Battery module equalization control method, device, system, equipment and medium

Abstract

The invention relates to a battery module equalization control method, device and system, equipment and a medium. The battery module balance control method is applied to balance control equipment in a harbor district energy storage system, and state data, power generation prediction data and load prediction data corresponding to each battery module in a battery pack are obtained; determining a balance control weight according to a proportional relation between the power generation prediction data and the load prediction data; and according to the state data and the equalization control weight, obtaining a target equalization current, and sending the target equalization current to a current controller, so that the current controller controls the equalization current in each battery module based on the target equalization current. By adopting the method, the battery module equalization control efficiency can be improved.

Description

Technical Field

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

[0002] With the green and intelligent development of the port area, the port energy storage system based on ship power lithium batteries plays an important role in the comprehensive energy system of the port area, and the secondary utilization of retired ship power lithium batteries is of great significance. In order to realize the overall retirement of the lithium battery system, it is necessary to balance the battery modules in the port energy storage system.

[0003] Most of the existing methods for balancing control of battery modules only consider a single-objective balancing control, such as achieving convergence of health status data among battery modules or battery packs in the system as quickly as possible.

[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 prior art is low. Summary of the invention

[0005] Based on this, 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 area energy storage system in response to the above technical problems.

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

[0007] Obtain status data, power generation forecast data and load forecast 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 forecast data and the load forecast data;

[0009] According to the state data and the balancing control weight, a target balancing current is obtained and sent to the current controller, so that the current controller controls the balancing current in each battery module based on the target balancing current.

[0010] In one embodiment, the balancing control weight includes a discharge depth weight and a state balancing weight. According to the state data and the balancing control weight, a target balancing current is obtained, including:

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

[0012] The objective function is optimized and solved to obtain the target balanced current.

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

[0014] The objective function is optimized by particle swarm optimization to obtain the optimal discharge depth of each battery module within the target time interval;

[0015] According to each optimal discharge depth, the target balancing current is obtained.

[0016] In one embodiment, obtaining a target balancing current according to an optimal discharge depth vector includes:

[0017] Obtain battery capacity data and collect current data;

[0018] According to the optimal discharge depth and battery capacity data corresponding to each battery module, the current proportionality coefficient is obtained;

[0019] The current proportionality coefficient is multiplied by 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; and the process of obtaining the current proportionality coefficient according to the optimal discharge depth and battery capacity data corresponding to each battery module includes:

[0021] The maximum value among the optimal discharge depths is taken as the target discharge depth;

[0022] The maximum value among the battery capacity data is taken as the target battery capacity;

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

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

[0025] The minimum value among the optimal discharge depths is taken as the target discharge depth;

[0026] The minimum value among the battery capacity data is taken as the target battery capacity;

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

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

[0029] 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;

[0030] A weight determination module, used to determine the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data;

[0031] The current control module is used to obtain the target balancing current according to the state data and the balancing control weight, and send it to the current controller so that the current controller can control the balancing current in each battery module based on the target balancing current.

[0032] In a third aspect, the present application further provides a port area energy storage system, the port area energy storage system comprising:

[0033] Battery packs, new energy system output forecasting equipment, port area load forecasting equipment, and

[0034] The balancing control device is used to obtain the status data corresponding to each battery module in the battery pack, obtain the power generation forecast data from the new energy system output forecast device, and obtain the load forecast data from the port area load forecast device; determine the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data; obtain the target balancing current according to the status data and the balancing control weight, and send it to the current controller in the battery pack, so that the current controller controls the balancing current in each battery module based on the target balancing current.

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

[0036] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method of the first aspect when the computer program is executed by a processor.

[0037] The above-mentioned battery module balancing control method, device, system, equipment and medium, one aspect of which provides a battery module balancing control method applied to a balancing control device in a port energy storage system, by obtaining the state data, power generation forecast data and load forecast data corresponding to each battery module in the battery pack; determining the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data; obtaining the target balancing current according to the state data and the balancing control weight, and sending it to the current controller, so that the current controller can control the balancing current in each of the battery modules based on the target balancing current. In this way, in the process of balancing control of the battery module, the special application scenario requirements of the port area are fully taken into account, and the state data of the battery module, the power generation forecast data and the load forecast data of the new energy power generation system are comprehensively considered, which can effectively improve the port energy storage system's absorption level of the port's new energy output during the balancing control process on the basis of ensuring that each battery module is effectively retired at the same time, thereby improving the balancing control efficiency of the battery module balancing 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 the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] FIG1 is a schematic diagram of the structure of a battery module balancing control system in one embodiment;

[0040] FIG2 is a schematic flow chart of a battery module balancing control method according to an embodiment;

[0041] FIG3 is a flow chart of a battery module balancing control method in another embodiment;

[0042] FIG4 is a schematic diagram of the balancing control effect of each battery module in one embodiment;

[0043] FIG5 is a structural block diagram of a battery module balancing control device in one embodiment;

[0044] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

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

[0047] The battery pack 102 includes a plurality of battery cells, each of which includes a state estimation module 1022, a battery module 1024, a current control module 1026, and an equalizing resistor 1028. The battery module 1024 is connected in parallel with the equalizing resistor 1028, the current control module 1026, and the state estimation module 1022, and each battery module 1024 is connected in series. 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 equalizing 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 the target time interval, obtain power generation prediction data, and send the power generation prediction data to the balancing control device 104.

[0049] The port area load prediction device 108 is connected to the balancing control device 104 and is used to predict the overall power load of the port area within the 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 status 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, so that the current control module 1026 controls the balancing current in each battery module 1024 based on the target balancing current.

[0051] The balancing 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 balancing control device 104 can be a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0052] In an exemplary embodiment, as shown in FIG2 , a battery module balancing control method is provided, which is applied to the balancing control device 104 in FIG1 , and is described by taking the balancing control device 104 as a server as an example, and includes the following steps 202 to 206 . Among them:

[0053] Step 202, obtaining status 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. SOH can reflect the degree of degradation of the battery module performance due to factors such as the number of cycles, charge and discharge depth, ambient temperature, thermal expansion and contraction, external mechanical vibration, or overcharge, overdischarge, rapid charge and discharge, etc. The state estimation module that collects 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 equivalent circuit models and thermal models, based on the operating parameters of the battery module such as charge and discharge current, voltage, and temperature, the capacity attenuation or internal resistance change of the battery module is fitted to obtain the SOH; or, the operating data of the battery module is input into a pre-trained neural network, random forest or other machine learning model 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=level V. According to the mapping relationship between the current mode level L and the balancing control weight, the balancing control weight corresponding to the current mode level L is determined.

[0061] Step 206 , obtaining a target balancing current according to the balancing control weight, and sending the target balancing current to the current controller, so that the current controller controls the balancing current in each battery module based on the target balancing current.

[0062] The above-mentioned battery module balancing control method is applied to the balancing control device in the port energy storage system, by obtaining the state data, power generation forecast data and load forecast data corresponding to each battery module in the battery pack; determining the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data; obtaining the target balancing current according to the state data and the balancing control weight, and sending it to the current controller, so that the current controller can control the balancing current in each of the battery modules based on the target balancing current. In this way, in the process of balancing control of the battery modules, the special application scenario requirements of the port area are fully taken into account, and the state data of the battery modules, the power generation forecast data and the load forecast data of the new energy power generation system are comprehensively considered. It can effectively improve the port energy storage system's absorption level of the port's new energy output during the balancing control process on the basis of ensuring that each battery module is effectively retired at the same time, thereby improving the balancing control efficiency of the battery module balancing control method.

[0063] In an exemplary embodiment, based on the embodiment shown in FIG2 , the balancing control weight in the provided method includes a discharge depth weight and a state balancing weight. The process of obtaining the target balancing current according to the state data and the balancing control weight in the provided method includes: determining the objective function according to the state data, the discharge depth weight and the state balancing weight; optimizing and solving the objective function to obtain the target balancing 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 contained in the battery pack; D n (t) is the discharge depth value of the nth battery module at the tth sampling time in the target time interval; SOH n (t) is the state data of the nth battery module at the tth sampling time in the target time interval; is the discharge depth weight; State balance weight.

[0067] In a possible implementation, in the provided method, the process of optimizing and solving the objective function to obtain the target balancing current may further include: performing particle swarm optimization processing on the objective function to obtain the optimal discharge depth corresponding to each battery module within the target time interval; and obtaining the target balancing 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 , optimize the vector It 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, particle swarm optimization refers to the optimization of the objective function J using the particle swarm optimization algorithm (PSO). The process of particle swarm optimization can 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 optimal position and the global optimal position corresponding to each particle; updating the speed and position corresponding to each particle, repeating the process of obtaining the personal optimal position and the global optimal position until the preset termination condition is reached, outputting the personal optimal position and the global optimal position, and using the personal optimal position as the optimal discharge depth of the corresponding battery module.

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

[0073] ,

[0074] in, (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, the objective function is generated based on the state data, the discharge depth weight and the state balance weight, and the objective function is optimized using the particle swarm optimization algorithm, which can avoid falling into the local optimum during the optimization process. The particle position is updated only based on the objective function value, and the calculation complexity is low, which can improve the real-time performance of the optimization process; the particle update process is independent of each other and can be calculated in parallel, which can accelerate the optimization process. The optimization method in this embodiment can achieve healthy balance of battery modules on the basis of meeting the load demand and new energy power generation and consumption requirements in the new energy port area, and has significant advantages in optimization efficiency, flexibility, adaptability and ease of implementation.

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

[0077] The purpose of the target balancing current is to gradually make the health status and remaining capacity of all battery modules consistent.

[0078] In a possible implementation, the target time interval is a charging cycle, and the collected current data is a charging 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:

[0079] The maximum value among the optimal discharge depths is taken as the target discharge depth;

[0080] The maximum value among the battery capacity data is taken as the target battery capacity;

[0081] For each battery module, a current proportionality coefficient is obtained according to the optimal discharge depth, battery capacity data, target discharge depth and 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 tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q best The maximum 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 time; It is the maximum optimal discharge depth of all battery modules in the battery pack at the tth sampling moment, that is, the target discharge depth.

[0087] In a possible implementation, the target time interval is a discharge cycle, and the collected current data is a 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] The minimum value among the optimal discharge depths is taken as the target discharge depth;

[0089] The minimum value among the battery capacity data is taken as the target battery capacity;

[0090] For each battery module, a current proportionality coefficient is obtained 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 balancing current can be expressed as:

[0094] ,

[0095] 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 D (t) is the discharge current of the battery pack at the tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q worst 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 time; It is the minimum optimal discharge depth of all battery modules in the battery pack at the tth sampling time, that is, the target discharge depth.

[0096] In this embodiment, the maximum value is used as the target discharge depth and target battery capacity for the charging cycle, which can prevent some battery modules from reaching the upper limit of charging too early due to small capacity or low discharge depth during the charging process, ensuring the maximum charging efficiency; the minimum value is used as the target discharge depth and target battery capacity for the discharge cycle, which can prevent some battery modules from running out of energy too early 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 realizing the balanced control of each battery module.

[0097] In an exemplary embodiment, as shown in FIG3 , a battery module balancing control method is provided, which is applied to the balancing control device 104 in FIG1 , and is described by taking the balancing control device 104 as a server as an example, and includes the following steps 301 to 306 . Among them:

[0098] Step 301, obtaining status 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, determining the balancing control weight according to the proportional relationship between the power generation forecast data and the load forecast data.

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

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

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

[0103] Step 306 , multiplying the current proportionality coefficient by the collected current data to obtain a target balancing current and sending the target balancing current to a current controller, so that the current controller can control the balancing current in each battery module based on the target balancing current.

[0104] In one possible implementation, the battery pack in the port energy storage system includes 10 battery modules, 10 balancing resistors, 10 current control modules, 10 state estimation modules, 1 new energy system output prediction device, 1 port 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 data SOH uploaded by the 10 state estimation modules to the balancing control device are 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 target time interval is 60 minutes long. gen =4800kWh, load forecast data Q load =8600kWh; combined with the matching rules in the above embodiment, the proportional relationship k i Take k1=2.0; k2=1.3; k3=0.9; k4=0.5 respectively. Therefore, it is determined that the current mode level L=IV.

[0106] The mapping relationship between the current mode level L and the balance control weight is as follows:

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

[0108] L I II III IV V #team# 0.85 0.70 0.55 0.30 0.05 #team# 0.15 0.30 0.45 0.7 0.95

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

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

[0111] .

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

[0113] .

[0114] in, To optimize the vector; D n (t)(n=1,2,…,10;t=1,2,…,60) is the discharge depth 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:

[0116]

[0117] .

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

[0119] ,

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

[0121] ,

[0122] 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 tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q best The maximum 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 time; It is the maximum 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 discharge cycle, and the collected current data is the discharge current, the current proportional coefficient corresponding to the discharge cycle can be expressed as:

[0124] ,

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

[0126] ,

[0127] 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 D (t) is the discharge current of the battery pack at the tth sampling moment; Q n is the remaining available capacity of the nth battery module; Q worst 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 time; It is the minimum optimal discharge depth of all battery modules in the battery pack at the tth sampling time, that is, the target discharge depth.

[0128] In this embodiment, since the target time interval is a discharge cycle, the balancing current is calculated based on the corresponding formula above, and the current control module controls the balancing resistor current in parallel with each battery module to make it equal to the calculated balancing current, completing the balancing control in the current cycle. Subsequently, the next balancing cycle begins, 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 shown in FIG. 4 , 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 displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, 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, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0131] Based on the same inventive concept, the embodiment of the present application also provides a battery module balancing control device for implementing the battery module balancing control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more battery module balancing control device embodiments provided below can refer to the limitations of the battery module balancing control method above, and will not be repeated here.

[0132] In an exemplary embodiment, as shown in FIG5 , a battery module balancing control device is provided, which is applied to a balancing control device in a port energy storage system. The device includes: a data acquisition module 502 , a weight determination module 504 and a current control module 506 , wherein:

[0133] A data acquisition module 502 is used 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, used to determine a balancing control weight according to a proportional relationship between power generation prediction data and load prediction data;

[0135] The current control module 506 is used to obtain a target balancing current according to the state data and the balancing control weight, and send the target balancing current to the current controller, so that the current controller controls the balancing current in each battery module based on the target balancing current.

[0136] In one embodiment, the balancing control weight includes a discharge depth weight and a state balancing weight. The current control module 506 is further used to determine an objective function according to the state data, the discharge depth weight and the state balancing weight; and optimize and solve the objective function to obtain a target balancing 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 an optimal discharge depth corresponding to each battery module within a target time interval; and obtain a target balancing current according to each optimal discharge depth.

[0138] In one embodiment, the current control module 506 is also used to obtain battery capacity data and collect current data; obtain a current proportionality coefficient according to the optimal discharge depth and battery capacity data corresponding to each battery module; and multiply the current proportionality coefficient by the collected current data to obtain a target balancing current.

[0139] In one embodiment, the target time interval is the charging cycle, and the collected current data is the charging current; the current control module 506 is also used to take the maximum value of each optimal discharge depth as the target discharge depth; take the maximum value of each battery capacity data as the target battery capacity; for each battery module, the current proportional coefficient is obtained according to the optimal discharge depth, battery capacity data, target discharge depth and target battery capacity.

[0140] In one embodiment, the target time interval is the discharge cycle, and the collected current data is the discharge current; the current control module 506 is also used to take the minimum value of each optimal discharge depth as the target discharge depth; the minimum value of each battery capacity data is taken as the target battery capacity; for each battery module, the current proportional coefficient is obtained according to the optimal discharge depth, battery capacity data, target discharge depth and target battery capacity.

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

[0142] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to 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 state 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 an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery module balancing control method is implemented.

[0143] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[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 used 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 must comply with relevant regulations.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0149] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0150] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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 forecast data and the load forecast data; A target balancing current is obtained according to the state data and the balancing control weight, and sent to a current controller, so that the current controller controls 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 balancing control weight includes a discharge depth weight and a state balancing weight, and obtaining a target balancing current according to the state data and the balancing control weight includes: Determining an objective function according to the state data, the discharge depth weight and the state balance weight; The objective function is optimized and solved to obtain the target balancing current.

3. The method according to claim 2, characterized in that The optimizing and solving the objective function to obtain the target balancing current includes: Performing particle swarm optimization on the objective function to obtain an optimal discharge depth corresponding to each battery module within a target time interval; The target balancing current is obtained according to each of the optimal discharge depths.

4. The method according to claim 3, characterized in that The step of obtaining the target balancing current according to the optimal discharge depth vector includes: 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 the target balancing current.

5. The method according to claim 4, 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.

6. The method according to claim 4, 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.

7. 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 balancing control weight according to a proportional relationship between the power generation prediction data and the load prediction data; A current control module is used to obtain a target balancing current according to the state data and the balancing control weight, and send the target balancing current to a current controller, so that the current controller controls the balancing current in each of the battery modules based on the target balancing current.

8. 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 a balancing control weight according to the proportional relationship between the power generation prediction data and the load prediction data; obtain a target balancing current according to the status data and the balancing control weight, 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.

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

10. 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 6 are implemented.

Images