Battery Module Balancing Control Method, Device, System, Equipment and Medium

Abstract

The present application relates to a battery module balancing control method, device, system, equipment and medium. On the one hand, the provided battery module balancing control method is applied to a balancing control device in a port area energy storage system. By obtaining the status data, power generation prediction data and load prediction data corresponding to each battery module in the battery pack; determining a balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtaining a target balancing current according to the status data and the balancing control weight, and sending it to a current controller for the current controller to control the balancing current in each of the battery modules based on the target balancing current. Using this method can improve the efficiency of battery module balancing control.

Description

Technical Field

[0001] This application relates to the technical field of balancing control, and particularly to a battery module balancing control method, device, system, equipment and medium. Background Art

[0002] With the green and intelligent development of port areas, the port energy storage system based on ship power lithium batteries plays an important role in the port integrated energy system, and the secondary utilization of retired ship power lithium batteries is of great significance. In order to achieve the overall retirement of the lithium battery system, it is necessary to perform balancing control on each battery module in the port energy storage system.

[0003] In the existing technology, the balancing control methods for battery modules mostly only consider the balancing control of a single target, such as quickly achieving the convergence of the health state data of each battery module or battery cell in the system.

[0004] However, in the application scenario of the port energy storage system, the balancing control efficiency of the battery module balancing control method in the existing technology is low. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a battery module balancing control method, device, system, equipment and medium that can improve the balancing control efficiency of the port energy storage system.

[0006] In a first aspect, this application provides a battery module balancing control method, which is applied to a balancing control device in a port energy storage system. The method includes:

[0007] Obtain the state data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack;

[0008] Determine the balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data;

[0009] Obtain the target balancing current according to the state data and the balancing control weight, and send it to the current controller for the current controller to control the balancing current in each battery module based on the target balancing current.

[0010] In one embodiment, the balancing control weight includes a depth of discharge weight and a health state weight. Obtaining the target balancing current according to the state data and the balancing control weight includes:

[0011] Determine the objective function according to the state data, the depth of discharge weight, and the health state weight;

[0012] Optimize and solve the objective function to obtain the target balancing current.

[0013] In one embodiment, optimizing and solving the objective function to obtain the target balanced current includes:

[0014] Performing particle swarm optimization on the objective function to obtain the optimal discharge depth corresponding to each battery module within the target time interval;

[0015] Obtaining the target balanced current according to each optimal discharge depth.

[0016] In one embodiment, obtaining the target balanced current according to the optimal discharge depth vector includes:

[0017] Obtaining battery capacity data and collected current data;

[0018] Obtaining a current proportionality coefficient according to the optimal discharge depth corresponding to each battery module and the battery capacity data;

[0019] Performing multiplication processing on the current proportionality coefficient and the collected current data to obtain the target balanced current.

[0020] In one embodiment, the target time interval is a charging cycle, and the collected current data is a charging current; the process of obtaining the current proportionality coefficient according to the optimal discharge depth corresponding to each battery module and the battery capacity data includes:

[0021] Taking the maximum value among the optimal discharge depths as the target discharge depth;

[0022] Taking the maximum value among the battery capacity data as the target battery capacity;

[0023] For each battery module, obtaining the current proportionality coefficient according to the optimal discharge depth, the battery capacity data, the target discharge depth, and the target battery capacity.

[0024] In one embodiment, the target time interval is a discharge cycle, and the collected current data is a discharge current; the process of obtaining the current proportionality coefficient according to the optimal discharge depth corresponding to each battery module and the battery capacity data includes:

[0025] Taking the minimum value among the optimal discharge depths as the target discharge depth;

[0026] Taking the minimum value among the battery capacity data as the target battery capacity;

[0027] For each battery module, obtaining the current proportionality coefficient according to the optimal discharge depth, the battery capacity data, the target discharge depth, and the target battery capacity.

[0028] In a second aspect, the present application further provides a battery module balancing control device, which is applied to an equalizing control device in a port area energy storage system. The device includes:

[0029] A data acquisition module, configured to acquire status data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack;

[0030] A weight determination module, configured to determine an equalization control weight according to the proportional relationship between the power generation prediction data and the load prediction data;

[0031] A current control module, configured to obtain a target equalization current according to the status data and the equalization control weight, and send it to a current controller for the current controller to control the equalization current in each battery module based on the target equalization current.

[0032] Thirdly, the present application further provides a port area energy storage system, which includes:

[0033] A battery pack, a new energy system output prediction device, a port area load prediction device, and

[0034] An equalization control device, configured to acquire status data corresponding to each battery module in the battery pack, acquire power generation prediction data from the new energy system output prediction device, and acquire load prediction data from the port area load prediction device; determine an equalization control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtain a target equalization current according to the status data and the equalization control weight, and send it to the current controller in the battery pack for the current controller to control the equalization current in each battery module based on the target equalization current.

[0035] Fourthly, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method in the first aspect are implemented.

[0036] Fifthly, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in the first aspect are implemented.

[0037] The above battery module equalization control method, device, system, equipment and medium. On the one hand, the provided battery module equalization control method is applied to the equalization control equipment in the port area energy storage system. By obtaining the status data, power generation prediction data and load prediction data corresponding to each battery module in the battery pack; determining the equalization control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtaining the target equalization current according to the status data and the equalization control weight, and sending it to the current controller for the current controller to control the equalization current in each battery module based on the target equalization current. In this way, in the process of equalization control of the battery module, fully considering the special application scenario requirements of the port area, taking into account the status data of the battery module, the power generation prediction data of the new energy power generation system and the load prediction data, it can effectively improve the consumption level of the new energy output in the port area energy storage system during the equalization control process on the basis of ensuring that each battery module can effectively achieve synchronous retirement, thereby improving the equalization control efficiency of the battery module equalization control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a schematic structural diagram of a battery module equalization control system in an embodiment;

[0040] Figure 2 It is a schematic flowchart of a battery module equalization control method in an embodiment;

[0041] Figure 3 It is a schematic flowchart of a battery module equalization control method in another embodiment;

[0042] Figure 4 It is a schematic diagram of the equalization control effect of each battery module in an embodiment;

[0043] Figure 5 It is a block diagram of the structure of a battery module equalization control device in an embodiment;

[0044] Figure 6 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions, and advantages of this application more clear and understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not used to limit this application.

[0046] The battery module balancing control method provided by the embodiments of this application can be applied to a port area energy storage system as Figure 1 shown. The port area energy storage system provided by the embodiments of this application includes: a battery pack 102, a balancing control device 104, a new energy system output prediction device 106, and a port area load prediction device 108.

[0047] Among them, the battery pack 102 includes a plurality of battery cells, and each battery cell includes a state estimation module 1022, a battery module 1024, a current control module 1026, and a balancing resistor 1028. The battery module 1024 is connected in parallel with the balancing resistor 1028, in parallel with the current control module 1026, and is connected to the state estimation module 1022, and the battery modules 1024 are connected in series with each other. The state estimation module 1022 is used to estimate the state data of the corresponding battery module 1024 and upload the state data to the balancing control device 104.

[0048] Among them, the new energy system output prediction device 106 is connected to the balancing control device 104, and is used to predict the power generation of the port area new energy system within a target time interval, obtain power generation prediction data, and send the power generation prediction data to the balancing control device 104.

[0049] Among them, the port area load prediction device 108 is connected to the balancing control device 104, and is used to predict the overall power consumption load of the port area within a target time interval, obtain load prediction data, and send the load prediction data to the balancing control device 104.

[0050] Among them, the balancing control device 104 is used to obtain the state data corresponding to each battery module in the battery pack, obtain the power generation prediction data from the new energy system output prediction device, and obtain the load prediction data from the port area load prediction device; determine the balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtain the target balancing current according to the balancing control weight, and send it to the current control module 1026 for the current control module 1026 to control the balancing current in each battery module 1024 based on the target balancing current.

[0051] Among them, the balance control device 104 communicates with the new energy system output prediction device 106 and the port area load prediction device 108 through a network. The balance control device 104 can be a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0052] In an exemplary embodiment, as Figure 2 shown, a battery module balance control method is provided. The method is applied to Figure 1 the balance control device 104 in it. Taking the balance control device 104 as a server as an example, the method includes the following steps 202 to 206. Among them:

[0053] Step 202, obtain the state data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack.

[0054] Among them, the state data refers to the state of health (SOH) of the battery module. The SOH can reflect the degree of degradation of the battery module's performance due to factors such as the number of charge and discharge cycles, charge and discharge depth, environmental temperature, thermal expansion and contraction, external mechanical vibration, or overcharge, over-discharge, fast charge and discharge, etc. The state estimation module for collecting the SOH can be a battery management system (BMS) or a device connected to the BMS.

[0055] Exemplarily, the state estimation module can estimate the SOH by performing charge and discharge tests on the battery module, collecting the actual operating parameters of the battery module and comparing them with the initial parameters; or, using physical and chemical models such as an equivalent circuit model and a thermal model, based on the operating parameters such as the charge and discharge current, voltage, and temperature of the battery module, fitting the capacity decay or internal resistance change of the battery module to obtain the SOH; or, inputting the operating data of the battery module into a pre-trained machine learning model such as a neural network or a random forest to generate the SOH.

[0056] Among them, the power generation prediction data refers to the prediction data of the power generation of the port area's new energy system within the target time interval by the new energy system output prediction device 106. Since the new energy sources such as photovoltaic, wind power, and hydropower in the port area are intermittent and volatile, timely absorption of the port area's new energy electricity is also an important task for the port area's energy storage system. The unabsorbed electricity may be wasted, or directly connected to the power grid may cause power grid fluctuations or overloads. The new energy system output prediction device 106 can obtain monitoring data by monitoring the meteorological conditions, power generation equipment performance, and historical power generation in the port area; the monitoring data is input into the radiation model or the power output model to obtain power generation prediction data.

[0057] The load forecast data refers to the forecast data of the overall power load of the port area in the target time interval by the port area load forecasting device 108. The overall power load of the port area may include production and operation loads, such as the power consumption of terminal operation equipment such as cranes, conveyor belts, shore power equipment, and cargo loading and unloading and transportation equipment such as automated guided vehicles, forklifts, and rail cranes; auxiliary facility loads, such as the power consumption of office and living facilities, port area management systems, monitoring equipment, and communication network equipment; environmental regulation loads, such as the power consumption of sewage treatment, waste gas treatment equipment, photovoltaic inverters, and wind power control systems. The port area load forecasting device 108 can obtain load forecast data by using the overall power load of the port area in the historical time interval and a time series model, such as an ARIMA model (Autoregressive Integrated Moving Average model) or a SARIMA model (Seasonal Autoregressive Integrated Moving Average).

[0058] Step 204, determining the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data.

[0059] Among them, the power generation forecast data can be expressed as , the load forecast data can be expressed as The process of determining the balance control weight specifically includes: according to the proportional relationship k i , determine the current mode level L; based on the current mode level, determine the balancing control weight corresponding to the level.

[0060] For example, the proportional relationship k i The matching rules with the current mode level L include: , then L=I level; if , then L = II level; if , then L=III level; if , then L = IV level; if , then L = V level. According to the mapping relationship between the current mode level L and the equalization control weight, determine the equalization control weight corresponding to the current mode level L.

[0061] Step 206, obtain the target equalization current according to the equalization control weight, and send it to the current controller for the current controller to control the equalization current in each battery module based on the target equalization current.

[0062] The above battery module equalization control method is applied to the equalization control device in the port area energy storage system. By obtaining the state data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack; determining the equalization control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtaining the target equalization current according to the state data and the equalization control weight, and sending it to the current controller for the current controller to control the equalization current in each battery module based on the target equalization current. In this way, during the process of equalization control of the battery module, the special application scenario requirements of the port area are fully considered, and the state data of the battery module, the power generation prediction data of the new energy power generation system, and the load prediction data are comprehensively considered, which can effectively improve the consumption level of the port area energy storage system for the new energy output in the port area during the equalization control process on the basis of ensuring that each battery module can effectively achieve simultaneous retirement, thereby improving the equalization control efficiency of the battery module equalization control method.

[0063] In an exemplary embodiment, based on Figure 2 the embodiment shown, the equalization control weight in the provided method includes the depth of discharge weight and the state of health weight. The process of obtaining the target equalization current according to the state data and the equalization control weight in the provided method includes: determining the objective function according to the state data, the depth of discharge weight, and the state of health weight; optimizing and solving the objective function to obtain the target equalization current.

[0064] Among them, the objective function J can be expressed as:

[0065] ,

[0066] Among them, T pre is the number of sampling moments in the target time interval; N is the total number of battery modules included in the battery pack; D n (t) is the depth of discharge value of the nth battery module at the tth sampling moment in the target time interval; SOH n (t) is the state data of the nth battery module at the tth sampling moment in the target time interval; is the depth of discharge weight; state of health weight.

[0067] In a possible implementation, in the provided method, the process of optimizing and solving the objective function to obtain the target balanced current may further include: performing particle swarm optimization on the objective function to obtain the optimal discharge depth corresponding to each battery module within the target time interval; and obtaining the target balanced current according to each optimal discharge depth.

[0068] Among them, based on the objective function J, the optimization vector within the target time interval can be obtained. , the optimization vector can be expressed as:

[0069] ,

[0070] Among them, D n (t), (n = 1, 2, …, N; t = 1, 2, …, Tpre) is the discharge depth of the nth battery module at the tth sampling moment.

[0071] Among them, the particle swarm optimization process refers to using the Particle Swarm Optimization (PSO) algorithm to optimize the objective function J. The process of particle swarm optimization may specifically include: obtaining the initial particle swarm; calculating the objective function value of each particle in the initial particle swarm, and using the objective function value to update the personal best position and the global best position corresponding to each particle; updating the speed and position corresponding to each particle, and repeating the process of obtaining the personal best position and the global best position until the preset termination condition is reached, outputting the personal best position and the global best position, and taking the personal best position as the optimal discharge depth of the corresponding battery module.

[0072] Among them, the optimal discharge depth vector can be expressed as:

[0073] ,

[0074] Among them, (n = 1, 2, …, N; t = 1, 2, …, T pre ) is the optimal discharge depth of the nth battery module at the tth sampling moment.

[0075] In this embodiment, a target function is generated based on state data, depth-of-discharge weights, and state-of-health weights, and the particle swarm optimization algorithm is used to optimize the target function. This can avoid falling into local optima during the optimization process, update the particle positions only based on the target function values, have a lower computational complexity, and improve the real-time performance of the optimization process. The particle update processes are independent of each other and can be calculated in parallel, which can accelerate the optimization process. The optimization method in this embodiment can achieve the health balance of battery modules on the basis of meeting the load demand and new energy power generation consumption requirements in the new energy port area, and has significant advantages in terms of optimization efficiency, flexibility, adaptability, and ease of implementation.

[0076] In an exemplary embodiment, the process of obtaining the target balancing current according to the optimal depth-of-discharge vector in the provided method includes: acquiring battery capacity data and collected current data; obtaining current proportionality coefficients according to the optimal depth-of-discharge corresponding to each battery module and the battery capacity data; multiplying the current proportionality coefficients by the collected current data to obtain the target balancing current.

[0077] Among them, the purpose of the target balancing current is to make the state of health and remaining capacity of all battery modules gradually tend to be consistent.

[0078] In a possible implementation manner, the target time interval is the charging cycle, the collected current data is the charging current. In the provided method, the process of obtaining the current proportionality coefficients according to the optimal depth-of-discharge corresponding to each battery module and the battery capacity data includes:

[0079] Taking the maximum value among the optimal depths-of-discharge as the target depth-of-discharge;

[0080] Taking the maximum value among the battery capacity data as the target battery capacity;

[0081] For each battery module, obtaining the current proportionality coefficient according to the optimal depth-of-discharge, the battery capacity data, the target depth-of-discharge, and the target battery capacity.

[0082] Among them, the current proportionality coefficient corresponding to the charging cycle can be expressed as:

[0083] ,

[0084] The target balancing current can be expressed as:

[0085] ,

[0086] Among them, I n (t)(n = 1, 2, …, N; t = 1, 2, …, T pre ) is the balancing current of the nth battery module at the tth sampling moment; I C(t) is the charging current of the battery pack at the t-th sampling moment; Q n is the remaining available capacity of the n-th battery module; Q best is the maximum value of the remaining available capacities of all battery modules in the battery pack, that is, the target battery capacity; is the optimal discharge depth of the n-th battery module at the t-th sampling moment; is the maximum value of the optimal discharge depths of all battery modules in the battery pack at the t-th sampling moment, that is, the target discharge depth.

[0087] In a possible implementation manner, the target time interval is the discharge cycle, the collected current data is the discharge current. In the provided method, the process of obtaining the current proportionality coefficient according to the optimal discharge depth and battery capacity data corresponding to each battery module includes:

[0088] Taking the minimum value among the optimal discharge depths as the target discharge depth;

[0089] Taking the minimum value among the battery capacity data as the target battery capacity;

[0090] For each battery module, obtaining the current proportionality coefficient according to the optimal discharge depth, battery capacity data, target discharge depth, and target battery capacity.

[0091] Among them, the current proportionality coefficient corresponding to the discharge cycle can be expressed as:

[0092] ,

[0093] The target equalization current can be expressed as:

[0094] ,

[0095] Among them, I n (t) (n = 1, 2, …, N; t = 1, 2, …, T pre ) is the equalization current of the n-th battery module at the t-th sampling moment; I D (t) is the discharge current of the battery pack at the t-th sampling moment; Q n is the remaining available capacity of the n-th battery module; Q worst is the minimum value of the remaining available capacities of all battery modules in the battery pack, that is, the target battery capacity; is the optimal discharge depth of the n-th battery module at the t-th sampling moment; is the minimum value of the optimal discharge depths of all battery modules in the battery pack at the t-th sampling moment, that is, the target discharge depth.

[0096] In this embodiment, using the maximum value as the target discharge depth and the target battery capacity for the charging cycle can prevent some battery modules from reaching the charging upper limit prematurely due to small capacity or low discharge depth during the charging process, ensuring the maximization of charging efficiency; using the minimum value as the target discharge depth and the target battery capacity for the discharging cycle can prevent some battery modules from depleting energy prematurely due to small capacity or excessive discharge depth, ensuring the power supply stability of the system. This dynamic adjustment strategy enables the algorithm to adapt to different working states, thereby ensuring the stability and reliability of the overall system performance, reducing the inconsistency between battery modules, and effectively achieving the balanced control of each battery module.

[0097] In an exemplary embodiment, as Figure 3 shown, a method for balancing control of battery modules is provided. The method is applied to Figure 1 the balancing control device 104. Taking the balancing control device 104 as a server as an example, the method includes the following steps 301 to 306. Among them:

[0098] Step 301, obtain the state data, power generation prediction data, load prediction data, battery capacity data, and collected current data corresponding to each battery module in the battery pack.

[0099] Step 302, determine the balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data.

[0100] Step 303, determine the objective function according to the state data, discharge depth weight, and health state weight.

[0101] Step 304, perform particle swarm optimization on the objective function to obtain the optimal discharge depth corresponding to each battery module within the target time interval.

[0102] Step 305, obtain the current proportionality coefficient according to the optimal discharge depth and battery capacity data corresponding to each battery module.

[0103] Step 306, perform multiplication processing on the current proportionality coefficient and the collected current data to obtain the target balancing current and send it to the current controller for the current controller to control the balancing current in each battery module based on the target balancing current.

[0104] In a possible implementation, the battery pack in the port area energy storage system includes a total of 10 battery modules, 10 balancing resistors, 10 current control modules, 10 state estimation modules, as well as 1 new energy system output prediction device, 1 port area load prediction device, and 1 balancing control device. The 10 battery modules are connected in series to form a battery pack. At the same time, each battery module is connected in parallel with a balancing resistor and a current control module. The state of health (SOH) of the state data uploaded by the 10 state estimation modules to the balancing control device is specifically: SOH1 = 0.90, SOH2 = 0.82, SOH3 = 0.88, SOH4 = 0.91, SOH5 = 0.75, SOH6 = 0.79, SOH7 = 0.72, SOH8 = 0.73, SOH9 = 0.77, SOH10 = 0.86.

[0105] The length of the target time interval is 60 min. The power generation prediction data Q gen = 4800 kWh, and the load prediction data Q load = 8600 kWh; combining the matching rules in the foregoing embodiments, the proportionality coefficients k i are respectively taken as k1 = 2.0; k2 = 1.3; k3 = 0.9; k4 = 0.5. Therefore, it is determined that the current mode level L = level IV.

[0106] The mapping relationship between the current mode level L and the balancing control weight is executed according to the following table:

[0107] Table 1 Mapping relationship between the current mode level L and the balancing control weight

[0108]

[0109] In the case of L = level IV, the depth of discharge weight and the state of health weight .

[0110] Subsequently, the objective function J of the target time interval is constructed as:

[0111] .

[0112] Among them, the sampling period t is 1 min. Further, the optimization vector within the target time interval is:

[0113] .

[0114] Among them, is the optimization vector; D n (t) (n = 1, 2,..., 10; t = 1, 2,..., 60) is the depth of discharge of the nth battery module at the tth sampling moment.

[0115] The particle swarm optimization algorithm is used to optimize the objective function J to obtain the optimal discharge depth vector of the battery module within the future window time, specifically as follows:

[0116]

[0117] 。

[0118] If the target time interval is the charging cycle and the collected current data is the charging current, the current proportionality coefficient corresponding to the charging cycle can be expressed as:

[0119] ,

[0120] The target balancing current can be expressed as:

[0121] ,

[0122] where, I n (t)(n = 1, 2, …, N; t = 1, 2, …, T pre ) is the balancing current of the nth battery module at the tth sampling moment; I C (t) is the charging current of the battery pack at the tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q best is the maximum value of the remaining available capacity of all battery modules in the battery pack, that is, the target battery capacity; is the optimal discharge depth of the nth battery module at the tth sampling moment; is the maximum value of the optimal discharge depth of all battery modules in the battery pack at the tth sampling moment, that is, the target discharge depth.

[0123] If the target time interval is the discharging cycle and the collected current data is the discharging current, the current proportionality coefficient corresponding to the discharging cycle can be expressed as:

[0124] ,

[0125] The target balancing current can be expressed as:

[0126] ,

[0127] where, I n (t)(n = 1, 2, …, N; t = 1, 2, …, T pre ) is the balancing current of the nth battery module at the tth sampling moment; I D (t) is the discharging current of the battery pack at the tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q worstis the minimum remaining available capacity of all battery modules in the battery pack, i.e., the target battery capacity; is the optimal discharge depth of the nth battery module at the tth sampling moment; is the minimum value of the optimal discharge depths of all battery modules in the battery pack at the tth sampling moment, i.e., the target discharge depth.

[0128] In this embodiment, since the target time interval is the discharge cycle, the balancing current is calculated based on the above corresponding formula, and the current control module controls the current of the balancing resistors connected in parallel with each battery module to be equal to the calculated balancing currents, thereby completing the balancing control within the current cycle. Subsequently, the next balancing cycle is started, and the process of obtaining the state data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack is repeated until the target balancing current is obtained and sent to the current controller.

[0129] Based on the data of this embodiment, the balancing control effect of each battery module is as Figure 4 shown, and the SOH value represents the state data corresponding to each battery module.

[0130] It should be understood that although the steps in the flowcharts involved in the above embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0131] Based on the same inventive concept, the embodiments of the present application also provide a battery module balancing control device for implementing the battery module balancing control method involved above. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the battery module balancing control device provided below can refer to the limitations on the battery module balancing control method in the above text, and will not be repeated here.

[0132] In an exemplary embodiment, as Figure 5 shown, a battery module balancing control device is provided, which is applied to the balancing control equipment in the port area energy storage system. The device includes: a data acquisition module 502, a weight determination module 504, and a current control module 506, where:

[0133] A data acquisition module 502, configured to acquire status data, power generation prediction data, and load prediction data corresponding to each battery module in the battery pack;

[0134] A weight determination module 504, configured to determine an equalization control weight according to the proportional relationship between the power generation prediction data and the load prediction data;

[0135] A current control module 506, configured to obtain a target equalization current according to the status data and the equalization control weight, and send it to a current controller for the current controller to control the equalization current in each battery module based on the target equalization current.

[0136] In one embodiment, the equalization control weight includes a depth of discharge weight and a state of health weight. The current control module 506 is further configured to determine an objective function according to the status data, the depth of discharge weight, and the state of health weight; optimize and solve the objective function to obtain a target equalization current.

[0137] In one embodiment, the current control module 506 is further configured to perform particle swarm optimization processing on the objective function to obtain the optimal depth of discharge corresponding to each battery module within a target time interval; obtain a target equalization current according to each optimal depth of discharge.

[0138] In one embodiment, the current control module 506 is further configured to acquire battery capacity data and collected current data; obtain a current proportionality coefficient according to the optimal depth of discharge and the battery capacity data corresponding to each battery module; perform multiplication processing on the current proportionality coefficient and the collected current data to obtain a target equalization current.

[0139] In one embodiment, the target time interval is a charging cycle, and the collected current data is a charging current; the current control module 506 is further configured to use the maximum value among each optimal depth of discharge as the target depth of discharge; use the maximum value among each battery capacity data as the target battery capacity; for each battery module, obtain a current proportionality coefficient according to the optimal depth of discharge, the battery capacity data, the target depth of discharge, and the target battery capacity.

[0140] In one embodiment, the target time interval is a discharging cycle, and the collected current data is a discharging current; the current control module 506 is further configured to use the minimum value among each optimal depth of discharge as the target depth of discharge; use the minimum value among each battery capacity data as the target battery capacity; for each battery module, obtain a current proportionality coefficient according to the optimal depth of discharge, the battery capacity data, the target depth of discharge, and the target battery capacity.

[0141] Each module in the above battery module balancing control device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0142] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the status data, power generation prediction data, and load prediction data corresponding to each battery module. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a battery module balancing control method.

[0143] Those skilled in the art can understand that Figure 6 the structure shown in

[0144] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0145] In an embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in each of the above method embodiments.

[0145] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in each of the above method embodiments.

[0146] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps in each of the above method embodiments.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0148] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0149] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0150] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A battery module balancing control method, characterized in that: A balancing control device applied to a port energy storage system, the method comprising: Obtain status data, power generation forecast data and load forecast data corresponding to each battery module in the battery pack; Determining a balancing control weight according to a proportional relationship between the power generation prediction data and the load prediction data, wherein the balancing control weight includes a discharge depth weight and a health state weight; Determining an objective function according to the state data, the discharge depth weight and the health state weight; Performing particle swarm optimization on the objective function to obtain an optimal discharge depth corresponding to each battery module within a target time interval; Obtain battery capacity data and collect current data; Obtaining a current proportionality coefficient according to the optimal discharge depth and the battery capacity data corresponding to each of the battery modules; The current proportionality coefficient is multiplied by the collected current data to obtain a target balancing current, and the target balancing current is sent to a current controller for the current controller to control the balancing current in each of the battery modules based on the target balancing current.

2. The method according to claim 1, characterized in that The target time interval is a charging cycle, and the collected current data is a charging current; and the process of obtaining a current proportionality coefficient according to the optimal discharge depth and the battery capacity data corresponding to each battery module includes: Taking the maximum value among the optimal discharge depths as the target discharge depth; Taking the maximum value among the battery capacity data as the target battery capacity; For each of the battery modules, the current proportionality coefficient is obtained according to the optimal discharge depth, the battery capacity data, the target discharge depth and the target battery capacity.

3. The method according to claim 1, characterized in that The target time interval is a discharge cycle, and the collected current data is a discharge current; and the process of obtaining a current proportionality coefficient according to the optimal discharge depth and the battery capacity data corresponding to each battery module includes: Taking the minimum value among the optimal discharge depths as the target discharge depth; Taking the minimum value among the battery capacity data as the target battery capacity; For each of the battery modules, the current proportionality coefficient is obtained according to the optimal discharge depth, the battery capacity data, the target discharge depth and the target battery capacity.

4. A battery module balancing control device, characterized in that: A balancing control device used in a port energy storage system, the device comprising: A data acquisition module is used to obtain status data, power generation prediction data and load prediction data corresponding to each battery module in the battery pack; A weight determination module, used to determine a balance control weight according to a proportional relationship between the power generation prediction data and the load prediction data, wherein the balance control weight includes a discharge depth weight and a health state weight; A current control module is used to determine an objective function based on the state data, the discharge depth weight and the health state weight; perform particle swarm optimization on the objective function to obtain the optimal discharge depth corresponding to each battery module within a target time interval; obtain battery capacity data and collected current data; obtain a current proportionality coefficient based on the optimal discharge depth and the battery capacity data corresponding to each battery module; multiply the current proportionality coefficient by the collected current data to obtain a target balancing current, and send it to a current controller for the current controller to control the balancing current in each battery module based on the target balancing current.

5. A port energy storage system, characterized in that: The port area energy storage system comprises: Battery packs, new energy system output forecasting equipment, port area load forecasting equipment, and A balancing control device is used to obtain status data corresponding to each battery module in the battery pack, obtain power generation prediction data from the new energy system output prediction device, and obtain load prediction data from the port area load prediction device; determine the balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data, and the balancing control weight includes a discharge depth weight and a health state weight; determine the objective function according to the status data, the discharge depth weight and the health state weight; perform particle swarm optimization on the objective function to obtain the optimal discharge depth corresponding to each battery module within the target time interval; obtain battery capacity data and collected current data; obtain a current proportionality coefficient according to the optimal discharge depth and the battery capacity data corresponding to each battery module; multiply the current proportionality coefficient by the collected current data to obtain a target balancing current, and send it to the current controller in the battery pack, so that the current controller can control the balancing current in each battery module based on the target balancing current.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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