test data, and fit the Wh-OCV curve;

3) Using the energy integral E of the Wh-OCV curve as the energy window benchmark, move the energy window with a width coefficient P in the charge and discharge interval of the Wh-OCV curve, and calculate the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window;

4) Sort the voltage standard deviation Vol_SD and voltage mean Vol_Mean of each energy window, and determine the optimal charge and discharge energy window E _best under the energy window with a width coefficient of P based on the sorting results;

5) Map the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system to complete the SOC interval calibration of the energy storage system.

Optionally, step 1) before charging and discharging the energy storage system with different powers also includes releasing the power restrictions caused by the upper and lower SOC limits of the EMS charging and discharging process on the energy storage system, leaving only the voltage, current, and temperature limits. The battery capacity is drained.

Optionally, when charging and discharging the energy storage system with different powers in step 1), the charging and discharging of different powers include three active powers of 10% PN, 50% PN, and 100% PN, where PN refers to the rated active power.

Optionally, step 1) When charging and discharging the energy storage system with different powers, the charging and discharging process of the same power is uninterrupted and the power remains constant, and the charging and discharging test data is recorded at uniform time intervals, and The period does not exceed 1 second.

Optionally, step 2), after fitting the Wh-OCV curve, also includes comparing Wh-OCV curves corresponding to different powers on the same graph, and determining the battery charge and discharge data and internal content by checking the overlap of the three curves. steps to prevent model correctness.

Optionally, using the energy integral E of the Wh-OCV curve as the energy window benchmark in step 3) includes: first calculating the energy integral E of the Wh-OCV curve 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 according to the time axis, i _n is the current of each segment after discretization, and u _n is the current voltage of each segment after discretization. , Δt is the basic time for discretization of the Wh-OCV curve according to the time axis; multiply the energy integral E of the Wh-OCV curve by different proportional coefficients P to obtain the energy contained in the left and right boundaries of the energy window W as PE.

Optionally, step 4) includes:

4.1) Calculate the voltage standard deviation Vol_SD and voltage average value Vol_Mean of each energy window, add the voltage standard deviation Vol_SD of each energy window to the voltage standard deviation sequence Vol_SD_Seq, and add the average value Vol_Mean of each energy window to the average value sequence Vol_Mean_Seq;

4.2) Sort the voltage standard deviation sequence Vol_SD_Seq in ascending order, and take the energy window corresponding to the minimum value item as the optimal charge and discharge energy window E _best ; if there are multiple optimal charge and discharge energy windows E _best , then use the average value sequence Vol_Mean_Seq Sort in descending order, and then select the optimal charge and discharge energy window E _best corresponding to the maximum value item among multiple optimal charge and discharge energy windows E _best as the final optimal charge and discharge energy window E _best .

Optionally, step 5) when mapping the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system, includes setting the starting point of the optimal charge and discharge energy window E _best as 0% of SOC, and the end of the window For 100% of SOC, record the real-time equivalent single-core voltage value of charge and discharge under the specified power to determine the starting point and end point of SOC, and determine the value of SOC at any point between the starting point and end point of SOC according to the following formula to optimize The charge and discharge energy window E _best is mapped to the voltage range under the specified power of the energy storage system:

In the above formula, PE is the energy contained in the left and right boundaries of the optimal charge and discharge energy window E _best , N is the number of segments of the Wh-OCV curve discretized according to the time axis, i _n is the current of each segment after discretization, u _n is the current voltage of each segment after discretization, and Δt is the basic time for discretizing the Wh-OCV curve according to the time axis.

In addition, the present invention also provides an energy window-based SOC interval calibration system, which includes an interconnected microprocessor and a memory. The microprocessor is programmed or configured to execute the steps of the aforementioned energy window-based SOC interval calibration method.

In addition, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute the aforementioned energy window-based SOC interval calibration method.

Compared with the existing technology, the present invention has the following advantages: Aiming at the problem that the existing energy storage station SOC interval calibration algorithm cannot calibrate the overall SOC interval of the energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy, the present invention includes Charge and discharge the energy storage system with different powers, and record the charge and discharge test data; perform Wh integration on the charge and discharge test data, and fit the Wh-OCV curve; use the energy integral E of the Wh-OCV curve as the energy window benchmark, and calculate the width The energy window with coefficient P moves in the charge and discharge interval of the Wh-OCV curve, and the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window are calculated; the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window are sorted, and According to the sorting results, determine the optimal charge and discharge energy window E _best under the energy window with a width coefficient of P; map the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system to complete the SOC interval of the energy storage system Calibration. Through the above means, the overall SOC range of the energy storage station can be calibrated based on the rechargeable power and rechargeable energy, ensuring a more accurate evaluation method for the power and capacity of the energy storage station, and providing an effective reference for dispatching operations.

Description of drawings

Figure 1 is a basic flow diagram of the method according to the embodiment of the present invention.

Figure 2 is a schematic diagram of energy window sliding in the method according to the embodiment of the present invention.

Figure 3 is a graph of charge and discharge curves of different powers in the embodiment of the present invention.

Figure 4 shows the charging and discharging interval with a limited energy window width coefficient of 0.95 under 10% Pn power.

Detailed ways

As shown in Figure 1, the SOC interval calibration method based on energy windows in this embodiment includes:

1) Charge and discharge the energy storage system with different powers and record the charge and discharge test data;

2) Perform Wh integration on the charge and discharge test data, and fit the Wh-OCV curve;

3) Using the energy integral E of the Wh-OCV curve as the energy window benchmark, move the energy window with a width coefficient P in the charge and discharge interval of the Wh-OCV curve, and calculate the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window;

4) Sort the voltage standard deviation Vol_SD and voltage mean Vol_Mean of each energy window, and determine the optimal charge and discharge energy window E _best under the energy window with a width coefficient of P based on the sorting results;

5) Map the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system to complete the SOC interval calibration of the energy storage system.

In this embodiment, step 1) before charging and discharging the energy storage system with different powers also includes releasing the power restrictions caused by the upper and lower limits of SOC during the EMS charging and discharging process of the energy storage system, leaving only the voltage, current, and temperature limits. Discharge the battery capacity.

It should be noted that different powers can be selected according to actual needs. For example, in step 1) of this embodiment, when charging and discharging the energy storage system with different powers, the charging and discharging of different powers include 10% PN, 50% PN, and 100% PN. Three types of active power, where PN refers to the rated active power.

In this embodiment, when step 1) charges and discharges the energy storage system with different powers, the charge and discharge process of the same power is uninterrupted and the power remains constant, and the charge and discharge test data are recorded at uniform time intervals. And the period does not exceed 1 second.

In this embodiment, after step 2) is fitted to obtain the Wh-OCV curve, it also includes comparing the Wh-OCV curves corresponding to different powers on the same graph, and determining the battery charge and discharge data by checking the overlap of the three curves. Steps to Correctness of Internal Resistance Model. Perform Wh integration on the 10% PN, 50% PN, and 100% PN active power charging and discharging test data, and fit the Wh-OCV curves under the three powers. Compare the three Wh-OCV curves on the same graph. Through calibration, Check the overlap of the three curves to determine the correctness of the battery charge and discharge data and internal resistance model.

In this embodiment, using the energy integral E of the Wh-OCV curve as the energy window benchmark in step 3) includes: first calculating the energy integral E of the Wh-OCV curve 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 according to the time axis, i _n is the current of each segment after discretization, and u _n is the current voltage of each segment after discretization. , Δt is the basic time for discretization of the Wh-OCV curve according to the time axis; multiply the energy integral E of the Wh-OCV curve by different proportional coefficients P to obtain the energy contained in the left and right boundaries of the energy window W as PE. According to the above formula, it can be seen that the energy integral E of the Wh-OCV curve is the sum of the energies after the Wh-OCV curve is discretized along the time axis.

In this embodiment, step 4) includes:

4.1) Calculate the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window, add the voltage standard deviation Vol_SD of each energy window to the voltage standard deviation sequence Vol_SD_Seq, and add the voltage average Vol_Mean of each energy window to the average value sequence Vol_Mean_Seq;

4.2) Sort the voltage standard deviation sequence Vol_SD_Seq in ascending order, and take the energy window corresponding to the minimum value item as the optimal charge and discharge energy window E _best ; if there are multiple optimal charge and discharge energy windows E _best , then use the average value sequence Vol_Mean_Seq Sort in descending order, and then select the optimal charge and discharge energy window E _best corresponding to the maximum value item among multiple optimal charge and discharge energy windows E _best as the final optimal charge and discharge energy window E _best .

In this embodiment, step 5) when mapping the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system includes setting the starting point of the optimal charge and discharge energy window E _best to 0% of SOC, and the window The end is 100% of the SOC. Record the real-time equivalent single-core voltage value of charge and discharge under the specified power to determine the starting point and end point of the SOC, and determine the SOC value at any point between the starting point and the end point of the SOC according to the following formula to convert the final The optimal charge and discharge energy window E _best is mapped to the voltage range under the specified power of the energy storage system:

In the above formula, PE is the energy contained in the left and right boundaries of the optimal charge and discharge energy window E _best , N is the number of segments of the Wh-OCV curve discretized according to the time axis, i _n is the current of each segment after discretization, u _n is the current voltage of each segment after discretization, and Δt is the basic time for discretizing the Wh-OCV curve according to the time axis. The optimal charge and discharge energy window E _best is mapped to the voltage interval under the specified power. The right end of the energy window corresponds to Vmax, and the left end of the energy window corresponds to Vmin. The schematic diagram is shown in Figure 2. Record the real-time equivalent single-core voltage value of charge and discharge under the specified power to determine the starting point and end point of SOC.

In this embodiment, the scale of the energy storage power station is 10MW/20MWh, which adopts electrochemical energy storage technology. The battery is a lithium iron phosphate battery. The parameters of the single-cell battery are as shown in Table 1. The charging and discharging process of the entire station is converted into the charging and discharging process of the equivalent single-cell battery by multiplying the corresponding proportional coefficient, and the equivalent single-cell battery data is used for SOC interval calibration.

Table 1 Basic parameters of the battery.

parameter numerical value Nominal capacity (0.5C, 25±3℃)(nominal capacity) 120(Ah) rated voltage 3.2(V) maximum charge voltage(max charge voltage) 3.65(V) Discharge cut-off voltage (Cut-off voltage_) 2.5(V) standard charge current 120(A) standard discharge current 120(A)

Charge and discharge with 10% PN, 50% PN, and 100% PN active power. Instead of using Ah integral, use Wh integral. Fit the test curve to obtain Wh-OCV curves under different powers to verify the charge. The internal resistance model and data of the discharge process are correct. The figure shows different power charge and discharge curves and the fitted Wh-OCV curve. It can be seen from the figure that the Wh-OCV curves fitted by the three charge and discharge powers basically overlap. The charge and discharge curves are located on both sides of the Wh-OCV curve. The internal resistance model can be used to check the validity of the charge and discharge data. According to the Wh-OCV curve, calculate the energy integral E of the Wh-OCV curve, set the width coefficient to 0.95, and calculate the optimal charge and discharge interval under the corresponding energy window. The calculation results are shown in Figures 3 and 4. Figure 3 is a graph of charge and discharge curves of different powers in the embodiment of the present invention. Figure 4 shows the charging and discharging interval with a limited energy window width coefficient of 0.95 under 10% Pn power. Referring to Figures 3 and 4, it can be seen that the SOC interval calibration method based on the energy window of this embodiment can calibrate the overall SOC interval of the energy storage station based on the chargeable and dischargeable power and the chargeable and dischargeable energy, and can have a comparatively good effect on the power and capacity of the energy storage station. Accurate evaluation methods provide effective reference for scheduling operations.

In addition, this embodiment also provides an energy window-based SOC interval calibration system, including an interconnected microprocessor and a memory. The microprocessor is programmed or configured to execute the steps of the energy window-based SOC interval calibration method. .

In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute the energy window-based SOC interval calibration method.

Those skilled in the art will understand that embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines 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, etc.) 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 process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes 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 device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in one process or multiple processes of the flowchart and/or one block or multiple blocks of the block diagram. These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram. These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above-mentioned embodiments. All technical solutions that fall under the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications may be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims (8)

1. A SOC interval calibration method based on energy windows, which is characterized by including: 1) Charge and discharge the energy storage system with different powers and record the charge and discharge test data; 2) Perform Wh integration on the charge and discharge test data, and fit the Wh-OCV curve; 3) Using the energy integral E of the Wh-OCV curve as the energy window benchmark, move the energy window with the width coefficient P in the charge and discharge interval of the Wh-OCV curve, and calculate the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window; 4) Sort the voltage standard deviation Vol_SD and voltage average Vol_Mean of each energy window, and determine the optimal charge and discharge energy window E _best under the energy window with a width coefficient of P based on the sorting results, including: 4.1) Calculate each energy window The voltage standard deviation Vol_SD and voltage average Vol_Mean are added to the voltage standard deviation Vol_SD of each energy window to the voltage standard deviation sequence Vol_SD_Seq, and the voltage average Vol_Mean of each energy window is added to the average value sequence Vol_Mean_Seq; 4.2) For the voltage standard deviation sequence Vol_SD_Seq Sort in ascending order, take the energy window corresponding to the minimum value item as the optimal charge and discharge energy window E _best ; if there are multiple optimal charge and discharge energy windows E _best , sort the average sequence Vol_Mean_Seq in descending order, and then sort the optimal charge and discharge energy windows E best The optimal charge and discharge energy window E _best corresponding to the maximum value item in the optimal charge and discharge energy window E _best is taken as the final optimal charge and discharge energy window E _best ; 5) Map the optimal charge and discharge energy window E _best to the voltage range under the specified power of the energy storage system to complete the SOC interval calibration of the energy storage system; map the optimal charge and discharge energy window E _best to the voltage range under the specified power of the energy storage system. The voltage interval includes setting the starting point of the optimal charge and discharge energy window E _best as 0% of SOC, and the end of the window as 100% of SOC. Record the real-time equivalent single-core voltage value of charge and discharge under the specified power to determine the starting point and end point of SOC. , and determine the value of SOC at any point between the starting point and end point of SOC according to the following formula to map the optimal charge and discharge energy window E _best to the voltage interval under the specified power of the energy storage system: , In the above formula, PE is the energy contained in the left and right boundaries of the optimal charge and discharge energy window E _best , N is the number of segments of the Wh-OCV curve discretized according to the time axis, i _n is the current of each segment after discretization, u _n is the current voltage of each segment after discretization, is the basic time for discretizing the Wh-OCV curve along the time axis. 2. The SOC interval calibration method based on energy windows according to claim 1, characterized in that, before step 1) charging and discharging the energy storage system with different powers, it also includes releasing the EMS charging and discharging process of the energy storage system. The power limitation caused by the upper and lower limits of SOC only retains the voltage, current, and temperature limits, which completely reduces the battery capacity. 3. The SOC interval calibration method based on energy window according to claim 2, characterized in that, in step 1) when charging and discharging the energy storage system with different powers, the charging and discharging of different powers include 10% PN and 50% PN. , 100% PN three types of active power, where PN refers to the rated active power. 4. The SOC interval calibration method based on energy windows according to claim 3, characterized in that, in step 1) when charging and discharging the energy storage system with different powers, the charging and discharging process of the same power is uninterrupted and the power remains constant. And the charging and discharging test data is recorded at uniform time intervals, and the period does not exceed 1 second. 5. The SOC interval calibration method based on energy window according to claim 1, characterized in that, after step 2) fitting to obtain the Wh-OCV curve, it also includes performing Wh-OCV curves corresponding to different powers on the same graph. Compare and determine the correctness of the battery charge and discharge data and internal resistance model by checking the overlap of the three curves. 6. The SOC interval calibration method based on the energy window according to claim 1, characterized in that using the energy integral E of the Wh-OCV curve as the energy window benchmark in step 3) includes: first calculating the Wh-OCV curve according to the following formula The energy integral E : , 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 according to the time axis, i _n is the current of each segment after discretization, u _n is the current voltage of each segment after discretization , is the basic time for discretizing the Wh-OCV curve according to the time axis; multiply the energy integral E of the Wh-OCV curve by different proportional coefficients P to obtain the energy contained in the left and right boundaries of the energy window W as/> . 7. An energy window-based SOC interval calibration system, comprising an interconnected microprocessor and a memory, characterized in that the microprocessor is programmed or configured to perform the energy-based calibration of any one of claims 1 to 6. Steps of window SOC interval calibration method. 8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer programmed or configured to perform the energy window-based SOC interval calibration method according to any one of claims 1 to 6. program.