A soh control method, system and storage medium of a reconfigurable energy storage system

Abstract

The application discloses a SOH control method and system of a reconfigurable energy storage system and a storage medium. First, initial operation data is acquired, and it is determined whether the SOH of each battery is less than the SOH health value. If yes, the battery is isolated and SOH control is stopped. Otherwise, the SOH standard deviation of the entire reconfigurable energy storage system at time t, the upper envelope curve, the lower envelope curve, the maximum value and the minimum value are calculated according to the SOH of each battery. Then, the operation condition is determined. If the operation condition is met, normal operation is maintained. Otherwise, numerical fitting is performed to obtain the coefficient matrix of the standard deviation, the coefficient matrix constituted by the duty cycles of all the batteries and the relationship matrix of the standard deviation and the duty cycles of all the batteries. An external adjustment coefficient algorithm is used to modulate each battery duty cycle to obtain a new coefficient matrix constituted by the duty cycles of all the batteries and each battery duty cycle at time t+Delta t, and the new coefficient matrix of each battery SOH and the standard deviation at time t+Delta t is calculated. The above steps are repeated until the operation condition is met to realize SOH control of the reconfigurable energy storage system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery energy storage optimization, and particularly relates to an SOH control method and system of a reconfigurable energy storage system and a storage medium. BACKGROUND

[0002] In a traditional energy storage system, the battery connection topology adopts a fixed series / parallel type, which cannot match the differences of battery monomers caused by dynamic electrochemical behavior, production manufacturing process and application working conditions, and the system "short board effect" problem caused thereby always exists.

[0003] Meanwhile, the battery module composition mode of the traditional battery energy storage system is fixedly welded, and the upper power electronic topology cannot finely control the battery monomers. The reconfigurable battery network composed of a high-frequency power electronic switch array can dynamically reconfigure the connection topology between the batteries to adapt to the dynamic changes of the battery characteristics and the system load. The continuous energy flow of each battery is discretized and digitized by the power electronics, and then the duty cycle and current size of each battery in the system participating in the charging or discharging loop are controlled in real time through an energy control algorithm, so as to meet the needs of different energy storage application scenarios.

[0004] At present, the reconfigurable energy storage system has not been realized in large-scale application, and there is a lack of key technical support in the design of the reconfigurable battery network, the battery state evaluation and the operation optimization. The battery module design based on the reconfigurable battery network can realize accurate control of the battery by using a large number of controllable switch tubes, and provides a solution to the inconsistency of the SOC, SOH and the like of the battery energy storage system. The application provides an SOH control method of a reconfigurable energy storage system, which can optimize the inconsistency problem of the SOH of the energy storage system battery, synchronize the retirement of the energy storage system battery, and improve the capacity utilization rate of the energy storage system. SUMMARY

[0005] The main purpose of the application is to overcome the shortcomings and deficiencies of the prior art, provide an SOH control method, system and storage medium of a reconfigurable energy storage system, and adjust the SOH of the reconfigurable energy storage system to be consistent by adjusting the duty cycle of each battery, so as to ensure the consistency of the SOH of the battery in the system.

[0006] In order to achieve the above purpose, the following technical scheme is adopted in the application:

[0007] On the one hand, an SOH control method of a reconfigurable energy storage system is provided, which comprises the following steps:

[0008] S1, initial operation data of a reconfigurable energy storage system is obtained; the initial operation data includes battery SOH, battery duty cycle D, battery SOH-t curve and battery D-t curve; the reconfigurable energy storage system comprises N battery units, each battery unit comprises M batteries, and each battery is connected with a controllable silicon switch tube;

[0009] S2, judging whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ, if yes, isolating the battery to stop SOH control; if not, entering S3;

[0010] S3, calculating the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t according to the SOH of each battery, and calculating the upper envelope curve H(t), the lower envelope curve h(t), the maximum value σ max (t) and the minimum value σ min (t) of the standard deviation σ(t);

[0011] S4, judging the operating condition according to the maximum value σ max (t), the upper envelope curve H(t) and the lower envelope curve h(t) of the standard deviation σ(t), if σ max (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0, h'(t)≤0 are satisfied, it is judged that the reconfigurable energy storage system at time t is in the normal operating range, the SOH of each battery is relatively consistent and has a trend of becoming more consistent, and the battery and the system are controlled to maintain normal operation; if not, it is transferred to step S5 to adjust the duty cycle of the battery; wherein, ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve H(t), h'(t) is the derivative of the lower envelope curve h(t), and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve;

[0012] S5, performing numerical fitting on the standard deviation σ(t) and the duty cycle of each battery to obtain the coefficient matrix N 1×m (t) of the standard deviation σ(t), the coefficient matrix M n×m (t) composed of all battery duty cycles, and the relationship matrix A 1×n (t) between the standard deviation σ(t) and all battery duty cycles; wherein, n=M×N is the total number of batteries, and m is the order determined by the calculation accuracy after the numerical fitting of the standard deviation σ(t) and the duty cycle of each battery;

[0013] S6, calculating the difference between the maximum value σ max (t) of the standard deviation σ(t) and the upper limit value ε, using an external adjustment coefficient algorithm to modulate the duty cycle of each battery, obtaining a new coefficient matrix M n×m (t+Δt) composed of all battery duty cycles and the duty cycle D of each battery at time t+Δt; wherein, Δt is the calculation period;

[0014] S7, calculating the SOH of each battery at time t+Δt according to the duty cycle D of each battery at time t+Δt, and using the relationship matrix A 1×n (t) between the standard deviation σ(t) and all battery duty cycles and the new coefficient matrix Mn×m (t+Δt) a new coefficient matrix N of the standard deviation σ(t) is calculated 1×m (t+Δt);

[0015] S8, using the SOH of each battery at t+Δt, repeating steps S2-S7 until the operating condition of S4 is met to achieve SOH control of the reconfigurable energy storage system.

[0016] As a preferred technical solution, the battery SOH is represented as:

[0017] SOH i = g i (t), i∈N*, i≤n,

[0018] wherein SOH i represents the SOH of the i-th battery at t, g i (t) is the health degree of the i-th battery at t, N * is a non-zero natural number;

[0019] The battery duty cycle D is represented as:

[0020]

[0021] wherein D i = f i (t) is the duty cycle of the i-th battery at t, k i,j is the j-th coefficient of the i-th battery duty cycle value fitting, which is set according to the battery historical data; o 1i (t) is the m-th order residual term of the i-th battery duty cycle value fitting, and R is a real number;

[0022] The battery SOH-t curve and the battery D-t curve are provided by the battery manufacturer or fitted by the battery management system according to the battery historical data.

[0023] As a preferred technical solution, the SOH standard deviation σ(t) at t is calculated according to the formula:

[0024]

[0025] wherein g i (t) is the health degree of the i-th battery at t, E(t) is the average value of the SOH of all batteries in the reconfigurable energy storage system at t, and is represented as:

[0026]

[0027] wherein n=M×N is the total number of batteries in the reconfigurable energy storage system.

[0028] As a preferred technical solution, the obtaining step of the upper envelope curve H(t) and the lower envelope curve h(t) is:

[0029] Derivation of the standard deviation σ(t) calculates the maximum and minimum of the standard deviation σ(t); the number of maximum values and the number of minimum values are both greater than or equal to 1; if there is only one maximum value and one minimum value, then H(t)=σ(t)=h(t);

[0030] When the number of maximum values or the number of minimum values is not 1, the upper envelope curve H(t) is fitted according to the maximum value of the standard deviation σ(t), and the lower envelope curve h(t) is fitted according to the minimum value of the standard deviation σ(t).

[0031] As a preferred technical solution, the maximum σ max (t) and the minimum σ min (t) of the standard deviation σ(t) are calculated as follows:

[0032] The SOH of the battery at time t is represented by the duty cycle of the battery at time t:

[0033]

[0034] Wherein, SOH i is the SOH of the i-th battery at time t represented by its duty cycle, is the inverse function of the duty cycle of the i-th battery at time t, is the health degree of the i-th battery at time t represented by the inverse function of the duty cycle, D i is the duty cycle of the i-th battery at time t;

[0035] Based on the SOH represented by the duty cycle of the battery at time t and the upper envelope function and the lower envelope function of the standard deviation σ(t) of the SOH at time t, the relationship function between the standard deviation of the battery SOH at time t and the duty cycle of the battery is obtained:

[0036]

[0037] Wherein, is a function of the duty cycle of all batteries at time t, k n+1,j is the j-th coefficient after numerical fitting of the standard deviation σ(t), ε is the upper limit value of the preset standard deviation σ(t), and o2(t) is the m-th remainder after numerical fitting of the standard deviation σ(t);

[0038] Derivation of the relationship function obtains the maximum σ max (t) and the minimum σ min (t) of the standard deviation σ(t).

[0039] As a preferred technical solution, the coefficient matrix N 1×m(t) is expressed as:

[0040] N 1×m (t) = (k n+1,1 … k n+1,j … k n+1,m ),

[0041] where k n+1,j is the jth term coefficient of the numerical fitting of the ith battery duty cycle,

[0042] The coefficient matrix M n×m (t) of all battery duty cycles is expressed as:

[0043]

[0044] where k i,j is the jth term coefficient of the numerical fitting of the ith battery duty cycle;

[0045] The relationship matrix A 1×n (t) between the standard deviation σ(t) and all battery duty cycles is expressed as:

[0046] N 1×m (t) = A 1×n (t)M n×m (t).

[0047] As a preferred technical solution, the external adjustment coefficient algorithm is any one of the equal proportion adjustment coefficient algorithm, the random adjustment coefficient algorithm, the interpolation adjustment coefficient algorithm and the neural network learning adjustment coefficient algorithm.

[0048] As a preferred technical solution, the external adjustment coefficient algorithm is the equal proportion adjustment coefficient algorithm, and the step of modulating each battery duty cycle is:

[0049] First, calculate p = |σ max (t) - ε | / σ max (t) ;

[0050] Then calculate the new coefficient matrix M n×m (t+Δt) of all battery duty cycles at t+Δt, M n×m (t+Δt) = (1-p)M (t) ;

[0051] Then calculate the new coefficient matrix N 1×m (t+Δt) of the standard deviation σ(t) at t+Δt, N 1×n (t+Δt) = A (t)M n×m (t+Δt) ;

[0052] Repeat the above process until σ max (t) - ε < 0.

[0053] In another aspect, a SOH control system of a reconfigurable energy storage system is provided, which is applied to the SOH control method of the reconfigurable energy storage system described above, and includes a data acquisition module, a SOH judgment module, a numerical calculation module, a running judgment module, and a coefficient modulation module.

[0054] The data acquisition module is configured to acquire initial running data of the reconfigurable energy storage system, wherein the initial running data includes battery SOH, battery duty cycle D, battery SOH-t curve, and battery D-t curve; the reconfigurable energy storage system includes N battery units, each of which includes M batteries, and each of the batteries is connected to a controllable silicon switch tube.

[0055] The SOH judgment module is configured to judge whether the SOH of each battery in the reconfigurable energy storage system is less than a SOH health value δ, and if yes, the battery is isolated and the SOH control is stopped; if not, the numerical calculation module is entered.

[0056] The numerical calculation module is configured to calculate the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t according to the SOH of each battery, and calculate the upper envelope curve H(t), the lower envelope curve h(t), the maximum value σ max (t), and the minimum value σ min (t) of the standard deviation σ(t).

[0057] The running judgment module is configured to judge the running condition according to the maximum value σ max (t), the upper envelope curve H(t), and the lower envelope curve h(t) of the standard deviation σ(t), and if σ max (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0, and h'(t)≤0 are satisfied, it is judged that the reconfigurable energy storage system at time t is in a normal running range, the SOH of each battery is relatively consistent and has a trend of becoming more consistent, and the battery and the system are controlled to maintain normal running; if not, the coefficient modulation module is entered to adjust the duty cycle of the battery; wherein ε is a preset upper limit value of the standard deviation σ(t), H'(t) is the derivative of the upper envelope curve H(t), h'(t) is the derivative of the lower envelope curve h(t), and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve.

[0058] The coefficient modulation module is configured to perform numerical fitting on the standard deviation σ(t) and the duty cycle D of each battery, to obtain a coefficient matrix N 1×m (t) of the standard deviation σ(t), a coefficient matrix M n×m (t) of all battery duty cycles, and a relationship matrix A 1×n(t); wherein, n=MxN is the total number of batteries, m is the order determined after fitting the standard deviation sigma (t) and the duty cycle D of each battery to meet the calculation accuracy; the maximum value sigma of the standard deviation sigma (t) is calculated max The difference between the standard deviation sigma (t) and the upper limit value epsilon, the duty cycle of each battery is modulated using an external adjustment coefficient algorithm, and a new coefficient matrix M composed of all battery duty cycles is obtained n×m (t+Delta t) and the duty cycle D of each battery at time t+Delta t; wherein, Delta t is the calculation period; the SOH of each battery at time t+Delta t is calculated according to the duty cycle D of each battery at time t+Delta t, and the relationship matrix A of the standard deviation sigma (t) at time t and all battery duty cycles is used 1×n The new coefficient matrix M composed of the standard deviation sigma (t) and all battery duty cycles n×m The new coefficient matrix N for calculating the standard deviation sigma (t) at time t+Delta t 1×m (t+Delta t); and the SOH of each battery at time t+Delta t is fed back to the SOH judgment module for continuous SOH control until the operation condition of the operation judgment module is met, thereby realizing the SOH control of the reconfigurable energy storage system.

[0059] In still another aspect, a computer readable storage medium is provided, which stores a program, and the program is executed by a processor to realize the SOH control method of the reconfigurable energy storage system.

[0060] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0061] 1. It can quickly bring the battery SOH values ​​in reconfigurable energy storage systems closer to a consistent level, extending the overall service life of the system. In actual projects, differences in factors such as battery cell manufacturing process, initial capacity, environment, temperature, and service time can cause differences in battery capacity, internal resistance, and self-discharge rate, resulting in differences in SOH between battery packs or individual batteries within a pack during operation. When the battery SOH reaches the SOH health value set by the system, it needs to be repaired and replaced in advance, increasing equipment costs. Battery disassembly and replacement will also expand the scope of disassembly and replacement (battery packs are composed of multiple batteries and need to be replaced in groups due to process manufacturing reasons), resulting in a huge waste of resources. Currently, passive balancing, active balancing, and composite balancing are mainly used to improve SOH. Among them, passive balancing technology has a long balancing time and less obvious effect, but is low in cost; active balancing technology has obvious balancing effect, but is more expensive; composite balancing technology is difficult to control. Each of these balancing methods has its own problems and is generally used in non-reconfigurable energy storage systems. The purpose of the present invention is to control the differences in the SOH values ​​of all batteries within a certain range by real-time analysis and calculation of the battery system and by controlling the charge and discharge time and frequency of each battery. The SOH of each battery remains basically consistent throughout the entire life cycle, allowing the entire system to be retired at the same time, avoiding battery replacement during the life cycle, extending the overall service life of the system, and reducing investment and operation and maintenance costs.

[0062] 2. The resulting function curve can serve as a historical experience set, providing a reference for the operating status of other energy storage systems. Since reconfigurable energy storage systems are not currently deployed on a large scale and lack data on the operating status of batteries across the entire system, the data generated by this invention can provide a reference for future reconfigurable energy storage systems. Future reconfigurable energy storage systems can analyze historical data to adjust operating algorithms and optimize design solutions, ensuring safer, more efficient, and longer-term service.

[0063] 3. It can be used as a health status diagnostic tool for reconfigurable energy storage systems. The calculation data of this invention is based on the battery's state of health. During implementation, the state of health of each battery can be monitored. When a battery failure or operating status change occurs, the data can be accurately located and reported in a timely manner. This data serves as a basis for operation and maintenance personnel to conduct maintenance inspections and analyze the cause of the failure. When the health status of the entire battery energy storage system does not meet or is about to meet operating requirements, the operation and maintenance personnel are reminded to formulate a retirement or replacement plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0065] Figure 1 A flow chart of a SOH control method of a reconfigurable energy storage system in an embodiment of the present application.

[0066] Figure 2 A topology diagram of a reconfigurable energy storage system in an embodiment of the present application.

[0067] Figure 3 A block diagram of a SOH control system of a reconfigurable energy storage system in an embodiment of the present application.

[0068] Figure 4 A structure diagram of a computer readable storage medium in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to enable persons skilled in the art to better understand the schemes of the present application, the technical schemes in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor are within the scope of protection of the present application.

[0070] In the present application, the phrase "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be contained in at least one embodiment of the present application. The appearance of this phrase at various places in the specification does not necessarily mean that it refers to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in the present application can be combined with other embodiments.

[0071] As shown in Figure 1 The present embodiment provides a SOH control method of a reconfigurable energy storage system, which comprises the following steps:

[0072] S1, obtaining initial operation data of the reconfigurable energy storage system, including battery SOH, battery duty cycle D, battery cell SOH-t curve and battery D-t curve, etc.

[0073] In the reconfigurable energy storage system, each battery is connected with a thyristor switch tube, and is provided with additional circuits such as a sampling circuit and a protection circuit. The sampling circuit collects various state quantities of the battery during operation, including voltage, current, temperature and the like, and calculates SOC, SOH and other parameters therefrom; the protection circuit provides overvoltage, overcurrent and other protections, isolates the fault battery, and ensures safe and stable operation of the system; by controlling the duty cycle of each thyristor switch tube in the system, the working time and the circuit topology of the battery can be dynamically adjusted. The SOH of the battery is related to the working time and the normal calendar life, therefore, controlling the duty cycle of the battery can affect the SOH of the battery, and when the SOH difference of the batteries in the energy storage system is too large, adjusting the duty cycle of each battery can adjust the SOH of the battery energy storage system to be consistent, thereby ensuring the consistency of the SOH of the batteries in the system. Figure 2 A commonly used reconfigurable energy storage system (additional circuits are not shown) is displayed, which includes N battery units, each battery unit including M batteries, a total of N x M batteries and N x M thyristor switch tubes. The on state of the thyristor switch tube is 1, and the off state is 0, then the running state matrix C of all batteries of the reconfigurable energy storage system can be constructed N×M (t), and the table expression is:

[0074]

[0075] By adjusting C N×M (t), the dynamic series-parallel topology of the battery can be obtained; for a more complex reconfigurable energy storage system, the number of thyristor switch tubes can be increased to achieve the same, and the present application will not be described again.

[0076] For the SOH of the battery, it is a monotonic decreasing function related to time t, which can be expressed as:

[0077] SOH i = g i (t), i∈N*,i≤n (1),

[0078] Wherein, SOH i represents the SOH of the i-th battery at time t, g i (t) is the health degree of the i-th battery at time t, N * is a non-zero natural number;

[0079] For the duty cycle D of the battery, it is expressed as:

[0080]

[0081] Wherein, D i =f i (t) is the duty cycle of the i-th battery at time t, k i,jThe jth coefficient of the fitted duty cycle value of the ith battery can be set according to the battery historical data; t j-1 is the j-1th power of time, which has no practical significance, o 1i (t) is the mth remainder of the fitted duty cycle value of the ith battery, and R is a real number.

[0082] For the battery SOH-t curve and the battery D-t curve, the battery manufacturer can provide or the battery management system can generate them according to the battery historical data.

[0083] S2, determine whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ, if yes, isolate the battery and stop SOH control; if not, go to S3. In this embodiment, δ is a settable SOH health value, when g i (t) is greater than or equal to δ, indicating that the battery can operate normally; when it is less than δ, indicating that the battery has exceeded the life cycle and needs to be removed from operation;

[0084] S3, according to the SOH of each battery, calculate the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t, and calculate the upper envelope curve H(t) and the lower envelope curve h(t) of the standard deviation σ(t), the maximum value σ max (t) and the minimum value σ min (t).

[0085] The formula for calculating the SOH standard deviation σ(t) at time t is:

[0086]

[0087] where g i (t) is the health degree of the ith battery at time t, and E(t) is the average value of the SOH of all batteries in the reconfigurable energy storage system at time t, which is represented as:

[0088]

[0089] where n=M×N is the total number of batteries in the reconfigurable energy storage system.

[0090] Since the standard deviation σ(t) can be a non-monotonic smooth curve, the envelope curve needs to be obtained, and two monotonic smooth curves are constructed to facilitate the judgment in the subsequent steps. For the upper envelope curve H(t) and the lower envelope curve h(t) of σ(t), they are specifically:

[0091] The maximum and minimum values of the standard deviation σ(t) are calculated by taking the derivative of the standard deviation σ(t); the number of maximum values and the number of minimum values are both greater than or equal to 1; if there is only one maximum value and one minimum value, then H(t)=σ(t)=h(t);

[0092] When the number of maximum values or the number of minimum values is not 1, the upper envelope curve H(t) is fitted according to the maximum values of the standard deviation σ(t), and the lower envelope curve h(t) is fitted according to the minimum values of the standard deviation σ(t).

[0093] For the maximum value σ max (t) and the minimum value σ min (t) of σ(t), the process is as follows:

[0094] First, the SOH of the battery at time t is represented by the duty cycle of the battery at time t:

[0095]

[0096] where SOH i is the SOH of the i th battery at time t represented by the duty cycle of the i th battery at time t, is the inverse function of the duty cycle of the i th battery at time t, is the health degree of the i th battery at time t represented by the inverse function of the duty cycle of the i th battery, D i is the duty cycle of the i th battery at time t;

[0097] Then, based on the SOH represented by the duty cycle of the battery at time t and the upper envelope function and the lower envelope function of the standard deviation σ(t) of the SOH at time t, the relationship function between the standard deviation of the SOH of the battery at time t and the duty cycle of the battery is obtained, that is, formula (5) is brought into formula (3):

[0098]

[0099] wherein, is a function of the duty cycle of all batteries at time t, k n+1,j is the j th coefficient after numerical fitting of the standard deviation σ(t), ε is the upper limit value of the preset standard deviation σ(t), and o2(t) is the m th remainder after numerical fitting of the standard deviation σ(t);

[0100] Finally, the relationship function is differentiated to obtain the maximum value σ max (t) and the minimum value σ min (t) of the standard deviation σ(t).

[0101] S4, according to the maximum value σ max (t) of the standard deviation σ(t), the upper envelope curve H(t) and the lower envelope curve h(t), the running condition is judged, and if σ max(t), (H(t)-h(t))'≤0, H'(t)≤0, h'(t)≤0, it is judged that the reconfigurable energy storage system at time t is in the normal operation range, the SOH of each battery is relatively consistent and has a more consistent trend, the battery and the system are maintained in normal operation; if not satisfied, go to step S5 to adjust the duty cycle of the battery; wherein, ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve H(t), h'(t) is the derivative of the lower envelope curve h(t), (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve; the derivative is taken in order to ensure that the SOH of the battery and the reconfigurable energy storage system tends to be consistent.

[0102] S5, σ(t) and each battery duty cycle D are numerically fitted to obtain the coefficient matrix N of the standard deviation σ(t) 1×m (t), the coefficient matrix M composed of all battery duty cycles n×m (t) and the relationship matrix A of the standard deviation σ(t) and all battery duty cycles 1×n (t); wherein, n=MxN is the total number of batteries, m is the order determined by the calculation accuracy after the numerical fitting of the standard deviation σ(t) and each battery duty cycle D.

[0103] Specifically, the coefficient matrix M composed of all NxM battery duty cycles at time t n×m (t) is represented as:

[0104]

[0105] Wherein, k i,j is the jth coefficient after the numerical fitting of the ith battery duty cycle;

[0106] The coefficient matrix N of the standard deviation σ(t) at time t 1×m (t) is represented as:

[0107] N 1×m (t)=(k n+1,1 … k n+1,j … k n+1,m ), (8)

[0108] Wherein, k n+1,j is the jth coefficient after the numerical fitting of the standard deviation σ(t);

[0109] The relationship matrix A of the standard deviation σ(t) and all battery duty cycles 1×n (t) is:

[0110] N 1×m (t)=A 1×n (t)M n×m (t), (9).

[0111] S6, obtaining the maximum value σ of σ(t) max (t) and ε, as the basis for calculation of the external adjustment coefficient algorithm; then using the external adjustment coefficient algorithm to modulate each battery duty cycle to obtain a new coefficient matrix M composed of all battery duty cycles max (t) and a specified value ε, as the basis for calculation of the external adjustment coefficient algorithm; then using the external adjustment coefficient algorithm to modulate each battery duty cycle to obtain a new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) and the duty cycle D of each battery at time t+Δt; wherein Δt is the calculation period.

[0112] Further, the external adjustment coefficient algorithm is any one of the equal proportion adjustment coefficient algorithm, the random adjustment coefficient algorithm, the interpolation adjustment coefficient algorithm and the neural network learning adjustment coefficient algorithm.

[0113] The modulation process of each battery duty cycle using the equal proportion adjustment coefficient algorithm in the embodiment is described in detail as follows:

[0114] First, calculate p=|σ max (t)-ε| / σ max (t);

[0115] Then calculate the new coefficient matrix M composed of all battery duty cycles at time t+Δt n×m (t+Δt)=(1-p)M n×m (t);

[0116] Then calculate the new coefficient matrix N of the standard deviation σ(t) at time t+Δt 1×m (t+Δt)=A 1×n (t)M n×m (t+Δt);

[0117] Repeat the above process until σ max (t)-ε<0.

[0118] S7, according to the duty cycle D of each battery at time t+Δt, calculate the SOH of each battery at time t+Δt, and use the relationship matrix A of the standard deviation σ(t) and all battery duty cycles 1×n (t) and the new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) to calculate the new coefficient matrix N of the standard deviation σ(t) 1×m (t+Δt);

[0119] S8, using the SOH of each battery at time t+Δt, repeat steps S2-S7 until the operating condition of S4 is met to realize the SOH control of the reconfigurable energy storage system.

[0120] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously.

[0121] Based on the same idea as the SOH control method of the reconfigurable energy storage system in the above embodiment, the present application also provides a SOH control system of a reconfigurable energy storage system, which can be used to execute the SOH control method of the reconfigurable energy storage system described above. For the convenience of description, in the structural schematic diagram of an embodiment of a SOH control system of a reconfigurable energy storage system, only the parts related to the embodiments of the present application are shown, and those skilled in the art can understand that the illustrated structure does not constitute a limitation on the device, and can include more or fewer components than the illustrated, or combine certain components, or different component arrangements.

[0122] As shown in Figure 3 , another embodiment of the present application provides a SOH control system of a reconfigurable energy storage system, which includes a data acquisition module, a SOH judgment module, a numerical calculation module, a running judgment module and a coefficient modulation module.

[0123] The data acquisition module is used to acquire the initial running data of the reconfigurable energy storage system, including the battery SOH, the battery duty cycle D, the battery SOH-t curve and the battery D-t curve, etc. The reconfigurable energy storage system contains N battery units, each battery unit contains M batteries, and each battery is connected to a controllable silicon switch tube.

[0124] The SOH judgment module is used to judge whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ, if yes, the battery is isolated and the SOH control is stopped; if not, the numerical calculation module is entered.

[0125] The numerical calculation module is used to calculate the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t according to the SOH of each battery, and to calculate the upper envelope curve H(t), the lower envelope curve h(t), the maximum value σ max (t) and the minimum value σ min (t) of σ(t).

[0126] The running judgment module is used to judge the running condition according to the maximum value σ max (t), the upper envelope curve H(t) and the lower envelope curve h(t) of σ(t), if the following conditions are met: σ maxIf (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0, and h'(t)≤0, then it is judged that the reconfigurable energy storage system is within the normal operating range at time t, the SOH of each battery is relatively consistent and tends to be more consistent, and the battery and system are controlled to maintain normal operation; if not satisfied, the step coefficient modulation module is turned to adjust the duty cycle of the battery; where ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve, h'(t) is the derivative of the lower envelope curve, and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve;

[0127] The coefficient modulation module is used to perform numerical fitting on σ(t) and each battery duty cycle D to obtain the coefficient matrix N of the standard deviation σ(t) 1×m (t), the coefficient matrix M composed of all battery duty cycles n×m The relationship matrix A between (t) and standard deviation σ(t) and duty cycle of all batteries 1×n (t), where n = M × N is the total number of batteries, m is the standard deviation σ(t) and the order determined by the numerical fitting of each battery duty cycle D to meet the calculation accuracy; the maximum value σ(t) is obtained max The difference between (t) and ε is used to modulate the duty cycle of each battery using an external adjustment coefficient algorithm to obtain a new coefficient matrix M consisting of all battery duty cycles. n×m (t+Δt) and the duty cycle D of each battery at time t+Δt; where Δt is the calculation period; calculate the SOH of each battery at time t+Δt based on the duty cycle D of each battery at time t+Δt, and use the relationship matrix A between the standard deviation σ(t) at time t and the duty cycle of all batteries 1×n (t) and the new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) calculates the new coefficient matrix N of the standard deviation σ(t) 1×m (t+Δt); and feed back the SOH of each battery at time t+Δt to the SOH judgment module to continue SOH control until the operating conditions of the operation judgment module are met, thereby realizing SOH control of the reconfigurable energy storage system.

[0128] It should be noted that the SOH control system of a reconfigurable energy storage system of the present invention corresponds one-to-one to the SOH control method of a reconfigurable energy storage system of the present invention. The technical features and beneficial effects described in the embodiment of the SOH control method of the above-mentioned reconfigurable energy storage system are applicable to the embodiment of the SOH control system of a reconfigurable energy storage system. For specific contents, please refer to the description in the embodiment of the method of the present invention. No further details will be given here. This is hereby declared.

[0129] Further, in the implementation of the SOH control system of the reconfigurable energy storage system in the above embodiment, the logical division of each program module is only illustrative. In actual application, the above function allocation can be completed by different program modules according to needs, for example, for the configuration requirements of corresponding hardware or the convenience of software implementation, that is, the internal structure of the SOH control system of the reconfigurable energy storage system is divided into different program modules to complete all or part of the functions described above.

[0130] As shown in Figure 4 In one embodiment, a computer readable storage medium is provided, which stores a program in the memory, and the program is executed by a processor to implement the SOH control method of the reconfigurable energy storage system, specifically:

[0131] S1, obtaining initial operation data of the reconfigurable energy storage system, including battery SOH, battery duty cycle D, battery SOH-t curve and battery D-t curve, etc. The reconfigurable energy storage system contains N battery units, each battery unit contains M batteries, and each battery is connected with a controllable silicon switch tube;

[0132] S2, judging whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ, if yes, isolating the battery to stop SOH control; if not, entering S3;

[0133] S3, calculating the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t according to the SOH of each battery, and calculating the upper envelope curve H(t), the lower envelope curve h(t), the maximum value σ max (t) and the minimum value σ min (t) of σ(t);

[0134] S4, judging the operation condition according to the maximum value σ max (t), the upper envelope curve H(t) and the lower envelope curve h(t) of σ(t), if σ max (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0, h'(t)≤0, it is judged that the reconfigurable energy storage system at time t is in the normal operation range, the SOH of each battery is relatively consistent and has a trend of more consistency, and the battery and the system are controlled to maintain normal operation; if not, entering step S5 to adjust the battery duty cycle; wherein ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve, h'(t) is the derivative of the lower envelope curve, and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve;

[0135] S5, performing numerical fitting on the standard deviation σ(t) and each battery duty cycle D to obtain the coefficient matrix N 1×m(t), the coefficient matrix M composed of all battery duty cycles n×m (t) and the relationship matrix A of standard deviation σ(t) and all battery duty cycles 1×n (t); wherein n=M×N is the total number of batteries, m is the order determined after fitting the values of standard deviation σ(t) and each battery duty cycle D to meet the calculation accuracy;

[0136] S6, find the maximum value σ of σ(t) max (t) and ε, use external adjustment coefficient algorithm to modulate each battery duty cycle, obtain new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) and each battery duty cycle D at t+Δt; wherein Δt is the calculation period;

[0137] S7, calculate each battery SOH at t+Δt according to each battery duty cycle D at t+Δt, and use the relationship matrix A of standard deviation σ(t) and all battery duty cycles 1×n (t) and new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) to calculate new coefficient matrix N of standard deviation σ(t) 1×m (t+Δt);

[0138] S8, use each battery SOH at t+Δt to repeat steps S2-S7 until the operation condition of S4 is met to realize SOH control of the reconfigurable energy storage system.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0141] The above embodiments are the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above embodiments, and any changes, modifications, substitutions, combinations and simplifications made without departing from the spirit and principles of the present application shall be equivalent replacement methods and shall be within the scope of protection of the present application.

Claims

1. A SOH control method for a reconfigurable energy storage system, characterized in that: The steps include: S1. Obtaining initial operating data of a reconfigurable energy storage system; the initial operating data includes battery SOH, battery duty cycle D, battery SOH-t curve, and battery Dt curve; the reconfigurable energy storage system includes N battery cells, each battery cell includes M batteries, and each battery is connected to a thyristor switch tube; S2, determining whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ, if so, isolating the battery and stopping SOH control; if not, proceeding to S3; S3. Calculate the SOH standard deviation σ(t) of the entire reconfigurable energy storage system at time t based on the SOH of each battery, and calculate the upper envelope curve H(t), lower envelope curve h(t), and maximum value σ max (t) and the minimum value σ min (t); S4, according to the maximum value σ(t) max (t), upper envelope curve H(t) and lower envelope curve h(t), judge the operating conditions, if σ max If (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0, and h'(t)≤0, then it is judged that the reconfigurable energy storage system is within the normal operating range at time t, the SOH of each battery is relatively consistent and tends to be more consistent, and the battery and system are controlled to maintain normal operation; if not satisfied, then the process proceeds to step S5 to adjust the battery duty cycle; wherein ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve H(t), h'(t) is the derivative of the lower envelope curve h(t), and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve; S5, numerical fitting is performed on the standard deviation σ(t) and each battery duty cycle D to obtain a coefficient matrix N of the standard deviation σ(t) 1×m (t), a coefficient matrix M of all battery duty cycles n×m (t), and a relationship matrix A of the standard deviation σ(t) and all battery duty cycles 1×n (t); wherein n=MxN is the total number of batteries, and m is an order determined according to the calculation accuracy after numerical fitting of the standard deviation σ(t) and each battery duty cycle D S6, obtaining the maximum value σ of the standard deviation σ(t) max (t) and the upper limit value ε, using an external adjustment coefficient algorithm to modulate each battery duty cycle to obtain a new coefficient matrix M composed of all battery duty cycles n×m (t+Δt) and the duty cycle D of each battery at time t+Δt; wherein Δt is a calculation period S7. Calculate SOH of each battery at t+Δt using the duty cycle D of each battery at t+Δt and the matrix A of standard deviation σ(t) and all duty cycles 1×n (t) and all duty cycles of the battery n×m (t+Δt) to calculate the new coefficient matrix N of standard deviation σ(t) 1×m (t+Δt) S8. Using the SOH of each battery at time t+Δt, repeat steps S2-S7 until the operating conditions of S4 are met to achieve SOH control of the reconfigurable energy storage system.

2. The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The battery SOH is expressed as: SOH i = g i (t), i e N*, i ≤ n, wherein SOH i represents the SOH of the i-th battery at time t, g i (t) is the health degree of the i-th battery at time t, N * is a non-zero natural number; The battery duty cycle D is expressed as: wherein D i = f i (t) is the duty cycle of the i-th battery at time t, k i,j is the j-th coefficient of the i-th battery duty cycle value fitting, which is set according to the battery historical data;o 1i (t) is the m-th remainder of the i-th battery duty cycle value fitting, and R is a real number. The battery SOH-t curve and battery Dt curve are provided by the battery manufacturer or generated by the battery management system based on historical battery data. 3.The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The calculation formula of the SOH standard deviation σ(t) at time t is: where g i (t) is the health degree of the i-th battery at time t, E(t) is the average value of the SOH of all batteries in the reconfigurable energy storage system at time t, and is expressed as: Wherein, n=M×N is the total number of batteries in the reconfigurable energy storage system. 4.The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The steps for obtaining the upper envelope curve H(t) and the lower envelope curve h(t) are as follows: Derivative the standard deviation σ(t) to calculate the maximum and minimum values ​​of the standard deviation σ(t); the number of the maximum and minimum values ​​is greater than or equal to 1; if there is only one maximum and one minimum, then H(t) = σ(t) = h(t); When the number of maxima or minima is not 1, the upper envelope curve H(t) is fitted according to the maximum value of the standard deviation σ(t), and the lower envelope curve h(t) is fitted according to the minimum value of the standard deviation σ(t).

5. The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The maximum value σ max (t) of the standard deviation σ(t) and the minimum value σ min (t) are calculated as follows: The battery duty cycle at time t is used to express the battery SOH at time t: wherein SOH i is the SOH of the i-th battery at time t expressed using its duty cycle, is the inverse function of the duty cycle of the i-th battery at time t, is the health of the i-th battery at time t expressed using the inverse function of the duty cycle, D i is the duty cycle of the i-th battery at time t; Based on the upper envelope function and lower envelope function of the SOH represented by the battery duty cycle at time t and the SOH standard deviation σ(t) at time t, the relationship function between the battery SOH standard deviation at time t and the battery duty cycle is obtained: wherein, is a function of the duty cycle of all batteries at time t, k n+1,j is the jth coefficient of the numerical fitting of the standard deviation σ(t), ε is the upper limit value of the preset standard deviation σ(t), and o2(t) is the mth residual term of the numerical fitting of the standard deviation σ(t). Differentiating the relationship function gives the maximum value σ max (t) and the minimum value σ min (t) of the standard deviation σ(t).

6. The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The coefficient matrix N of the standard deviation σ(t) 1×m (t) is expressed as: N 1×m (t) = (k n+1,1 … k n+1,j … k n+1,m ), where k n+1,j is the jth coefficient of the standard deviation σ(t) numerical fit, The coefficient matrix M of all battery duty cycles n×m (t) is represented as: where k i,j is the jth coefficient of the fitted duty cycle value for the ith battery. The standard deviation σ(t) is related to the matrix A of all battery duty cycles 1×n (t) is expressed as: N 1×m (t) = A 1×n (t)M n×m (t).

7. The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The external adjustment coefficient algorithm is any one of a proportional adjustment coefficient algorithm, a random adjustment coefficient algorithm, an interpolation adjustment coefficient algorithm and a neural network learning adjustment coefficient algorithm. 8.The SOH control method of a reconfigurable energy storage system according to claim 1, wherein, The external adjustment coefficient algorithm is a proportional adjustment coefficient algorithm, and the steps for modulating the duty cycle of each battery are as follows: First calculate p=|σ max (t)-ε| / σ max (t); Then the new coefficient matrix M composed of all battery duty cycles at time t+Δt is calculated n×m (t+Δt) = (1-p)M n×m (t); N 1×m (t+Δt) = A 1×n (t)M n×m (t+Δt); The above process is repeated until σ max (t) - ε < 0.

9. A SOH control system of a reconfigurable energy storage system, characterized in that, A SOH control method for a reconfigurable energy storage system applied to any one of claims 1-8, comprising a data acquisition module, an SOH judgment module, a numerical calculation module, an operation judgment module and a coefficient modulation module; The data acquisition module is used to acquire initial operating data of the reconfigurable energy storage system; the initial operating data includes battery SOH, battery duty cycle D, battery SOH-t curve and battery Dt curve; the reconfigurable energy storage system includes N battery cells, each battery cell includes M batteries, and each battery is connected to a thyristor switch tube; The SOH judgment module is used to judge whether the SOH of each battery in the reconfigurable energy storage system is less than the SOH health value δ. If so, the battery is isolated and the SOH control is stopped; if not, the numerical calculation module is entered; The numerical calculation module is configured to calculate the standard deviation σ(t) of the SOH of the entire reconfigurable energy storage system at time t according to the SOH of each battery, and calculate the upper envelope curve H(t), the lower envelope curve h(t), the maximum value σ max (t) and the minimum value σ min (t) of the standard deviation σ(t). The operation judging module is configured to judge the operation condition according to the maximum value σ max (t) of σ(t), the upper envelope curve H(t) and the lower envelope curve h(t). If σ max (t)≤ε, (H(t)-h(t))'≤0, H'(t)≤0 and h'(t)≤0 are satisfied, it is judged that the energy storage system at time t is in the normal operation range, the SOH of each battery is relatively consistent and has a more consistent trend, and the battery and the system are controlled to maintain normal operation. If the conditions are not satisfied, the step coefficient modulation module is entered to adjust the duty cycle of the battery. ε is the upper limit value of the preset standard deviation σ(t), H'(t) is the derivative of the upper envelope curve H(t), h'(t) is the derivative of the lower envelope curve h(t), and (H(t)-h(t))' is the derivative of the difference between the upper envelope curve and the lower envelope curve. The coefficient modulation module is configured to perform numerical fitting on the standard deviation σ(t) and each battery duty cycle D to obtain a coefficient matrix N of the standard deviation σ(t) 1×m (t), a coefficient matrix M of all battery duty cycles n×m (t), and a relationship matrix A of the standard deviation σ(t) and all battery duty cycles 1×n (t); wherein n=M×N is the total number of batteries, m is an order determined according to a calculation accuracy after numerical fitting of the standard deviation σ(t) and each battery duty cycle D; the maximum value σ max (t) and the upper limit value ε, modulates each battery duty cycle using an external adjustment coefficient algorithm to obtain a new coefficient matrix M of all battery duty cycles n×m (t+Δt) and the duty cycle D of each battery at the time t+Δt; wherein Δt is a calculation period; the SOH of each battery at the time t+Δt is calculated according to the duty cycle D of each battery at the time t+Δt, and the relationship matrix A of the standard deviation σ(t) and all battery duty cycles at the time t is used 1×n (t+Δt) and all battery duty cycles n×m (t+Δt) to obtain a new coefficient matrix N of the standard deviation σ(t) 1×m (t+Δt); and the SOH of each battery at the time t+Δt is fed back to the SOH judgment module for continuous SOH control until the operation condition of the operation judgment module is met, thereby realizing SOH control of the reconfigurable energy storage system.

10. A computer-readable storage medium storing a program, characterized in that: The program is executed by the processor to implement the SOH control method of the reconfigurable energy storage system according to any one of claims 1-8.

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