Photovoltaic energy storage stable management method and management system for avoiding deep charging and deep discharging

Abstract

The present application relates to the technical field of energy storage management, in particular to a photovoltaic energy storage stable management method and management system for avoiding deep charging and deep discharging, comprising: obtaining charging environment data and discharging environment data of an energy storage device and SOC data of each single battery in a battery pack of the energy storage device; obtaining an attenuation coefficient of the single battery according to a change trend and a change amplitude of the SOC data of the single battery; obtaining an initial SOC adjustment rate of the single battery in combination with a change trend and the attenuation coefficient of the SOC data of the single battery; obtaining a relative coefficient of the single battery according to a correlation between the SOC change of the single battery and the charging environment data and the discharging environment data; thus correcting the initial SOC adjustment rate to obtain a final SOC adjustment rate, so as to adjust upper and lower limits of the SOC of the corresponding single battery in the battery pack of the energy storage device. The present application enhances the balance and stability of the overall operation of the battery pack.

Description

Technical Field

[0001] This invention relates to the field of energy storage management technology, specifically to a method and system for stable management of photovoltaic energy storage that avoids deep charging and deep discharging. Background Technology

[0002] Photovoltaic cells convert solar energy into electrical energy through the photoelectric effect. The core principle is based on the photovoltaic effect of semiconductor materials. In order to ensure the absorption of redundant electrical energy generated by photovoltaic power generation and to meet the corresponding electrical energy demand when discharge is required, energy storage devices are connected to photovoltaic power generation devices to store electrical energy. In order to reduce the chemical reaction stress of the battery under extreme conditions and slow down the rate of battery capacity decay during the energy storage process, thereby extending the battery's lifespan, it is usually necessary to avoid the battery being in a state of deep charging or deep discharging.

[0003] In existing energy storage management methods, it is common to set corresponding fixed upper and lower limits of SOC (State of Charge) for the battery pack of energy storage equipment to limit the state of charge within a suitable range. However, the power generation and consumption environment in the photovoltaic industry is complex, and there are differences in SOC among the individual cells in the battery pack of energy storage equipment. Therefore, using fixed upper and lower limits of SOC for the battery pack will result in insufficient overall energy storage stability of the battery pack, and the effect of setting upper and lower limits of SOC on delaying battery capacity decay and extending battery life is limited. Summary of the Invention

[0004] This invention provides a photovoltaic energy storage stability management method and system to avoid deep charging and deep discharging, thereby solving existing problems.

[0005] The photovoltaic energy storage stability management method and management system for avoiding deep charging and deep discharging of the present invention adopts the following technical solution:

[0006] One embodiment of the present invention provides a method for stable management of photovoltaic energy storage to avoid deep charging and deep discharging, the method comprising the following steps:

[0007] Acquire charging and discharging environment data of the energy storage device, as well as the SOC data of each individual cell in the battery pack of the energy storage device;

[0008] Based on the changing trend and magnitude of the SOC data of a single cell, the degradation coefficient of the single cell is obtained; combined with the convergence of the changes in SOC data between any single cell and other single cells, and combined with the degradation coefficient of the single cell, the initial SOC regulation rate of the single cell is obtained.

[0009] Based on the correlation between the SOC change of any single battery cell and charging environment data and discharging environment data, the relative coefficient of the single battery cell is obtained; the initial SOC regulation rate of any single battery cell is corrected using the relative coefficient to obtain the final SOC regulation rate of the single battery cell.

[0010] By combining the current SOC level of all individual cells and the final SOC regulation rate of each individual cell, the upper and lower limits of the SOC of the corresponding individual cells in the energy storage device's battery pack are adjusted.

[0011] Optionally, the method for obtaining the degradation coefficient of a single cell based on the changing trend and magnitude of the SOC data of the single cell includes:

[0012] Based on the SOC index and upper and lower limits of any single cell at any time, relative SOC data is obtained by forming relative SOC data from the relative SOC parameters at all times.

[0013] The relative SOC data of any single cell is decomposed over time, and the relative SOC data is divided into trends based on the periodic characteristics in the decomposition results, resulting in several SOC trend sequence segments of the single cell.

[0014] The degradation coefficient of the single cell is calculated based on the magnitude and trend of change of all relative SOC trend sequence segments of the single cell.

[0015] Optionally, the method for obtaining the relative SOC data formed by the relative SOC parameters at all times includes:

[0016] For any single cell, the upper and lower limits of the SOC of the single cell at each time are obtained. Based on the relative position of the SOC index of the single cell at each time within the range corresponding to the upper and lower limits of the SOC at that time, the relative SOC parameter at that time is calculated, thereby obtaining the time-series data of the relative SOC parameter of the single cell at all times, which is denoted as the relative SOC data of the single cell.

[0017] Optionally, the method for obtaining several SOC trend sequence segments of the single cell includes:

[0018] The STL time series decomposition algorithm is used to process the relative SOC data of the individual battery to obtain the trend term, periodic term, and residual term of the relative SOC data. Trigonometric functions are used to perform curve fitting on the periodic term of the relative SOC data to obtain the function corresponding to the fitted curve of the periodic term, which is denoted as the periodic term function. The period value of the periodic term function is obtained, and the trend term of the relative SOC data is divided into several sequence segments using the period value, which are used as the relative SOC trend sequence segments of the individual battery.

[0019] Optionally, the specific method for calculating the degradation coefficient of the single cell based on the variation magnitude and trend of all relative SOC trend sequence segments of the single cell includes:

[0020] For any relative SOC trend sequence segment of a single battery cell, a straight line is fitted to the relative SOC trend sequence segment using the least squares method to obtain the slope of the fitted straight line corresponding to the relative SOC trend sequence segment, which is denoted as the relative trend slope of the corresponding relative SOC trend sequence segment. The relative trend slopes corresponding to all relative SOC trend sequence segments of the single battery cell are obtained, and the relative trend slopes are sorted according to the time order of the corresponding SOC trend sequence segments to obtain the relative trend slope sequence of the single battery cell. Based on the changes in the relative trend slope sequence, the degradation coefficient of the single battery cell is calculated.

[0021] Optionally, the specific calculation method for the degradation coefficient of the single cell is as follows:

[0022] A backward difference sequence of the relative trend slope sequence of a single cell is obtained. Based on the cumulative value of the elements in the backward difference sequence and the numerical dispersion of the elements in the relative trend slope sequence, the attenuation coefficient of the single cell is obtained. Both the cumulative value and the numerical dispersion are positively correlated with the attenuation coefficient.

[0023] Optionally, the method for obtaining the initial SOC regulation rate of a single cell by combining the convergence of SOC data changes between any single cell and other single cells, and combining the cell's degradation coefficient, includes the following specific methods:

[0024] Any single cell is designated as the target single cell. The average Pearson correlation coefficient between the SOC data of the target single cell and the SOC data of all other single cells in the battery pack is calculated and used as the convergence coefficient of the target single cell.

[0025] The initial SOC regulation rate is calculated by fusing the convergence coefficient of the target single cell with the decay coefficient.

[0026] Optionally, the method for obtaining the relative coefficient of a single battery cell based on the correlation between the SOC change of any single battery cell and charging environment data and discharging environment data includes:

[0027] Time-align the charging environment data, discharging environment data, and the SOC data of individual batteries;

[0028] For any single battery cell, calculate the SOC change rate of the single battery cell at each time point and form time-series data, denoted as SOC change rate data; calculate the Pearson correlation coefficient between the SOC change rate data and the charging environment data, and the Pearson correlation coefficient between the SOC change rate data and the discharging environment data, respectively.

[0029] Based on the Pearson correlation coefficients between the SOC change rate data and the charging environment data and the discharging environment data, the relative coefficients of individual cells were calculated. The Pearson correlation coefficients between the SOC change rate data and the charging environment data were negatively correlated with the relative coefficients, while the Pearson correlation coefficients between the SOC change rate data and the discharging environment data were positively correlated with the relative coefficients.

[0030] Optionally, the method for adjusting the upper and lower limits of the SOC of the corresponding individual cells in the energy storage device's battery pack by combining the current SOC level of all individual cells and the final SOC regulation rate of the individual cells includes:

[0031] Obtain the average SOC of all individual cells in the battery pack at the current moment as a reference SOC level;

[0032] For any single cell, calculate the difference between the current SOC index of the single cell and the reference SOC level, and record it as the SOC deviation of the single cell; based on the sign and magnitude of the SOC deviation of the single cell, and combined with the final SOC regulation rate, calculate the lower limit adjustment amount and the upper limit adjustment amount of the SOC of the single cell.

[0033] A preset lower limit and a preset upper limit for SOC are established. The lower limit of SOC is increased by adding the adjustment amount of the lower limit of SOC for a single cell to the lower limit of SOC to obtain the new lower limit of SOC. The upper limit of SOC is decreased by the adjustment amount of the upper limit of SOC to obtain the new upper limit of SOC.

[0034] A photovoltaic energy storage stability management system for avoiding deep charging and deep discharging includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the photovoltaic energy storage stability management methods for avoiding deep charging and deep discharging.

[0035] The beneficial effects of the technical solution of this invention are as follows: By integrating the battery's own state with external charging and discharging environment information, dynamic and precise adjustment of the upper and lower limits of the state of charge (SOC) of each individual battery in the energy storage system is achieved. An assessment of the convergence of SOC changes among batteries is introduced to initially determine the adjustment requirements. Then, considering the actual impact of charging and discharging environments on SOC changes, the adjustment coefficient is corrected to improve adaptability. Finally, based on the degree to which the current SOC deviates from the average level, the SOC operating range of each individual battery is dynamically adjusted. This method effectively alleviates the performance degradation problem caused by battery inconsistency and significantly enhances the overall balance and stability of the battery pack operation. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the steps in the photovoltaic energy storage stability management method for avoiding deep charging and deep discharging according to the present invention;

[0038] Figure 2 This is a structural block diagram of the photovoltaic energy storage stability management system for avoiding deep charging and deep discharging according to the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the photovoltaic energy storage stability management method and management system for avoiding deep charging and discharging proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] The following description, in conjunction with the accompanying drawings, details the specific scheme of the photovoltaic energy storage stability management method and management system provided by the present invention to avoid deep charging and deep discharging.

[0042] Please see Figure 1 The diagram illustrates a flowchart of a photovoltaic energy storage stability management method for avoiding deep charging and discharging, according to an embodiment of the present invention. The method includes the following steps:

[0043] Step S001: Obtain charging environment data and discharging environment data of the energy storage device, as well as the SOC data of each individual cell in the battery pack of the energy storage device.

[0044] It should be noted that during photovoltaic power generation, energy storage devices operate in a complex environment under the dual effects of charging and discharging. This causes the State of Charge (SOC) index of the energy storage device to constantly change dynamically. The SOC index reflects the charge and discharge state of the battery in the energy storage device. When the SOC index is close to 100%, it indicates that the battery is in a deep charge state, and when the SOC index is close to 0%, it indicates that the battery is in a deep discharge state. However, if the battery is in such extreme states of deep charging or deep discharging for a long time, it is very easy to cause the battery capacity to decay too quickly, resulting in a reduction in battery life. Therefore, in order to avoid the battery being in such extreme states for a long time, this embodiment of the invention needs to acquire data on the current state of the battery and the environment, so as to facilitate subsequent data processing and analysis, and then use the analysis results for stable energy storage management.

[0045] Specifically, in order to implement the photovoltaic energy storage stability management method to avoid deep charging and deep discharging proposed in this embodiment, it is first necessary to collect SOC data, charging environment data, and discharging environment data. The specific process is as follows:

[0046] First, the battery management system (BMS) monitors the terminal voltage, charging and discharging current, and surface temperature parameters of each individual cell in the energy storage device's battery pack in real time. Then, based on the coulomb integral method combined with the open-circuit voltage calibration algorithm, the SOC data of each individual cell is calculated to obtain the SOC data of the individual cells in the energy storage device's battery pack.

[0047] It should be noted that, in this embodiment of the invention, SOC data is recorded in percentage form, and the sampling frequency is not less than 1Hz to ensure accurate capture of dynamic changes.

[0048] Then, the photovoltaic power generation is measured by the DC-side power transmitter of the photovoltaic array in the photovoltaic monitoring system, and the resulting time-series data is used as the charging environment data for the energy storage device.

[0049] Finally, the discharge power is measured by the AC side current transformer of the energy storage converter (PCS) of the load monitoring system, and the resulting time-series data is used as the discharge environment data of the energy storage device.

[0050] Thus, the SOC data, charging environment data, and discharging environment data of the energy storage device are obtained through the above methods.

[0051] Step S002: Based on the changing trend and magnitude of the SOC data of a single cell, obtain the degradation coefficient of the single cell; combine the convergence of SOC data changes between any single cell and other single cells, and combine the degradation coefficient of the single cell to obtain the initial SOC regulation rate of the single cell.

[0052] It should be noted that, due to the influence of manufacturing tolerances, inconsistent aging levels, and temperature gradients between battery modules, the SOC changes of different individual cells in the battery pack of the energy storage device vary. This can easily further affect the overall performance of the battery pack and the stability of energy storage. Therefore, in order to ensure that the individual cells in the battery pack maintain a consistent SOC state during charging and discharging, thereby improving the overall performance stability of the energy storage system, this embodiment of the invention selects to analyze the individual cells in the battery pack of the photovoltaic energy storage device to determine the state of each individual cell at the corresponding time.

[0053] Specifically, in step S201, the degradation coefficient of a single cell is obtained based on the changing trend and magnitude of the SOC data of the single cell.

[0054] As a preferred embodiment, the method for obtaining the degradation coefficient of a single battery cell based on the changing trend and magnitude of its SOC data includes:

[0055] First, based on the SOC index and upper and lower limits of any single cell at any time, relative SOC data is obtained by forming relative SOC data from the relative SOC parameters at all times.

[0056] As an optional embodiment, the method for obtaining the relative SOC data formed by the relative SOC parameters at all times includes:

[0057] For any single cell, the upper and lower limits of the SOC of the single cell at each time are obtained. Based on the relative position of the SOC index of the single cell at each time within the range corresponding to the upper and lower limits of the SOC at that time, the relative SOC parameter at that time is calculated, thereby obtaining the time-series data of the relative SOC parameter of the single cell at all times, which is denoted as the relative SOC data of the single cell.

[0058] It should be noted that the STL time series decomposition algorithm is a time series analysis method based on local weighted regression, which is used to decompose data into three independent components: trend term, seasonal term, and residual term. Since the STL time series decomposition algorithm is an existing algorithm, it will not be described in detail in this embodiment of the invention.

[0059] Then, the relative SOC data of any single cell is decomposed over time, and the relative SOC data is divided into trends based on the periodic characteristics in the decomposition results, resulting in several SOC trend sequence segments of the single cell.

[0060] As an optional embodiment, the method for obtaining several SOC trend sequence segments of the single cell includes:

[0061] The STL time series decomposition algorithm is used to process the relative SOC data of the individual battery to obtain the trend term, periodic term, and residual term of the relative SOC data. Trigonometric functions are used to perform curve fitting on the periodic term of the relative SOC data to obtain the function corresponding to the fitted curve of the periodic term, which is denoted as the periodic term function. The period value of the periodic term function is obtained, and the trend term of the relative SOC data is divided into several sequence segments using the period value, which are used as the relative SOC trend sequence segments of the individual battery.

[0062] Finally, the degradation coefficient of the single cell is calculated based on the magnitude and trend of change of all relative SOC trend sequence segments of the single cell.

[0063] It should be noted that since the upper and lower limits of the SOC of a single battery cell may be adjusted, the fluctuation range of the specific SOC index of a single battery cell in the SOC data varies at different times during the fluctuation process, which can easily lead to difficulties in analyzing the SOC changes of a single battery cell and determining its SOC decay. Therefore, this embodiment of the invention obtains relative SOC parameters to characterize the relative index of the corresponding single battery cell within the corresponding upper and lower limits of SOC at a given time, avoiding the problem of difficulty in analyzing SOC decay when the upper and lower limits of SOC are dynamically adjusted. In addition, although photovoltaic energy storage devices are affected by complex charging and discharging environments, resulting in relatively complex fluctuations in the SOC data of each single battery cell, the daily cycle characteristics of photovoltaic power generation cause the charging and discharging mode to exhibit a regular day-night cycle. The relative SOC data of a single battery cell still has certain periodic characteristics as a whole, and shows a corresponding decay trend in all changing cycles. Therefore, this embodiment of the invention chooses to perform time-series decomposition on the relative SOC data of a single battery cell, and determines the decay coefficient of the corresponding single battery cell by analyzing the trend characteristics shown in the time-series decomposition results.

[0064] As an optional embodiment, the specific method for calculating the degradation coefficient of the single cell based on the variation amplitude and trend of all relative SOC trend sequence segments of the single cell includes:

[0065] For any relative SOC trend sequence segment of a single battery cell, a straight line is fitted to the relative SOC trend sequence segment using the least squares method to obtain the slope of the fitted straight line corresponding to the relative SOC trend sequence segment, which is denoted as the relative trend slope of the corresponding relative SOC trend sequence segment. The relative trend slopes corresponding to all relative SOC trend sequence segments of the single battery cell are obtained, and the relative trend slopes are sorted according to the time order of the corresponding SOC trend sequence segments to obtain the relative trend slope sequence of the single battery cell. Based on the changes in the relative trend slope sequence, the degradation coefficient of the single battery cell is calculated.

[0066] As an optional embodiment, the specific calculation method for the attenuation coefficient of the single cell is as follows: obtain the backward difference sequence of the relative trend slope sequence of the single cell, and obtain the attenuation coefficient of the single cell based on the cumulative value of the elements in the backward difference sequence and the numerical dispersion of the elements in the relative trend slope sequence. The cumulative value and the numerical dispersion are both positively correlated with the attenuation coefficient.

[0067] As an optional embodiment, the specific method for calculating the degradation coefficient of the single battery cell is as follows:

[0068]

[0069] in, Indicates the first The degradation coefficient of an individual cell; Indicates the first The backward difference sequence of the relative trend slope sequence of the individual cells The numerical value of each element; Indicates the first The number of elements in the backward difference sequence of the relative trend slope sequence of individual cells; Indicates the first The standard deviation of all elements in the relative trend slope sequence of individual cells.

[0070] It should be noted that the attenuation coefficient is used to describe the degree of attenuation of a single battery cell in terms of its state of charge (SOC). The larger the value of the attenuation coefficient, the more severe the SOC attenuation of the corresponding single battery cell. In this case, a higher regulation rate is required when setting the dynamic SOC upper and lower limits for the single battery cell to keep its SOC stable within a certain range.

[0071] Step S202: Combining the convergence of SOC data changes between any single cell and other single cells, and combining the cell's attenuation coefficient, the initial SOC regulation rate of the single cell is obtained.

[0072] As a preferred embodiment, the method for obtaining the initial SOC regulation rate of a single cell by combining the convergence of SOC data changes between any single cell and other single cells, and by combining the cell's degradation coefficient, includes the following specific steps:

[0073] First, any single cell is designated as the target single cell. The average Pearson correlation coefficient between the SOC data of the target single cell and the SOC data of all other single cells in the battery pack is calculated and used as the convergence coefficient of the target single cell.

[0074] Then, the convergence coefficient of the target single cell and the decay coefficient are fused together to calculate the initial SOC regulation rate.

[0075] As an optional embodiment, the specific method for calculating the initial SOC regulation rate of the single battery cell is as follows:

[0076]

[0077] in, Indicates the first Initial SOC regulation rate of each individual cell; Indicates the first The degradation coefficient of an individual cell; Indicates the first The coefficient of convergence of changes in individual cells.

[0078] It should be noted that the initial SOC regulation rate is used to represent the degree of adjustment to the upper and lower limits of the SOC of the corresponding individual battery cell. The larger the initial SOC regulation rate, the greater the subsequent adjustment to the upper and lower limits of the SOC of the corresponding individual battery cell. In the specific calculation method of the initial SOC regulation rate, the convergence coefficient of the individual battery cell's change is used. The larger the value, the higher the degree of similarity between the SOC change of the individual cell and other cells. In this case, the higher the degree of consistency between the SOC level of the individual cell and the overall SOC level of the battery pack, the less adjustment should be made to the upper and lower limits of the SOC of the individual cell in the future.

[0079] Thus, the initial SOC regulation rate of each individual cell in the photovoltaic energy storage device's battery pack is obtained using the above method.

[0080] Step S003: Based on the correlation between the SOC change of any single battery cell and the charging environment data and the discharging environment data, obtain the relative coefficient of the single battery cell; use the relative coefficient to correct the initial SOC regulation rate of any single battery cell to obtain the final SOC regulation rate of the single battery cell.

[0081] It should be noted that during the operation of photovoltaic energy storage equipment, it receives electrical energy from the photovoltaic power generation device and simultaneously transmits some electrical energy to the power user. Therefore, the SOC index of the individual cells in the battery pack fluctuates due to the influence of power generation and discharge. Thus, in order to adjust the upper and lower limits of the SOC of individual cells, it is also necessary to consider the impact of the charging and discharging process of the battery pack on the SOC, so as to obtain a more accurate SOC regulation rate.

[0082] Specifically, in step S301, the relative coefficient of a single cell is obtained based on the correlation between the SOC change of any single cell and the charging environment data and the discharging environment data.

[0083] It should be noted that, due to the dual charge-discharge cycle of the energy storage system's battery pack, and the differences between individual cells within the battery pack, the impact of this dual charge-discharge cycle on individual cells is not entirely consistent. As a result, after multiple charge-discharge cycles, the State of Charge (SOC) of individual cells gradually exhibits different characteristics, making it difficult to maintain consistency. Therefore, this embodiment of the invention takes into account this impact of charge and discharge on individual cells, and chooses to further combine and analyze the charging environment data and discharging environment data of the energy storage system with the SOC data of individual cells to obtain the relative coefficients of individual cells, thereby describing the impact of the dual charge-discharge cycle on the SOC state.

[0084] As a preferred embodiment, the specific method for obtaining the relative coefficient of a single cell is as follows:

[0085] First, the charging environment data, discharging environment data, and SOC data of individual batteries are time-aligned.

[0086] Then, for any single cell, the SOC change rate of the single cell at each time step is calculated and time-series data is generated, denoted as SOC change rate data; the Pearson correlation coefficient between the SOC change rate data and the charging environment data, and the Pearson correlation coefficient between the SOC change rate data and the discharging environment data are calculated respectively.

[0087] Finally, based on the Pearson correlation coefficients between the SOC change rate data and the charging environment data and the discharging environment data, the relative coefficients of individual cells were calculated. The Pearson correlation coefficients between the SOC change rate data and the charging environment data were negatively correlated with the relative coefficients, while the Pearson correlation coefficients between the SOC change rate data and the discharging environment data were positively correlated with the relative coefficients.

[0088] As an optional embodiment, the specific method for calculating the relative coefficient of the single cell is as follows:

[0089]

[0090] in, Indicates the first The relative coefficient of each individual cell; Indicates the first Pearson correlation coefficient between the SOC change rate data of individual cells and the charging environment data; Indicates the first The Pearson correlation coefficient between the SOC change rate data of individual cells and the discharge environment data.

[0091] It should be noted that the relative coefficient describes the degree to which the charging and discharging process of a single cell affects the state of charge (SOC) of the battery. A larger relative coefficient indicates a greater degree of SOC degradation caused by the charging and discharging process of a single cell. In the calculation method of the relative coefficient for a single cell, for the ... The Pearson correlation coefficient between the SOC change rate data of individual cells and the charging environment data indicates that, generally, an increase in charging power should lead to a positive change in SOC. It should approach 1; if A value significantly less than 1 indicates that the charging process has not been effectively converted into an increase in SOC (e.g., energy loss due to increased internal resistance), reflecting accelerated battery degradation; for the second... The Pearson correlation coefficient between the SOC change rate data of individual cells and the discharge environment data indicates that an increase in discharge power should lead to a negative change in SOC. It should approach -1; if A value greater than -1 (e.g., -0.5) indicates that the SOC decrease during the discharge process is insufficient, reflecting the capacity decay caused by battery aging.

[0092] Step S302: The initial SOC regulation rate of any single cell is corrected using a relative coefficient to obtain the final SOC regulation rate of the single cell.

[0093] As an optional embodiment, the method for correcting the initial SOC regulation rate of any single cell using a relative coefficient to obtain the final SOC regulation rate of the single cell includes the following specific method:

[0094]

[0095] in, Indicates the first Initial SOC regulation rate of each individual cell; Indicates the first Initial SOC regulation rate of each individual cell; Indicates the first The relative coefficient of each individual cell.

[0096] It should be noted that the initial SOC adjustment rate This already reflects the battery's own degradation trend and the convergence within the group. By combining... Introducing environmental decay as a weight, when hour, This indicates that there is no additional environmental impact; when hour, ,and The larger the value, the more significant the increase in the regulation rate.

[0097] Thus, the final SOC regulation rate for each individual cell is obtained using the above method.

[0098] Step S004: Based on the current SOC level of all individual cells and the final SOC regulation rate of the individual cells, adjust the upper and lower limits of the SOC of the corresponding individual cells in the energy storage device's battery pack.

[0099] It should be noted that although the final SOC regulation rate is obtained to adjust the upper and lower limits of the corresponding individual battery, in the embodiments of the present invention, the adjustment method of the final SOC regulation rate is to adjust the range of the upper and lower limits of SOC. Therefore, the specific values ​​of the upper and lower limits of SOC of the individual battery cannot be accurately obtained. Therefore, the embodiments of the present invention choose to evaluate the SOC level of the individual batteries in the battery pack, so as to adaptively adjust the specific values ​​of the upper and lower limits of SOC of different individual batteries.

[0100] Specifically, as a preferred embodiment, the method for adjusting the upper and lower limits of the SOC of the corresponding individual cells in the energy storage device's battery pack, by combining the current SOC level of all individual cells and the final SOC regulation rate of the individual cells, includes the following specific methods:

[0101] First, obtain the average SOC of all individual cells in the battery pack at the current moment as a reference SOC level.

[0102] Then, for any single cell, calculate the difference between the current SOC index of the single cell and the reference SOC level, and record it as the SOC deviation of the single cell; based on the sign and magnitude of the SOC deviation of the single cell, and in conjunction with the final SOC regulation rate, calculate the lower limit adjustment amount and the upper limit adjustment amount of the SOC of the single cell.

[0103] As an optional embodiment, the specific calculation method for the SOC lower limit adjustment and SOC upper limit adjustment of the single battery cell is as follows:

[0104]

[0105] in, This indicates the adjustment amount of the lower limit of the SOC of a single battery cell; This indicates the adjustment amount of the SOC upper limit for a single battery cell; This indicates the SOC deviation of a single cell. This indicates the initial SOC regulation rate of a single cell. This indicates that the maximum value is retrieved.

[0106] It should be noted that the calculation method for the SOC lower limit adjustment of a single battery cell is as follows: hour, ,but This indicates that the lower limit of SOC needs to be increased to limit the depth of discharge; while when d≥0, The lower limit of SOC remains unchanged. Regarding the adjustment of the upper limit of SOC for a single battery cell, when... hour, ,but This indicates that the SOC limit needs to be lowered to restrict the depth of charging; when hour, The SOC upper limit remains unchanged. Additionally, As a scaling factor The larger the adjustment amount or The greater the adjustment range, the more aggressive the adjustment strategy is needed for high-degradation batteries.

[0107] Finally, a basic lower limit and a basic upper limit of SOC are preset. The basic lower limit of SOC is added to the adjustment amount of the lower limit of SOC of a single cell to obtain the new lower limit of SOC. The basic upper limit of SOC is subtracted from the adjustment amount of the upper limit of SOC to obtain the new upper limit of SOC.

[0108] As an optional embodiment, the specific calculation method for the new lower and upper SOC limits of a single battery cell is as follows:

[0109]

[0110]

[0111] in, This indicates the new lower limit of the state of charge (SOC) for a single cell. This indicates the new SOC limit for a single battery cell; and These are the preset lower limit and upper limit of basic SOC, respectively; This indicates the adjustment amount of the lower limit of the SOC of a single battery cell; This indicates the adjustment amount of the SOC upper limit for a single battery cell; This indicates that the maximum value is retrieved. This indicates that the minimum value is obtained.

[0112] It should be noted that the preset lower limit and upper limit of basic SOC are 20% and 80% in this embodiment of the invention, respectively. In other embodiments, they can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations.

[0113] It should be noted that, in this embodiment of the invention, the upper and lower limits of the SOC corresponding to each individual cell in the battery pack of the photovoltaic energy storage device are adjusted, so that individual cells with different SOC change rates gradually exhibit convergence characteristics. That is, at the corresponding time, the SOC difference between individual cells in the battery pack is balanced, so that the individual cells in the battery pack can always maintain a relatively consistent SOC state during the charging and discharging process, thereby improving the overall energy storage performance of the photovoltaic energy storage device and realizing stable management of photovoltaic energy storage.

[0114] By following the steps above, photovoltaic energy storage management can be completed.

[0115] Please see Figure 2 The diagram illustrates a structural block diagram of a photovoltaic energy storage stability management system for avoiding deep charging and deep discharging according to an embodiment of the present invention. The system includes a memory 202, a processor 201, and a computer program 2021 stored in the memory 202 and executable on the processor. When the processor 201 executes the computer program 2021, it implements steps S001 to S004 of the photovoltaic energy storage stability management method for avoiding deep charging and deep discharging.

[0116] Furthermore, in an optional embodiment, the memory 202 described above may include read-only memory and random access memory, and provide instructions and data to the processor. The memory 202 may also include non-volatile random access memory. For example, the memory may also store device type information.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A photovoltaic energy storage stability management method that avoids deep charging and deep discharging, characterized in that, The method includes the following steps: Acquire charging and discharging environment data of the energy storage device, as well as the SOC data of each individual cell in the battery pack of the energy storage device; Based on the changing trend and magnitude of the SOC data of a single cell, the degradation coefficient of the single cell is obtained; combined with the convergence of the changes in SOC data between any single cell and other single cells, and combined with the degradation coefficient of the single cell, the initial SOC regulation rate of the single cell is obtained. Based on the correlation between the SOC change of any single battery cell and charging environment data and discharging environment data, the relative coefficient of the single battery cell is obtained; the initial SOC regulation rate of any single battery cell is corrected using the relative coefficient to obtain the final SOC regulation rate of the single battery cell. Based on the current SOC level of all individual cells and the final SOC regulation rate of each individual cell, the upper and lower limits of the SOC of the corresponding individual cells in the energy storage device's battery pack are adjusted. The method for obtaining the initial SOC regulation rate of the single cell is as follows: any single cell is designated as the target single cell, and the average Pearson correlation coefficient between the SOC data of the target single cell and the SOC data of all other single cells in the battery pack is calculated as the change convergence coefficient of the target single cell. The initial SOC regulation rate is calculated by fusing the convergence coefficient of the target single cell with the decay coefficient.

2. The method of claim 1, wherein the method further comprises: The method for obtaining the degradation coefficient of a single battery cell based on the changing trend and magnitude of its SOC data includes the following specific steps: Based on the SOC index and upper and lower limits of any single cell at any time, relative SOC data is obtained by forming relative SOC data from the relative SOC parameters at all times. The relative SOC data of any single cell is decomposed over time, and the relative SOC data is divided into trends based on the periodic characteristics in the decomposition results, resulting in several SOC trend sequence segments of the single cell. The degradation coefficient of the single cell is calculated based on the magnitude and trend of change of all relative SOC trend sequence segments of the single cell.

3. The method of claim 2, wherein the method further comprises: The method for obtaining relative SOC data formed from relative SOC parameters at all times includes: For any single cell, the upper and lower limits of the SOC of the single cell at each time are obtained. Based on the relative position of the SOC index of the single cell at each time within the range corresponding to the upper and lower limits of the SOC at that time, the relative SOC parameter at that time is calculated, thereby obtaining the time-series data of the relative SOC parameter of the single cell at all times, which is denoted as the relative SOC data of the single cell.

4. The method of claim 2, wherein the method further comprises: The method for obtaining several SOC trend sequence segments of the single cell includes: The STL time series decomposition algorithm is used to process the relative SOC data of the individual battery to obtain the trend term, periodic term, and residual term of the relative SOC data. Trigonometric functions are used to perform curve fitting on the periodic term of the relative SOC data to obtain the function corresponding to the fitted curve of the periodic term, which is denoted as the periodic term function. The period value of the periodic term function is obtained, and the trend term of the relative SOC data is divided into several sequence segments using the period value, which are used as the relative SOC trend sequence segments of the individual battery.

5. The method of claim 2, wherein the method further comprises: The method for calculating the degradation coefficient of a single battery cell based on the variation magnitude and trend of all relative SOC trend sequence segments of the single battery cell includes the following specific methods: For any relative SOC trend sequence segment of a single battery cell, a straight line is fitted to the relative SOC trend sequence segment using the least squares method to obtain the slope of the fitted straight line corresponding to the relative SOC trend sequence segment, which is denoted as the relative trend slope of the corresponding relative SOC trend sequence segment. The relative trend slopes corresponding to all relative SOC trend sequence segments of the single battery cell are obtained, and the relative trend slopes are sorted according to the time order of the corresponding SOC trend sequence segments to obtain the relative trend slope sequence of the single battery cell. Based on the changes in the relative trend slope sequence, the degradation coefficient of the single battery cell is calculated.

6. The method of claim 5, wherein the method further comprises: The specific calculation method for the attenuation coefficient of the individual battery cell is as follows: A backward difference sequence of the relative trend slope sequence of a single cell is obtained. Based on the cumulative value of the elements in the backward difference sequence and the numerical dispersion of the elements in the relative trend slope sequence, the attenuation coefficient of the single cell is obtained. Both the cumulative value and the numerical dispersion are positively correlated with the attenuation coefficient.

7. The method of claim 1, wherein the method further comprises: The method for obtaining the relative coefficient of a single battery cell based on the correlation between the SOC change of any single battery cell and charging environment data and discharging environment data includes the following specific methods: Time-align the charging environment data, discharging environment data, and the SOC data of individual batteries; For any single cell, calculate the SOC change rate of the single cell at each time step and form time-series data, denoted as SOC change rate data; Calculate the Pearson correlation coefficient between the SOC change rate data and the charging environment data, and the Pearson correlation coefficient between the SOC change rate data and the discharging environment data, respectively. Based on the Pearson correlation coefficients between the SOC change rate data and the charging environment data and the discharging environment data, the relative coefficients of individual cells were calculated. The Pearson correlation coefficients between the SOC change rate data and the charging environment data were negatively correlated with the relative coefficients, while the Pearson correlation coefficients between the SOC change rate data and the discharging environment data were positively correlated with the relative coefficients.

8. The method of claim 1, wherein the method further comprises: The method for adjusting the upper and lower limits of the SOC of corresponding individual cells in the energy storage device's battery pack by combining the current SOC level of all individual cells and the final SOC regulation rate of the individual cells includes the following specific methods: Obtain the average SOC of all individual cells in the battery pack at the current moment as a reference SOC level; For any single cell, calculate the difference between the current SOC index of the single cell and the reference SOC level, and denot it as the SOC deviation of the single cell. Based on the sign and magnitude of the SOC deviation of a single cell, and combined with the final SOC regulation rate, calculate the lower limit adjustment and upper limit adjustment of the SOC of a single cell. A preset lower limit and a preset upper limit for SOC are established. The lower limit of SOC is increased by adding the adjustment amount of the lower limit of SOC for a single cell to the lower limit of SOC to obtain the new lower limit of SOC. The upper limit of SOC is decreased by the adjustment amount of the upper limit of SOC to obtain the new upper limit of SOC.

9. A photovoltaic energy storage stabilization management system that avoids deep discharge and deep cycling, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the steps of the photovoltaic energy storage stability management method as described in any one of claims 1 to 8.

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