CN116699419A - SOC, SOH and RUL joint estimation method of energy storage equipment - Google Patents
SOC, SOH and RUL joint estimation method of energy storage equipment Download PDFInfo
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- 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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- 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
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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Abstract
The application belongs to the field of energy storage system management, and discloses a method for jointly estimating SOC, SOH and RUL of energy storage equipment, which comprises the following steps: determining a charge-discharge period according to the operation record of the energy storage equipment; calculating the charge and discharge efficiency of the energy storage device in each charge and discharge period, so as to obtain the device electric quantity and the maximum electric quantity corresponding to each operation record; then calculating the charge and discharge cycle times of the energy storage equipment, and acquiring the maximum electric quantity, the equipment operation time length and the charge and discharge efficiency corresponding to each charge and discharge cycle, so as to obtain predicted data corresponding to SOH, RUL and the charge and discharge efficiency; and finally, acquiring the SOC prediction data by utilizing the SOH prediction data and the charge-discharge efficiency prediction data. According to the application, by combining the theoretical attenuation curve of the battery core and the historical capacity of the equipment, the actual equipment capacity attenuation curve is fitted, and the equipment capacity attenuation curve is corrected based on the change of the SOC, so that more accurate SOH and RUL predicted values can be obtained with lower calculation cost.
Description
Technical Field
The application relates to a method for jointly estimating SOC, SOH and RUL of energy storage equipment, and belongs to the field of energy storage system management.
Background
The energy storage device is generally composed of a single or multiple cells connected in series or in parallel, and the performance of the cells determines the relevant performance of the energy storage device, for example, the electric quantity of the energy storage device at a certain time is determined by the electric quantity of all the cells in the energy storage device, and each cell has a certain difference in performance, such as inconsistent initial electric quantity and maximum electric quantity, due to factors such as production process and the like. Meanwhile, in the operation process of the energy storage equipment, the electric quantity of the battery cells is attenuated along with the increase of the operation time, and the attenuation conditions among different battery cells are possibly inconsistent, so that a theoretical attenuation curve formula cannot be directly used. The actual service life of the energy storage equipment may have a certain gap from the theoretical service life, which is not beneficial to equipment maintenance time estimation and the like.
The current relevant projected approaches to state of charge (SOC), state of health (SOH) and Remaining Useful Life (RUL) of energy storage devices are largely divided into four categories.
1. A method based on characterization parameters. And estimating the SOC of the battery cell through electrochemical performance indexes of the battery cell, such as impedance spectrum of the battery cell, and the like, and then confirming the residual capacity of the battery cell according to a discharge experiment method and the like, so as to estimate the SOH of the battery cell, and obtaining RUL according to an attenuation curve. The method has higher accuracy, but the main problems of the method are that: long-time detection is required for related professional equipment to obtain related results, and the method is only suitable for specific environments such as laboratories.
2. Ampere-hour integration method. And obtaining the charge capacity through integration of the current value, obtaining the SOC of the battery before and after charge and discharge according to an OCV-SOC table to calculate the variation of the SOC, and finally dividing the charge capacity by the variation of the SOC to obtain the capacity of the battery core and SOH. This method is the mainstream method at present, but there are some problems such as that the OCV-SOC meter requires the cell to stand still for a sufficient time, and the requirement for the current sensor is high. In the actual operation process of the energy storage device, relevant conditions cannot be met in many cases, the OCV-SOC table changes along with SOH, the OCV-SOC table changes along with the performance attenuation of the battery cell, but the OCV-SOC table cannot be updated based on SOH in normal cases, so that the deviation between the SOC of the energy storage device and the SOH estimation occurs, and further RUL estimation errors are caused.
3. Model-based methods. And (3) through establishing an equivalent circuit and other cell physical models, combining Kalman filtering and other algorithms, and carrying out equivalent estimation on the SOC, SOH and RUL of the cell based on actual data. The method is also a more common method at present, but the method has higher calculation cost and depends on the accuracy of the model.
4. A data-driven based method. And (3) estimating the SOC, SOH and RUL of the battery cells by establishing and training a model which directly maps the data such as the current, voltage and temperature of the battery cells with the SOC, SOH and RUL of the battery cells. The method has higher result precision, but has the problems of high calculation cost, high requirement on training data, lower robustness and the like.
Based on the problems existing in the estimation of the SOC, SOH and RUL of the existing energy storage device, it is necessary to study the method for obtaining higher accuracy and robustness and having lower calculation cost to perform the estimation of the relevant performance of the energy storage device.
Disclosure of Invention
The application provides a combined estimation method of SOC, SOH and RUL of energy storage equipment, which is used for predicting relevant performance indexes based on capacity change of the energy storage equipment, so that the calculation cost is reduced and the accuracy of prediction estimation is improved.
The method for jointly estimating the SOC, SOH and RUL of the energy storage equipment is characterized by at least comprising the following steps:
acquiring all historical data of energy storage equipment;
acquiring an operation record of the energy storage equipment based on the historical data;
setting a charge-discharge period of the energy storage equipment, and determining the charge-discharge period according to the operation record of the energy storage equipment;
calculating the charge and discharge efficiency of the energy storage device in each charge and discharge period;
calculating the equipment electric quantity and the maximum electric quantity corresponding to each operation record in a charge-discharge state by using the charge-discharge efficiency of the energy storage equipment in each charge-discharge period;
according to the operation record of the energy storage equipment, calculating the charge-discharge cycle times corresponding to each charge-discharge cycle of the energy storage equipment, and obtaining the maximum electric quantity, the equipment operation duration and the charge-discharge efficiency corresponding to each charge-discharge cycle;
acquiring SOH prediction data, RUL prediction data and charge and discharge efficiency prediction data by using the acquired maximum electric quantity, equipment operation time length, charge and discharge efficiency and a cell theoretical attenuation formula corresponding to each charge and discharge cycle;
and acquiring SOC prediction data by using the SOH prediction data and the charge-discharge efficiency prediction data.
Optionally, the charge-discharge cycle of the energy storage device is set as follows:
and starting charging from the lower limit value of the SOC of the energy storage device to a certain value of the SOC, and discharging to the period of the lower limit value of the SOC of the energy storage device.
Optionally, determining a charge-discharge cycle according to an operation record of the energy storage device includes:
judging a charge-discharge period by using a voltage minimum value v_min_end at the end of recording in the operation record of the energy storage equipment;
preferably, in the operation record of the energy storage device, if the voltage minimum value v_min_end at the end of the record is smaller than the set cut-off voltage of the electric core discharge, the current charge-discharge cycle is considered to be ended, and the next charge-discharge cycle is started.
Optionally, the method for calculating the electric quantity of the device corresponding to each operation record in the charge and discharge state includes:
setting the initial value of the electric quantity of the equipment to be 0, setting the maximum electric quantity of the energy storage equipment to be a null value NA,
the calculation formula of the electric quantity of the device in the charging state is shown as formula (1):
（1）
the calculation formula of the electric quantity of the equipment in the discharging state is shown in the formula (2):
（2）
wherein ch_accumlate_e is the electric quantity of the device, delta_e electric quantity variable quantity, and battery_eff_1 is the charge and discharge efficiency of the energy storage device in the corresponding charge and discharge period; for charging recording, if the voltage minimum value v_min_sta at the beginning of recording is smaller than the set initial voltage of full charge of the battery core and the voltage maximum value v_max_end at the end of recording is larger than or equal to the cut-off voltage of charging of the battery core, the recorded electric quantity delta_e is considered to be the maximum electric quantity of the current energy storage device and the electric quantity ch_accumlate_e of the recording device is equal to the electric quantity delta_e; if the voltage maximum value v_max_end at the end of recording is larger than or equal to the battery cell charging cut-off voltage, the device electric quantity ch_accumlate_e at the moment is considered to be the maximum electric quantity of the current energy storage device;
for discharge recording, if the maximum voltage v_max_sta at the beginning of recording is greater than the full discharge voltage of the battery cell and the minimum voltage v_min_end at the end of recording is less than or equal to the cut-off voltage of the discharge of the battery cell, then the value obtained by dividing the recorded electric quantity variation delta_e by the charge-discharge efficiency battery_eff_1 of the energy storage device in the corresponding charge-discharge period is considered as the maximum electric quantity of the current energy storage device, and ch_accumlate_e is reset to 0; if only the voltage minimum v_min_end at the end of recording is equal to or less than the cell discharge cutoff voltage, ch_accumlate_e is reset to 0.
Optionally, before obtaining the maximum electric quantity, the equipment operation duration and the charge-discharge efficiency corresponding to each charge-discharge cycle, the method further includes:
deleting invalid data in the operation record of the energy storage equipment;
preferably, the invalid data refers to data that does not satisfy the theoretical charge-discharge efficiency of the battery.
Optionally, the method for calculating the charge-discharge cycle times of the energy storage device includes:
calculating the accumulated charge quantity at the end of recording by utilizing the data in the operation record of the energy storage equipment; and dividing the accumulated charge electric quantity at the end of recording by the rated electric quantity of the energy storage equipment to obtain the charge and discharge cycle times of the energy storage equipment.
Optionally, obtaining SOH prediction data, RUL prediction data, and charge-discharge efficiency prediction data by using the obtained maximum electric quantity, equipment operation duration, charge-discharge efficiency, and a theoretical attenuation formula of the battery cell corresponding to each charge-discharge cycle includes:
setting an initial value of charge-discharge efficiency, a relation formula between the charge-discharge cycle times and the equipment operation time length, and initial parameters of a theoretical attenuation formula of a battery cell, and presetting a length threshold of input data;
taking the maximum electric quantity corresponding to the charge-discharge cycle and the charge-discharge efficiency as input data, and if the length of the input data is smaller than the length threshold value, estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by using initial parameters; and if the length of the input data is greater than or equal to the length threshold, filtering the input data, performing data curve fitting to obtain fitting parameters, and estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by adopting the fitting parameters.
Optionally, obtaining SOC prediction data using the SOH prediction data and the charge-discharge efficiency prediction data includes:
for a selected prediction period, acquiring the maximum electric quantity of the energy storage equipment in the period by utilizing the SOH prediction data;
calculating an electric quantity accumulated value of the energy storage device in the prediction period, and calculating the SOC prediction data based on the electric quantity accumulated value;
preferably, in the charged state, the charge integrated value=the integrated charge+the charged charge;
in the discharge state, the charge integrated value= (integrated charge-discharge charge)/charge-discharge efficiency prediction data.
The selection of the prediction period can be performed on a certain day and a certain charge-discharge cycle.
Optionally, the method further comprises:
if the SOC prediction data is the SOC upper limit value of the energy storage device, but the battery cell voltage in the prediction period does not reach the battery cell cut-off voltage of the energy storage device, updating the maximum electric quantity of the energy storage device in the prediction period to be the current electric quantity accumulation value until the battery cell voltage reaches the battery cell cut-off voltage of the energy storage device;
if the SOC prediction data is the SOC lower limit value of the energy storage device, but the battery cell voltage in the prediction period does not reach the battery cell cut-off voltage of the energy storage device, continuing to operate until the battery cell voltage reaches the battery cell cut-off voltage;
if the battery cell voltage reaches the battery cell charging cut-off voltage in the charging state, but the SOC prediction data is not the SOC upper limit value of the energy storage device, the maximum electric quantity of the energy storage device in the prediction period is updated to be the current electric quantity accumulation value, and after the charging is finished, the SOC prediction data is corrected to be the SOC upper limit value of the energy storage device;
if the battery cell voltage reaches the battery cell discharge cut-off voltage in the discharge state, but the SOC prediction data is not the SOC lower limit value of the energy storage device, correcting the SOC prediction data to the SOC lower limit value of the energy storage device after the discharge is finished.
Optionally, the method further comprises: and correcting the RUL expected data by using the SOC predicted data.
The application has the beneficial effects that:
according to the SOC, SOH and RUL joint estimation method of the energy storage equipment, provided by the application, the actual equipment capacity attenuation curve is fitted by combining the theoretical attenuation curve of the battery core and the historical capacity condition of the equipment, the corresponding accumulated charging data is calculated based on the change of the SOC in the prediction period, the equipment capacity attenuation curve is corrected in time, and relatively accurate SOH and RUL predicted values can be obtained with relatively low calculation cost.
Drawings
FIG. 1 is a diagram of a predicted case of a device capacity change in an embodiment of the present application;
FIG. 2 is a diagram showing the comparison of predicted SOC data and SOC data obtained by an ampere-hour integration method according to an embodiment of the present application;
fig. 3 is a diagram showing the prediction of the capacity change of the device after the correction in the embodiment of the present application.
Detailed Description
The present application is described in detail below with reference to examples, but the present application is not limited to these examples.
An energy storage device is set to exist, the device ID of the energy storage device is A1, the energy storage device is formed by combining 224 electric cores, the full cut-off voltage is 3600mV, the emptying cut-off voltage is 2800mV, and the rated power is 172kWh. The historical data acquisition times were 2022-06-29 00:00:00 to 2023-04-16 00:00:00, with 2023-04-16 00:00:00 to 2023-04-17-00:00:00 being used as SOC calculation test data.
Step 1, acquiring all historical data of the equipment A1, wherein the data comprise voltage data, power data, running state data, accumulated charge quantity data and accumulated discharge quantity data, and the data are shown in the following table.
And the upper limit value of the SOC of the device A1 was set to 100%, the lower limit value of the SOC was set to 0%, the cell charge cutoff voltage was set to 3600mV, the cell full charge initial voltage was set to 3000mV, the cell full discharge initial voltage was set to 3300mV, and the cell discharge cutoff voltage was set to 2800mV.
Step 2, obtaining a device operation record, namely data of a device charge-discharge time period, based on the operation state data obtained in the step 1, and obtaining a charge-discharge state (ch_dis), an electric quantity change (delta_e), a device operation time length (run_time) (such as days), a recording corresponding time length (delta_time), a voltage maximum value (v_max_sta) at the beginning of recording, a voltage minimum value (v_min_sta) at the beginning of recording, a voltage maximum value (v_max_end) at the end of recording, and a voltage minimum value (v_min_end) at the end of recording corresponding to the record.
The number of charge-discharge cycles (cyc_time) of the device is obtained by calculating the accumulated charge amount (e_charge_ac) at the end of recording, dividing the e_charge_ac by the rated power of the device, and using the date of the end time of recording as the recording date (date) of recording.
Finally, invalid data is excluded according to the recorded delta_e and delta_time, such as the delta_e is excluded to be 0 or the delta_time is excluded to be 0, and the result is shown in the following table, wherein ch represents charging, dis represents discharging, and sto represents standby or shutdown.
Step 3, a charge-discharge period (ch_dis_period) is set, that is, a period in which the SOC of the device A1 starts to be charged to a certain value from the SOC lower limit value of the device A1 and then is discharged to the SOC lower limit value of the device A1, and the ch_dis_period corresponding to the operation record is judged according to v_min_end. The judging method comprises the following steps:
the default initial value of ch_dis_period is 1 (i.e. the initial charge-discharge period numbers are accumulated from 1), if v_min_end is smaller than the set battery cell discharge cut-off voltage in the record, the current charge-discharge period is considered to be ended, and the next charge-discharge period is started. The resulting chdis period is shown in the following table:
and 4, calculating the charge and discharge efficiency in each ch_dis_period, and recording as a battery_eff_1, namely dividing the total discharge electric quantity value in the ch_dis_period by the total charge electric quantity value. The results are shown in the following table:
step 5, setting the initial value of the equipment electric quantity (ch_accumlate_e) as 0, setting the maximum electric quantity of the energy storage equipment as a null value NA, and calculating the ch_accumlate_e corresponding to each operation record according to the delta_e corresponding to the operation record and the recorded charge and discharge states, wherein the initial value is equal to the null value NA
The calculation formula of the electric quantity of the equipment in the charging state is shown as formula (1):
（1）
the calculation formula of the electric quantity of the equipment in the discharging state is shown in the formula (2):
（2）
for charging record, if v_min_sta is smaller than the set initial voltage of full charge of the battery cell and v_max_end is larger than or equal to the cut-off voltage of the charging of the battery cell, then considering delta_e of the record as the maximum electric quantity (energy) of the current energy storage device and recording ch_accumlate_e is equal to delta_e; if only v_max_end is larger than or equal to the battery cell charging cut-off voltage, then the ch_accumlate_e at the moment is considered as the maximum electric quantity of the current energy storage device;
for discharge recording, if v_max_sta is greater than the full discharge voltage of the battery cell and v_min_end is less than or equal to the cut-off voltage of the battery cell, then the recorded value obtained by dividing the electric quantity change delta_e by the battery_eff is considered to be the maximum electric quantity of the current energy storage device, and ch_accumlate_e is reset to 0; if only v_min_end is less than or equal to the cell discharge cutoff voltage, then reset ch_accumlate_e to 0. The maximum power obtained in the above process is denoted as energy_1. The partial calculation results are shown in the following table:
and 6, removing invalid data according to the charge and discharge efficiency, namely removing data which does not meet the theoretical charge and discharge efficiency of the lithium battery, wherein the charge and discharge efficiency is larger than 1 or smaller than 0.9. Then, based on the data obtained in step 5, for the cyc_time corresponding to each ch_dis_period, calculating the maximum electric quantity (denoted as energy_2, i.e. the historical maximum electric quantity), run_time, charge-discharge efficiency (denoted as battery_eff_2, i.e. the historical charge efficiency) and date corresponding to each charge-discharge cycle, and excluding the data with energy_2 as NA to obtain the following table:
the data obtained in the step 6 are relevant performance parameters of the equipment A1 in the corresponding historical period calculated based on the historical data.
And 7, acquiring SOH prediction data, RUL prediction data and charge and discharge efficiency prediction data based on the data obtained in the step 6 and a theoretical attenuation formula of the battery cell. Specifically:
setting an initial value of charge-discharge efficiency battery_eff for predictive estimation, an initial parameter of a relation formula between charge-discharge cycle times and equipment operation time length and an initial parameter of a battery cell attenuation formula, and presetting a length threshold of input data;
taking the historical maximum electricity quantity and the historical charge-discharge efficiency calculated in the step 6 as input data, and if the length of the input data is smaller than the length threshold value, estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by using the initial parameters; if the length of the input data is greater than or equal to the length threshold, filtering the input data by using an SG filtering method (Savitzky-Golay filter), then performing data curve fitting to obtain fitting parameters, and estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by adopting the fitting parameters.
Wherein SG filtering refers to: savitzky A, golay M J E Smoothing and differentiation of data by simplified least squares procedures [ J ]. Analytical chemistry, 1964, 36 (8): 1627-1639.
Curve fitting reference: montgomery D.C., peck E.A., vining G.G. Introduction to Linear Regression Analysis [ M ]. 6th ed. Hoboken: john Wiley & Sons, inc., 2021:223-235.
The estimated results are shown in fig. 1, which contains a theoretical cell attenuation curve, a historical data curve (i.e., a device capacity attenuation curve), and a prediction curve generated by fitting after filtering based on the historical data and SG filtering. It can be seen that, in the current state, the historical data curve of the device A1 shows a much greater decay rate than the theoretical cell decay curve. Meanwhile, based on the prediction curve, the final estimation results in the RUL prediction data of 579.520742 cycles, namely, the service life of the device A1 is about to 2025, 1 month and 30 days.
And 8, multiplying the SOH prediction data by the rated power of the equipment in the selected prediction period based on the SOH prediction data to obtain a predicted maximum power (esti_energy). And then calculating an electric quantity accumulated value in the prediction period, and dividing the electric quantity accumulated value by esti_energy to obtain SOC prediction data.
Specifically, the present application selects the day 2023-4-16 00:00 to 2023-4-17-00:00 as the prediction period, and calculates the predicted maximum power (esti_energy) = 162.59 kWh of the device A1 in the period. Then, the charge integrated value for the day is acquired, and SOC prediction data=charge integrated value/esti_energy for the period.
The result is shown in fig. 2, and it can be seen from the graph that, compared with the data obtained by the current ampere-hour integration method, the prediction result of the method is more accurate.
And 9, correcting the predicted maximum electric quantity (esti_energy) of the equipment A1 based on the obtained SOC predicted data, wherein the corrected predicted maximum electric quantity is 163.70 kWh, and the corrected RUL predicted data is 589.220742333803 cycles, namely the service life of the equipment A1 is about to 2025, 2 and 19 days. The modified device capacity change and predicted data conditions are shown in fig. 3.
While the application has been described in terms of preferred embodiments, it will be understood by those skilled in the art that various changes and modifications can be made without departing from the scope of the application, and it is intended that the application is not limited to the specific embodiments disclosed.
Claims (10)
1. The joint estimation method for the SOC, SOH and RUL of the energy storage equipment is characterized by at least comprising the following steps:
acquiring all historical data of energy storage equipment;
acquiring an operation record of the energy storage equipment based on the historical data;
setting a charge-discharge period of the energy storage equipment, and determining the charge-discharge period according to the operation record of the energy storage equipment;
calculating the charge and discharge efficiency of the energy storage device in each charge and discharge period;
calculating the equipment electric quantity and the maximum electric quantity corresponding to each operation record in a charge-discharge state by using the charge-discharge efficiency of the energy storage equipment in each charge-discharge period;
according to the operation record of the energy storage equipment, calculating the charge-discharge cycle times corresponding to each charge-discharge cycle of the energy storage equipment, and obtaining the maximum electric quantity, the equipment operation duration and the charge-discharge efficiency corresponding to each charge-discharge cycle;
acquiring SOH prediction data, RUL prediction data and charge and discharge efficiency prediction data by using the acquired maximum electric quantity, equipment operation time length, charge and discharge efficiency and a cell theoretical attenuation formula corresponding to each charge and discharge cycle;
and acquiring SOC prediction data by using the SOH prediction data and the charge-discharge efficiency prediction data.
2. The method of claim 1, wherein the charge-discharge cycle of the energy storage device is set to:
and starting charging from the lower limit value of the SOC of the energy storage device to a certain value of the SOC, and discharging to the period of the lower limit value of the SOC of the energy storage device.
3. The method of claim 2, wherein determining a charge-discharge cycle from an operational record of the energy storage device comprises:
judging a charge-discharge period by using a voltage minimum value v_min_end at the end of recording in the operation record of the energy storage equipment;
and if the voltage minimum value v_min_end at the end of the record is smaller than the set battery cell discharge cut-off voltage in the operation record of the energy storage equipment, the current charge and discharge period is considered to be ended, and the next charge and discharge period is started.
4. The method of claim 1, wherein the calculating method of the device power corresponding to each operation record in the charge-discharge state includes:
setting the initial value of the electric quantity of the equipment to be 0, setting the maximum electric quantity of the energy storage equipment to be a null value NA, and then,
the calculation formula of the electric quantity of the equipment in the charging state is shown as formula (1):
（1）
the calculation formula of the electric quantity of the equipment in the discharging state is shown in the formula (2):
（2）
wherein ch_accumlate_e is the electric quantity of the device, delta_e electric quantity variable quantity, and battery_eff_1 is the charge and discharge efficiency of the energy storage device in the corresponding charge and discharge period;
for charging recording, if the voltage minimum value v_min_sta at the beginning of recording is smaller than the set initial voltage of full charge of the battery core and the voltage maximum value v_max_end at the end of recording is larger than or equal to the cut-off voltage of charging of the battery core, the recorded electric quantity delta_e is considered to be the maximum electric quantity of the current energy storage device and the electric quantity ch_accumlate_e of the recording device is equal to the electric quantity delta_e; if the voltage maximum value v_max_end at the end of recording is larger than or equal to the battery cell charging cut-off voltage, the device electric quantity ch_accumlate_e at the moment is considered to be the maximum electric quantity of the current energy storage device;
for discharge recording, if the maximum voltage v_max_sta at the beginning of recording is greater than the full discharge voltage of the battery cell and the minimum voltage v_min_end at the end of recording is less than or equal to the cut-off voltage of the discharge of the battery cell, then the value obtained by dividing the recorded electric quantity variation delta_e by the charge-discharge efficiency battery_eff_1 of the energy storage device in the corresponding charge-discharge period is considered as the maximum electric quantity of the current energy storage device, and ch_accumlate_e is reset to 0; if only the voltage minimum v_min_end at the end of recording is equal to or less than the cell discharge cutoff voltage, ch_accumlate_e is reset to 0.
5. The method of claim 1, further comprising, prior to obtaining the maximum power, the device operating duration, and the charge-discharge efficiency for each charge-discharge cycle:
deleting invalid data in the operation record of the energy storage equipment;
the invalid data refers to data which does not satisfy the theoretical charge and discharge efficiency of the battery.
6. The method of claim 4, wherein the method for calculating the charge-discharge cycle number of the energy storage device comprises:
calculating the accumulated charge quantity at the end of recording by utilizing the data in the operation record of the energy storage equipment; and dividing the accumulated charge electric quantity at the end of recording by the rated electric quantity of the energy storage equipment to obtain the charge and discharge cycle times of the energy storage equipment.
7. The method of claim 1, wherein obtaining SOH prediction data, RUL prediction data, and charge-discharge efficiency prediction data using the obtained maximum power, device operation duration, and charge-discharge efficiency corresponding to each charge-discharge cycle, and a cell theoretical attenuation formula, comprises:
setting an initial value of charge-discharge efficiency, a relation formula between the charge-discharge cycle times and the equipment operation time length, and initial parameters of a theoretical attenuation formula of a battery cell, and presetting a length threshold of input data;
taking the maximum electric quantity corresponding to the charge-discharge cycle and the charge-discharge efficiency as input data, and if the length of the input data is smaller than the length threshold value, estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by using initial parameters; and if the length of the input data is greater than or equal to the length threshold, filtering the input data, performing data curve fitting to obtain fitting parameters, and estimating charge-discharge efficiency prediction data, SOH prediction data and RUL prediction data by adopting the fitting parameters.
8. The method according to claim 1, wherein obtaining SOC prediction data using the SOH prediction data and the charge-discharge efficiency prediction data, comprises:
for a selected prediction period, acquiring the maximum electric quantity of the energy storage equipment in the period by utilizing the SOH prediction data;
calculating an electric quantity accumulated value of the energy storage device in the prediction period, and calculating the SOC prediction data based on the electric quantity accumulated value;
wherein, in the charged state, the electric quantity accumulated value=accumulated electric quantity+charged electric quantity;
in the discharge state, the charge integrated value= (integrated charge-discharge charge)/charge-discharge efficiency prediction data.
9. The method of claim 8, wherein the method further comprises:
if the SOC prediction data is the SOC upper limit value of the energy storage device, but the battery cell voltage in the prediction period does not reach the battery cell cut-off voltage of the energy storage device, updating the maximum electric quantity of the energy storage device in the prediction period to be the current electric quantity accumulation value until the battery cell voltage reaches the battery cell cut-off voltage of the energy storage device;
if the SOC prediction data is the SOC lower limit value of the energy storage device, but the battery cell voltage in the prediction period does not reach the battery cell cut-off voltage of the energy storage device, continuing to operate until the battery cell voltage reaches the battery cell cut-off voltage;
if the battery cell voltage reaches the battery cell charging cut-off voltage in the charging state, but the SOC prediction data is not the SOC upper limit value of the energy storage device, the maximum electric quantity of the energy storage device in the prediction period is updated to be the current electric quantity accumulation value, and after the charging is finished, the SOC prediction data is corrected to be the SOC upper limit value of the energy storage device;
if the battery cell voltage reaches the battery cell discharge cut-off voltage in the discharge state, but the SOC prediction data is not the SOC lower limit value of the energy storage device, correcting the SOC prediction data to the SOC lower limit value of the energy storage device after the discharge is finished.
10. The method according to claim 1, wherein the method further comprises: and correcting the RUL expected data by using the SOC predicted data.
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