A battery management method and system for grid-type energy storage

Abstract

This invention relates to the field of battery management technology, specifically disclosing a battery management method and system for grid-based energy storage. This application first obtains the battery degradation coefficient through historical operating parameters of the battery, and then acquires the control parameters of the grid-based energy storage device based on the grid-based energy storage system. The control parameters of the grid-based energy storage device include charging rate control information and power loss control information. Next, a full-charge control coefficient can be obtained from the charging rate control information, and then a power loss control coefficient can be obtained from the power loss control information. These full-charge control coefficients and power loss control coefficients can be input into an equalization charging model for training to obtain equalization control values. Then, based on these equalization control values, multiple batteries with fluctuating performance are sequentially equalized, thereby avoiding the problem of performance fluctuations caused by degraded batteries affecting the stable operation of the entire energy storage system during energy storage.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a battery management method and system for grid-type energy storage. Background Technology

[0002] Grid-based energy storage refers to a technology that stores electrical energy in the power grid to balance the supply and demand differences in the power system and improve the stability and reliability of the power grid. This type of energy storage system usually uses batteries to store electrical energy and releases the electrical energy to the grid when needed or absorbs the excess electrical energy when the supply exceeds the demand through battery management. Grid-based energy storage plays an important role in the power system and can help solve problems such as grid dispatch, frequency control, peak-valley balance, etc. At the same time, it can also promote the large-scale application of renewable energy and the development of smart grids.

[0003] When electrical energy in the grid is stored in batteries, the losses generated by the batteries during multiple charging and discharging processes are different. The batteries with losses will experience performance fluctuations when storing energy, and the performance fluctuations of the batteries will affect the operational stability of the entire energy storage system. Therefore, a battery management method for grid-based energy storage is needed to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a battery management method and system for grid-type energy storage to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A battery management method for grid-connected energy storage, applied to grid energy storage devices, includes:

[0007] Obtain the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electrical cycles, charging current and charging temperature;

[0008] Multiple battery degradation prediction models are trained by sequentially inputting multiple battery degradation coefficients, multiple electric cycle counts, multiple charging currents, and multiple charging temperatures for each battery.

[0009] The control parameters of the grid energy storage device are obtained based on the grid-type energy storage system. The control parameters of the grid energy storage device include charging rate control information and power loss control information. The grid-type energy storage system is a system used to control the battery energy storage balance in the grid energy storage device.

[0010] The full charge control coefficient is obtained based on the charging rate control information, and the power loss control coefficient is obtained based on the power loss control information.

[0011] The full charge control coefficient and the power loss control coefficient are input into the equalization charging model for training to obtain the equalization control value.

[0012] Sequentially determine whether the degradation coefficients of multiple batteries are within a preset range;

[0013] If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance.

[0014] The battery with fluctuating performance is balanced according to the aforementioned balance control value.

[0015] Preferably, the step of sequentially inputting multiple electrical cycle counts, multiple charging currents, and multiple charging temperatures of each battery into the battery degradation prediction model for training to obtain multiple battery degradation coefficients includes:

[0016] Based on the multiple electric cycle counts, obtain the corresponding battery's multiple electric cycle count weight values;

[0017] Multiple charging current weight values ​​for the corresponding battery are obtained based on the multiple charging currents.

[0018] Based on the multiple charging temperatures, obtain multiple charging temperature weight values ​​for the corresponding battery;

[0019] For each battery, multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights are sequentially input into the battery degradation prediction model for training, resulting in multiple battery degradation coefficients. The function formula for the battery degradation prediction model is as follows:

[0020]

[0021] Where β(o...p) represents the battery degradation coefficient corresponding to the 0th to pth batteries, c(s)i represents the number of electrical cycles of the i-th battery, ai represents the weight value of the number of electrical cycles of the i-th battery, d(l)i represents the charging current of the i-th battery, bi represents the weight value of the charging current of the i-th battery, w(d)i represents the charging temperature of the i-th battery, and ci represents the weight value of the charging temperature of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0th to pth batteries, o = 1, 2, 3...p.

[0022] Preferably, the step of obtaining the full-charge control coefficient based on the charging rate control information includes:

[0023] The energy storage charging power and upper limit charging power of the grid energy storage device within a preset time are obtained based on the charging rate control information.

[0024] Get the remaining power value of each battery before charging;

[0025] Obtain the start and end times of charging for each battery;

[0026] The final energy storage capacity is calculated based on the remaining energy value, energy storage charging power, maximum charging power, battery degradation coefficient, start charging time, and end charging time. The calculation formula is as follows:

[0027]

[0028] Where E2 represents the final energy storage capacity, E1 represents the remaining energy capacity, and c(n) represents the energy storage charging power. max t1 represents the upper limit of charging power, β represents the battery degradation coefficient, t1 represents the start time of charging, and t2 represents the end time of charging.

[0029] The full-charge control coefficient is calculated based on the final energy storage capacity and the remaining energy capacity. The calculation formula is as follows:

[0030]

[0031] Where M(C) represents the full charge control coefficient.

[0032] Preferably, the step of obtaining the power loss control coefficient based on the power loss control information includes:

[0033] The phase current, phase voltage, active load of end energy storage, and reactive load of end energy storage are obtained based on the power loss control information.

[0034] The load influence coefficient is calculated based on the active and reactive loads of the end-point energy storage, wherein the calculation formula is:

[0035]

[0036] Where Y(s) represents the load influence coefficient, F1 represents the active load of the end-point energy storage, and F2 represents the reactive load of the end-point energy storage;

[0037] The power loss control coefficient is calculated based on the phase current, phase voltage, and load influence coefficient, wherein the calculation formula is:

[0038] P(s) = 3 * V * I * Y(s);

[0039] Where P(s) represents the power loss control coefficient in three-phase electricity, V represents the phase voltage, I represents the phase current, and Y(s) represents the load influence coefficient.

[0040] Preferably, the step of inputting the full-charge control coefficient and the power loss control coefficient into the equalization charging model for training to obtain the equalization control value includes:

[0041] The corresponding full charge control weight value is obtained based on the full charge control coefficient.

[0042] The corresponding power loss control weight value is obtained based on the power loss control power coefficient.

[0043] Based on the deviation coefficient of the control parameters of grid energy storage devices obtained from the grid-type energy storage system;

[0044] The full-charge control coefficient, power loss control coefficient, full-charge control weight value, power loss control weight value, and deviation coefficient are input into the equalization charging model for training to obtain the equalization control value. The function formula for the equalization charging model is:

[0045] j(h)=[M(C)*d+P(s)*e]*θ;

[0046] Where s(c) represents the balance control value, M(C) represents the full charge control coefficient, d represents the full charge control weight value, P(s) represents the power loss control coefficient, e represents the power loss control weight value, and θ represents the deviation coefficient of the control parameters of the grid energy storage device.

[0047] Preferably, the step of balancing the battery performance fluctuations based on the balancing control value further includes:

[0048] Based on the equalization control value, the charging gradient of each battery with performance fluctuation is divided to obtain multiple gradient control intervals;

[0049] Warning thresholds are set for each of the multiple gradient control intervals;

[0050] The BMS is activated based on the equilibrium control value to sequentially perform equilibrium adjustment on multiple gradient control intervals. The BMS is applied in a grid-type energy storage system. The equilibrium adjustment can only be performed on the next gradient control interval after the warning threshold is met, until all multiple gradient control intervals are balanced.

[0051] This application also provides a battery management system for grid-based energy storage, including:

[0052] The first acquisition module is used to acquire the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electric cycles, charging current and charging temperature;

[0053] The first training module is used to input multiple electrical cycle counts, multiple charging currents, and multiple charging temperatures of each battery into the battery degradation prediction model for training, and to obtain multiple battery degradation coefficients.

[0054] The second acquisition module is used to acquire control parameters of grid energy storage devices based on the grid-type energy storage system. The control parameters of the grid energy storage devices include charging rate control information and power loss control information. The grid-type energy storage system is a system used to control the battery energy storage balance in the grid energy storage devices.

[0055] The third acquisition module is used to acquire the full charge control coefficient based on the charging rate control information and to acquire the power loss control coefficient based on the power loss control information.

[0056] The second training module is used to input the full charge control coefficient and the power loss control coefficient into the equalization charging model for training, so as to obtain the equalization control value.

[0057] The first judgment module is used to sequentially judge whether multiple battery degradation coefficients are within a preset range;

[0058] If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance.

[0059] The first equalization control module is used to equalize the battery with performance fluctuations according to the equalization control value.

[0060] Preferably, the first training module includes:

[0061] The first acquisition unit is used to acquire multiple electric cycle number weight values ​​of the corresponding battery based on the multiple electric cycle number;

[0062] The second acquisition unit is used to acquire multiple charging current weight values ​​of the corresponding battery based on the multiple charging currents.

[0063] The third acquisition unit is used to acquire multiple charging temperature weight values ​​of the corresponding battery based on the multiple charging temperatures.

[0064] The first calculation unit is used to sequentially input multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights of each battery into the battery degradation prediction model for training, thereby obtaining multiple battery degradation coefficients. The function formula of the battery degradation prediction model is as follows:

[0065]

[0066] Where β(o...p) represents the battery degradation coefficient corresponding to the 0th to pth batteries, c(s)i represents the number of electrical cycles of the i-th battery, ai represents the weight value of the number of electrical cycles of the i-th battery, d(l)i represents the charging current of the i-th battery, bi represents the weight value of the charging current of the i-th battery, w(d)i represents the charging temperature of the i-th battery, and ci represents the weight value of the charging temperature of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0th to pth batteries, o = 1, 2, 3...p.

[0067] Preferably, the third acquisition module includes:

[0068] The fourth acquisition unit is used to acquire the energy storage charging power and the upper limit charging power of the grid energy storage device within a preset time according to the charging rate control information.

[0069] The fifth acquisition unit is used to acquire the remaining power value of each battery before charging;

[0070] The sixth acquisition unit is used to acquire the start and end times of charging for each battery.

[0071] The second calculation unit is used to calculate the final energy storage capacity based on the remaining energy value, energy storage charging power, upper limit charging power, battery degradation coefficient, start charging time, and end charging time, wherein the calculation formula is:

[0072]

[0073] Where E2 represents the final energy storage capacity, E1 represents the remaining energy capacity, and c(n) represents the energy storage charging power. max t1 represents the upper limit of charging power, β represents the battery degradation coefficient, t1 represents the start time of charging, and t2 represents the end time of charging.

[0074] The third calculation unit is used to calculate the full-charge control coefficient based on the final energy storage capacity and the remaining energy capacity. The calculation formula is as follows:

[0075]

[0076] Where M(C) represents the full charge control coefficient.

[0077] Preferably, the third acquisition module includes:

[0078] The fourth calculation unit is used to calculate the load influence coefficient based on the active load and reactive load of the end-point energy storage, wherein the calculation formula is:

[0079]

[0080] Where Y(s) represents the load influence coefficient, F1 represents the active load of the end-point energy storage, and F2 represents the reactive load of the end-point energy storage;

[0081] The seventh acquisition unit is used to acquire the resistance value of the input power line in the power grid energy storage device;

[0082] The fifth calculation unit is used to calculate the power loss control coefficient based on the phase current, phase voltage, and load influence coefficient, wherein the calculation formula is:

[0083] P(s) = 3 * V * I * Y(s);

[0084] Where P(s) represents the power loss control coefficient in three-phase electricity, V represents the phase voltage, I represents the phase current, and Y(s) represents the load influence coefficient.

[0085] The beneficial effects of this application are as follows: First, this application obtains the battery degradation coefficient through the historical operating parameters of the battery, and then obtains the control parameters of the grid energy storage device based on the grid-type energy storage system. The control parameters of the grid energy storage device include charging rate control information and power loss control information. Then, the full charge control coefficient can be obtained through the charging rate control information, and then the power loss control power coefficient can be obtained through the power loss control information. In this way, the full charge control coefficient and the power loss control power coefficient can be input into the equalization charging model for training to obtain the equalization control value. Then, based on the equalization control value, multiple batteries with fluctuating performance are sequentially equalized, thereby avoiding the problem that the performance fluctuation of the degraded battery during energy storage will affect the stable operation of the entire energy storage system. Attached Figure Description

[0086] Figure 1 This is a schematic flowchart of a battery management method for grid-type energy storage according to an embodiment of this application.

[0087] Figure 2 This is a schematic diagram of a grid-type energy storage battery management system according to an embodiment of this application.

[0088] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0089] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0090] like Figure 1-2 As shown, this application provides a battery management method for grid-connected energy storage, applied to grid energy storage devices. The method includes:

[0091] S1. Obtain the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electric cycles, charging current and charging temperature;

[0092] S2. Input multiple electrical cycle counts, multiple charging currents, and multiple charging temperatures of each battery into the battery degradation prediction model for training to obtain multiple battery degradation coefficients.

[0093] S3. Obtain control parameters of grid energy storage devices based on grid-type energy storage system, wherein the control parameters of grid energy storage devices include charging rate control information and power loss control information, wherein the grid-type energy storage system is a system used to control the battery energy storage balance in grid energy storage devices;

[0094] S4. Obtain the full charge control coefficient based on the charging rate control information, and obtain the power loss control coefficient based on the power loss control information;

[0095] S5. Input the full charge control coefficient and the power loss control coefficient into the equalization charging model for training to obtain the equalization control value;

[0096] S6. Sequentially determine whether multiple battery degradation coefficients are within the preset range;

[0097] If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance.

[0098] S7. Perform equalization control on the battery with performance fluctuations according to the equalization control value.

[0099] As described in steps S1-S7 above, when electrical energy in the grid is stored in a battery, the losses generated by the battery during multiple charge-discharge cycles vary. Batteries with losses will experience performance fluctuations during energy storage, which can affect the overall stability of the energy storage system. Therefore, it is necessary to first determine whether the battery degradation coefficient is within a reasonable range. Based on this, this application first obtains the historical operating parameters of each battery in the current grid energy storage device. These historical operating parameters include the number of charge cycles, charging current, and charging temperature. By comprehensively obtaining the battery degradation coefficient based on these key operating parameters, the number of charge cycles refers to the cumulative number of times the battery reaches a full charge and discharge state during multiple charge-discharge cycles. For example, one discharge cycle is... 20%, meaning it would require 5 discharges of 20% to complete one full discharge. The specific steps for obtaining the battery degradation coefficient are as follows: Input multiple battery cycle counts, multiple charging currents, and multiple charging temperatures for each battery into the battery degradation prediction model for training, obtaining multiple battery degradation coefficients. These multiple coefficients can then be used to determine whether the battery is within a preset degradation range, thus quickly identifying batteries requiring equalization control. Next, control parameters for the grid energy storage device are obtained based on the grid-based energy storage system. These control parameters include charging rate control information and power loss control information. The grid-based energy storage system is used to control the battery energy storage equalization within the grid energy storage device. The following steps require obtaining... The main parameters for battery regulation are those obtained from historical battery balancing processes. Specifically, charging rate control information allows the acquisition of the grid-connected energy storage device's energy storage charging power and upper limit charging power within a preset time. A full-charge control coefficient is calculated based on this energy storage charging power, upper limit charging power, and battery degradation coefficient. This full-charge control coefficient represents the control coefficient for a normal charge. This coefficient allows for targeted regulation of the battery's full-charge time, slowing down charging efficiency and reducing battery performance fluctuations. Then, based on the power loss control information, the phase current, phase voltage, active load at the end of the energy storage line, and reactive load at the end of the energy storage line are obtained. The phase current... The power loss control coefficient is calculated based on phase voltage, active load of end-point energy storage, and reactive load of end-point energy storage. This power loss control coefficient, after eliminating power losses, comprehensively regulates battery performance. The main control involves further precise calculation of the actual energy storage capacity during the energy storage process after obtaining the power loss control coefficient. This allows for precise control of battery energy storage, preventing performance fluctuations from affecting the overall stability of the energy storage system. After obtaining the precise control parameters, the full-charge control coefficient and the power loss control coefficient are input into the equalization charging model for training to obtain equalization control values. Then, multiple battery degradation coefficients are sequentially determined to be within a preset range.If the battery degradation coefficient is not within the preset range, the battery corresponding to the degradation coefficient is determined to have performance fluctuations. After the judgment, multiple batteries with performance fluctuations are obtained, and these batteries are sequentially balanced according to the balance control value. This avoids the problem of performance fluctuations caused by damaged batteries affecting the stable operation of the entire energy storage system. Specifically, the balance control is implemented as follows: when the battery capacity is 5kWh, 10kWh, and 15kWh, the battery degradation coefficients of the corresponding batteries are calculated to be 0.5, 0.2, and 0.3 based on the number of electric cycles, charging current, and charging temperature. It can then be determined whether 0.5, 0.2, and 0.3 are within the preset range; if not, the corresponding battery is determined to have performance fluctuations. The process involves resolving the underlying issues, then acquiring control parameters for the grid-connected energy storage devices based on the grid-connected energy storage system. These parameters are used to sequentially obtain the full-charge control coefficient and the power loss control coefficient. The actual stored energy capacity is then obtained under the influence of the power loss control coefficient. These coefficients are then input into the equalization charging model for training, yielding equalization control values. This allows for sequential division of charging gradients for 5kWh, 10kWh, and 15kWh, for example, a 20% gradient with a warning threshold set at 20%. Based on the equalization control values, the BMS (Battery Management System) is activated to sequentially adjust the multiple gradient control intervals. The numbers mentioned above are for illustrative purposes only and are not definitive representations.

[0100] In one embodiment, step S2, which involves sequentially inputting multiple battery cycle counts, multiple charging currents, and multiple charging temperatures into a battery degradation prediction model for training to obtain multiple battery degradation coefficients, includes:

[0101] S201. Obtain multiple battery cycle weight values ​​corresponding to the multiple battery cycle counts based on the multiple battery cycle counts;

[0102] S202. Obtain multiple charging current weight values ​​for the corresponding battery based on the multiple charging currents;

[0103] S203. Obtain multiple charging temperature weight values ​​for the corresponding battery based on the multiple charging temperatures;

[0104] S204. Input the multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights of each battery sequentially into the battery degradation prediction model for training, thereby obtaining multiple battery degradation coefficients. The function formula for the battery degradation prediction model is:

[0105]

[0106] Where β(o...p) represents the battery degradation coefficient corresponding to the 0th to pth batteries, c(s)i represents the number of electrical cycles of the i-th battery, ai represents the weight value of the number of electrical cycles of the i-th battery, d(l)i represents the charging current of the i-th battery, bi represents the weight value of the charging current of the i-th battery, w(d)i represents the charging temperature of the i-th battery, and ci represents the weight value of the charging temperature of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0th to pth batteries, o = 1, 2, 3...p.

[0107] As described in steps S201-S204 above, since the actual capacity of the battery determines the speed at which the power grid stores energy from the battery, this application first obtains the corresponding weight value of the number of electric cycles based on the multiple electric cycle counts, then obtains the corresponding weight value of the charging current based on the multiple charging currents, and then obtains the corresponding weight value of the charging temperature based on the multiple charging temperatures. Next, the multiple electric cycle counts, multiple charging currents, multiple charging temperatures, the weight values ​​of the electric cycle counts, the weight values ​​of the charging currents, and the weight values ​​of the charging temperatures are input into the battery degradation prediction model for training, resulting in multiple battery degradation coefficients. After obtaining these multiple battery degradation coefficients, it is possible to determine whether a corresponding battery exhibits degradation anomalies based on these coefficients, and to quickly identify abnormal batteries. This allows for rapid control and adjustment of abnormal batteries to prevent performance fluctuations from affecting the normal operation of the entire energy storage system.

[0108] In one embodiment, step S4, which involves obtaining the full-charge control coefficient based on the charging rate control information, includes:

[0109] S401. Obtain the energy storage charging power and upper limit charging power of the grid energy storage device within a preset time according to the charging rate control information.

[0110] S402, Obtain the remaining power value of each battery before charging;

[0111] S404. Obtain the start and end charging times for each battery.

[0112] S405. Calculate the final energy storage capacity based on the remaining energy value, energy storage charging power, upper limit charging power, battery degradation coefficient, start charging time, and end charging time, wherein the calculation formula is:

[0113]

[0114] Where E2 represents the final energy storage capacity, E1 represents the remaining energy capacity, and c(n) represents the energy storage charging power. maxt1 represents the upper limit of charging power, β represents the battery degradation coefficient, t1 represents the start time of charging, and t2 represents the end time of charging.

[0115] S406. Calculate the full-charge control coefficient based on the final energy storage capacity and the remaining energy capacity, wherein the calculation formula is:

[0116]

[0117] Where M(C) represents the full charge control coefficient.

[0118] As described in steps S401-S406 above, since the full-charge control coefficient of the battery determines the battery's charging efficiency, the charging time can be estimated after obtaining the charging efficiency. Furthermore, by controlling the battery charging duration and charging power, the question of how much charge to give in a given time can be resolved. Therefore, the full-charge control coefficient is a crucial control parameter for the battery. Based on this, the energy storage charging power and upper limit charging power of the grid energy storage device within a preset time can be obtained first, according to the charging rate control information. Then, the remaining charge value of each battery before charging is obtained. Next, the start and end charging times of each battery are obtained. Finally, the calculation is performed based on the remaining charge value, energy storage charging power, upper limit charging power, battery degradation coefficient, start charging time, and end charging time. The final energy storage capacity is calculated as the actual energy storage capacity of the battery. This actual energy storage capacity allows for accurate adjustment of charging parameters, which in turn helps manage battery performance fluctuations. Then, a full-charge control coefficient is calculated based on the final energy storage capacity and the remaining capacity. This coefficient allows for precise control of the charging capacity, enabling balanced regulation of fluctuations across multiple batteries. This prevents performance fluctuations from negatively impacting the overall energy storage system's operation. For example, if charging occurs between 1 AM and 2 AM, with a remaining capacity of 5 kWh at 1 AM, a charging power of 0.1 kWh, a maximum charging power of 0.3 kWh, and a battery degradation coefficient of 0.1,... Then calculate the full charge control coefficient = (5.01-5) / 5.01 = 0.0019. The numbers mentioned above are only for illustrative purposes and are not intended to be the only reference.

[0119] In one embodiment, step S4, which involves obtaining the power loss control coefficient based on the power loss control information, includes:

[0120] S407. Obtain the phase current, phase voltage, end-of-line active power load, and end-of-line reactive power load through the preset line based on the power loss control information.

[0121] S408. Calculate the load influence coefficient based on the active load and reactive load of the end-point energy storage, wherein the calculation formula is:

[0122]

[0123] Where Y(s) represents the load influence coefficient, F1 represents the active load of the end-point energy storage, and F2 represents the reactive load of the end-point energy storage;

[0124] S409. Obtain the resistance value of the input power line in the power grid energy storage device;

[0125] S4010. Calculate the power loss control coefficient based on the phase current, phase voltage, and load influence coefficient, wherein the calculation formula is:

[0126] P(s) = 3 * V * I * Y(s);

[0127] Where P(s) represents the power loss control coefficient in three-phase electricity, V represents the phase voltage, I represents the phase current, and Y(s) represents the load influence coefficient.

[0128] As described in steps S407-S4010 above, since the losses during grid charging solve the problem of actual energy storage capacity, based on this, the phase current, phase voltage, active load of end energy storage, and reactive load of end energy storage are first obtained according to the power loss control information. Then, the load influence coefficient is calculated according to the active load and reactive load of end energy storage. In this case, the grid energy storage is operated on a three-phase basis. Then, the power loss control power coefficient is calculated according to the phase current, phase voltage, and load influence coefficient. After obtaining the power loss control power coefficient, the power loss in the preset charging capacity can be calculated. After removing the power loss, the actual energy storage value can be obtained. Thus, the power and voltage parameters during battery charging can be precisely adjusted according to the actual energy storage value to minimize the performance fluctuations of the damaged battery during energy storage.

[0129] In one embodiment, step S5, which involves inputting the full-charge control coefficient and the power loss control coefficient into the equalization charging model for training to obtain the equalization control value, includes:

[0130] S501. Obtain the corresponding full charge control weight value according to the full charge control coefficient;

[0131] S502. Obtain the corresponding power loss control weight value based on the power loss control power coefficient;

[0132] S503, Deviation coefficient for obtaining control parameters of grid energy storage devices based on grid-type energy storage systems;

[0133] S504. Input the full-charge control coefficient, power loss control coefficient, full-charge control weight value, power loss control weight value, and deviation coefficient into the equalization charging model for training to obtain the equalization control value. The function formula of the equalization charging model is:

[0134] j(h)=[M(C)*d+P(s)*e]*θ;

[0135] Where s(c) represents the balance control value, M(C) represents the full charge control coefficient, d represents the full charge control weight value, P(s) represents the power loss control coefficient, e represents the power loss control weight value, and θ represents the deviation coefficient of the control parameters of the grid energy storage device.

[0136] As described in steps S501-S504 above, the present invention first obtains the corresponding full-charge control weight value based on the full-charge control coefficient, then obtains the corresponding power loss control weight value based on the power loss control coefficient, and then obtains the deviation coefficient of the control parameters of the grid energy storage device based on the grid-type energy storage system. After obtaining the main data required for grid-type energy storage regulation, the full-charge control coefficient, power loss control coefficient, full-charge control weight value, power loss control weight value and deviation coefficient are input into the equalization charging model for training to obtain the equalization control value. After obtaining the equalization control value, multiple batteries with fluctuating performance can be sequentially equalized according to the equalization control value, thereby avoiding the problem that the performance fluctuation of the damaged battery during energy storage will affect the stable operation of the entire energy storage system.

[0137] In one embodiment, step S7 of balancing the battery performance fluctuations according to the balancing control value further includes:

[0138] S701. Based on the equalization control value, the charging gradient is divided for each battery with performance fluctuations to obtain multiple gradient control intervals.

[0139] S702. Set warning thresholds for each of the multiple gradient control intervals;

[0140] S703. The BMS is started according to the balance control value to perform balance adjustment on multiple gradient control intervals in sequence. The BMS is applied in the grid-type energy storage system. The balance adjustment can only be adjusted to the next gradient control interval after the warning threshold is met, until multiple gradient control intervals are all balanced.

[0141] As described in steps S701-S702 above, when this application performs sequential equalization control on multiple performance-fluctuating batteries using equalization control values, the specific steps are as follows: First, each performance-fluctuating battery is divided into charging gradients according to the equalization control values, resulting in multiple gradient control intervals. This multiple control intervals allow for repeated control of the performance-fluctuating batteries, preventing errors from a single control from affecting the entire energy storage system. Simultaneously, warning thresholds are set for each of the multiple gradient control intervals. Then, the BMS is activated based on the equalization control values ​​to sequentially equalize and adjust the multiple gradient control intervals. The equalization adjustment must meet the warning thresholds before adjusting the next gradient control interval, until all multiple gradient control intervals are balanced. This reduces the risk of regulating performance-fluctuating batteries and brings them to a balanced state, thus preventing performance fluctuations caused by damaged batteries during energy storage from affecting the stable operation of the entire energy storage system.

[0142] This application also provides a battery management system for grid-based energy storage, including:

[0143] The first acquisition module 1 is used to acquire the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electric cycles, charging current and charging temperature;

[0144] The first training module 2 is used to input multiple electrical cycle counts, multiple charging currents and multiple charging temperatures of each battery into the battery degradation prediction model for training, so as to obtain multiple battery degradation coefficients.

[0145] The second acquisition module 3 is used to acquire control parameters of grid energy storage devices based on the grid-type energy storage system. The control parameters of the grid energy storage devices include charging rate control information and power loss control information. The grid-type energy storage system is a system used to control the battery energy storage balance in the grid energy storage devices.

[0146] The third acquisition module 4 is used to acquire the full charge control coefficient based on the charging rate control information and to acquire the power loss control coefficient based on the power loss control information.

[0147] The second training module 5 is used to input the full charge control coefficient and the power loss control coefficient into the equalization charging model for training, so as to obtain the equalization control value.

[0148] The first judgment module 6 is used to sequentially judge whether multiple battery degradation coefficients are within a preset range;

[0149] If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance.

[0150] The first equalization control module 7 is used to equalize the battery with performance fluctuations according to the equalization control value.

[0151] In one embodiment, the first training module includes:

[0152] The first acquisition unit is used to acquire multiple electric cycle number weight values ​​of the corresponding battery based on the multiple electric cycle number;

[0153] The second acquisition unit is used to acquire multiple charging current weight values ​​of the corresponding battery based on the multiple charging currents.

[0154] The third acquisition unit is used to acquire multiple charging temperature weight values ​​of the corresponding battery based on the multiple charging temperatures.

[0155] The first calculation unit is used to sequentially input multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights of each battery into the battery degradation prediction model for training, thereby obtaining multiple battery degradation coefficients. The function formula of the battery degradation prediction model is as follows:

[0156]

[0157] Where β(o...p) represents the battery degradation coefficient corresponding to the 0th to pth batteries, c(s)i represents the number of electrical cycles of the i-th battery, ai represents the weight value of the number of electrical cycles of the i-th battery, d(l)i represents the charging current of the i-th battery, bi represents the weight value of the charging current of the i-th battery, w(d)i represents the charging temperature of the i-th battery, and ci represents the weight value of the charging temperature of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0th to pth batteries, o = 1, 2, 3...p.

[0158] In one embodiment, the third acquisition module includes:

[0159] The fourth acquisition unit is used to acquire the energy storage charging power and the upper limit charging power of the grid energy storage device within a preset time according to the charging rate control information.

[0160] The fifth acquisition unit is used to acquire the remaining power value of each battery before charging;

[0161] The sixth acquisition unit is used to acquire the start and end times of charging for each battery.

[0162] The second calculation unit is used to calculate the final energy storage capacity based on the remaining energy value, energy storage charging power, upper limit charging power, battery degradation coefficient, start charging time, and end charging time, wherein the calculation formula is:

[0163]

[0164] Where E2 represents the final energy storage capacity, E1 represents the remaining energy capacity, and c(n) represents the energy storage charging power. max t1 represents the upper limit of charging power, β represents the battery degradation coefficient, t1 represents the start time of charging, and t2 represents the end time of charging.

[0165] The third calculation unit is used to calculate the full-charge control coefficient based on the final energy storage capacity and the remaining energy capacity. The calculation formula is as follows:

[0166]

[0167] Where M(C) represents the full charge control coefficient.

[0168] In one embodiment, the third acquisition module includes:

[0169] The fourth calculation unit is used to calculate the load influence coefficient based on the active load and reactive load of the end-point energy storage, wherein the calculation formula is:

[0170]

[0171] Where Y(s) represents the load influence coefficient, F1 represents the active load of the end-point energy storage, and F2 represents the reactive load of the end-point energy storage;

[0172] The seventh acquisition unit is used to acquire the resistance value of the input power line in the power grid energy storage device;

[0173] The fifth calculation unit is used to calculate the power loss control coefficient based on the phase current, phase voltage, and load influence coefficient, wherein the calculation formula is:

[0174] P(s) = 3 * V * I * Y(s);

[0175] Where P(s) represents the power loss control coefficient in three-phase electricity, V represents the phase voltage, I represents the phase current, and Y(s) represents the load influence coefficient.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0177] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0178] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A battery management method for grid-connected energy storage, applied to grid energy storage devices, characterized in that, The method includes: Obtain the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electrical cycles, charging current and charging temperature; Multiple battery degradation prediction models are trained by sequentially inputting multiple battery degradation coefficients, multiple electric cycle counts, multiple charging currents, and multiple charging temperatures for each battery. The control parameters of the grid energy storage device are obtained based on the grid-type energy storage system. The control parameters of the grid energy storage device include charging rate control information and power loss control information. The grid-type energy storage system is a system used to control the battery energy storage balance in the grid energy storage device. The full charge control coefficient is obtained based on the charging rate control information, and the power loss control coefficient is obtained based on the power loss control information. The full charge control coefficient and the power loss control coefficient are input into the equalization charging model for training to obtain the equalization control value. Sequentially determine whether the degradation coefficients of multiple batteries are within a preset range; If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance. The battery with fluctuating performance is balanced according to the aforementioned balance control value; The step of obtaining the power loss control coefficient based on the power loss control information includes: The phase current, phase voltage, active load of end energy storage, and reactive load of end energy storage are obtained based on the power loss control information. The load influence coefficient is calculated based on the active and reactive loads of the end-point energy storage, wherein the calculation formula is: ； in, Indicates the load impact factor. Indicates the active load of end-point energy storage. Indicates reactive load at the end of the energy storage system; The power loss control coefficient is calculated based on the phase current, phase voltage, and load influence coefficient, wherein the calculation formula is: ； in, This represents the power factor for controlling power loss in three-phase electricity. I represents phase voltage, and I represents phase current. This represents the load impact coefficient.

2. The battery management method for grid-type energy storage according to claim 1, characterized in that, The step of sequentially inputting multiple electrical cycle counts, multiple charging currents, and multiple charging temperatures of each battery into the battery degradation prediction model for training to obtain multiple battery degradation coefficients includes: Based on the multiple electric cycle counts, obtain the corresponding battery's multiple electric cycle count weight values; Multiple charging current weight values ​​for the corresponding battery are obtained based on the multiple charging currents. Based on the multiple charging temperatures, obtain multiple charging temperature weight values ​​for the corresponding battery; For each battery, multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights are sequentially input into the battery degradation prediction model for training, resulting in multiple battery degradation coefficients. The function formula for the battery degradation prediction model is as follows: + + ；...； + + ； in, This represents the battery degradation coefficient corresponding to the 0th to pth batteries. This represents the number of electrical cycles for the i-th battery. This represents the weight value of the number of electrical cycles for the i-th battery. This represents the charging current of the i-th battery. i represents the charging current weight value of the i-th battery. This represents the charging temperature of the i-th battery. Let represent the charging temperature weight value of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0-th to p-th batteries, o = 1, 2, 3...p.

3. The battery management method for grid-type energy storage according to claim 1, characterized in that, The step of obtaining the full-charge control coefficient based on the charging rate control information includes: The energy storage charging power and upper limit charging power of the grid energy storage device within a preset time are obtained based on the charging rate control information. Get the remaining power value of each battery before charging; Obtain the start and end times of charging for each battery; The final energy storage capacity is calculated based on the remaining energy value, energy storage charging power, maximum charging power, battery degradation coefficient, start charging time, and end charging time. The calculation formula is as follows: ； in, Indicates the final energy storage capacity. This indicates the remaining battery level. Indicates the energy storage charging power. Indicates the maximum charging power. Indicates the battery degradation coefficient. Indicates the start time of charging. Indicates the end of charging; The full-charge control coefficient is calculated based on the final energy storage capacity and the remaining energy capacity. The calculation formula is as follows: ； in, This represents the full-fill control coefficient.

4. The battery management method for grid-type energy storage according to claim 1, characterized in that, The step of inputting the full-charge control coefficient and the power loss control coefficient into the equalization charging model for training to obtain the equalization control value includes: The corresponding full charge control weight value is obtained based on the full charge control coefficient. The corresponding power loss control weight value is obtained based on the power loss control power coefficient. Based on the deviation coefficient of the control parameters of grid energy storage devices obtained from the grid-type energy storage system; The full-charge control coefficient, power loss control coefficient, full-charge control weight value, power loss control weight value, and deviation coefficient are input into the equalization charging model for training to obtain the equalization control value. The function formula for the equalization charging model is: ； in, Indicates the balance control value. d represents the full charge control coefficient, and d represents the full charge control weight value. This represents the power loss control coefficient, and e represents the power loss control weight value. This represents the deviation coefficient of the control parameters of the grid energy storage device.

5. The battery management method for grid-type energy storage according to claim 1, characterized in that, The step of balancing the battery performance fluctuations based on the balancing control value further includes: Based on the equalization control value, the charging gradient of each battery with performance fluctuation is divided to obtain multiple gradient control intervals; Warning thresholds are set for each of the multiple gradient control intervals; The BMS is activated based on the equilibrium control value to sequentially perform equilibrium adjustment on multiple gradient control intervals. The BMS is applied in a grid-type energy storage system. The equilibrium adjustment can only be adjusted to the next gradient control interval after the warning threshold is met, until all multiple gradient control intervals have reached equilibrium.

6. A battery management system for grid-type energy storage, characterized in that, include: The first acquisition module is used to acquire the historical operating parameters of each battery in the current grid energy storage device, wherein the historical operating parameters include the number of electric cycles, charging current and charging temperature; The first training module is used to input multiple electrical cycle counts, multiple charging currents, and multiple charging temperatures of each battery into the battery degradation prediction model for training, and to obtain multiple battery degradation coefficients. The second acquisition module is used to acquire control parameters of grid energy storage devices based on the grid-type energy storage system. The control parameters of the grid energy storage devices include charging rate control information and power loss control information. The grid-type energy storage system is a system used to control the battery energy storage balance in the grid energy storage devices. The third acquisition module is used to acquire the full charge control coefficient based on the charging rate control information and to acquire the power loss control coefficient based on the power loss control information. The second training module is used to input the full charge control coefficient and the power loss control coefficient into the equalization charging model for training, so as to obtain the equalization control value. The first judgment module is used to sequentially judge whether multiple battery degradation coefficients are within a preset range; If the battery degradation coefficient is not within the preset range, the battery corresponding to the battery degradation coefficient is determined to be a battery with fluctuating performance. The first equalization control module is used to equalize the battery with performance fluctuations according to the equalization control value. The third acquisition module includes: The fourth calculation unit is used to calculate the load impact coefficient based on the active and reactive loads of the end-point energy storage. The calculation formula is as follows: ； in, Indicates the load impact factor. Indicates the active load of end-point energy storage. Indicates reactive load at the end of the energy storage system; The seventh acquisition unit is used to acquire the resistance value of the input power line in the power grid energy storage device; The fifth calculation unit is used to calculate the power loss control coefficient based on the phase current, phase voltage, and load influence coefficient. The calculation formula is as follows: ； in, This represents the power factor for controlling power loss in three-phase electricity. I represents phase voltage, and I represents phase current. This represents the load impact coefficient.

7. A battery management system for grid-type energy storage according to claim 6, characterized in that, The first training module includes: The first acquisition unit is used to acquire multiple electric cycle number weight values ​​of the corresponding battery based on the multiple electric cycle number; The second acquisition unit is used to acquire multiple charging current weight values ​​of the corresponding battery based on the multiple charging currents. The third acquisition unit is used to acquire multiple charging temperature weight values ​​of the corresponding battery based on the multiple charging temperatures. The first calculation unit is used to sequentially input multiple electrical cycle counts, multiple charging currents, multiple charging temperatures, multiple electrical cycle count weights, multiple charging current weights, and multiple charging temperature weights of each battery into the battery degradation prediction model for training, thereby obtaining multiple battery degradation coefficients. The function formula of the battery degradation prediction model is as follows: + + ；...； + + ； in, This represents the battery degradation coefficient corresponding to the 0th to pth batteries. This represents the number of electrical cycles for the i-th battery. This represents the weight value of the number of electrical cycles for the i-th battery. This represents the charging current of the i-th battery. i represents the charging current weight value of the i-th battery. This represents the charging temperature of the i-th battery. Let represent the charging temperature weight value of the i-th battery, where i = 1, 2, 3...n, and o and p represent the counting numbers of the 0-th to p-th batteries, o = 1, 2, 3...p.

8. A battery management system for grid-type energy storage according to claim 6, characterized in that, The third acquisition module includes: The fourth acquisition unit is used to acquire the energy storage charging power and the upper limit charging power of the grid energy storage device within a preset time according to the charging rate control information. The fifth acquisition unit is used to acquire the remaining power value of each battery before charging; The sixth acquisition unit is used to acquire the start and end times of charging for each battery. The second calculation unit is used to calculate the final energy storage capacity based on the remaining energy value, energy storage charging power, upper limit charging power, battery degradation coefficient, start charging time, and end charging time, wherein the calculation formula is: ； in, Indicates the final energy storage capacity. This indicates the remaining battery level. Indicates the energy storage charging power. Indicates the maximum charging power. Indicates the battery degradation coefficient. Indicates the start time of charging. Indicates the end of charging; The third calculation unit is used to calculate the full-charge control coefficient based on the final energy storage capacity and the remaining energy capacity. The calculation formula is as follows: ； in, This represents the full-fill control coefficient.

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