Battery charging control method, processor for multi-port topology energy storage system

Abstract

The application discloses a battery charging control method for a multi-port topology energy storage system, a processor, a multi-port topology energy storage system and a machine readable storage medium thereof, and relates to the technical field of battery energy storage optimization control. The method comprises the following steps: determining, according to battery monomer internal resistance fluctuation data, an internal resistance stability quantitative value of each battery monomer in a preset period, and determining, according to temperature change data of the battery monomer, a temperature rise quantitative value of the battery monomer in the preset period; determining the charging performance of each battery monomer according to the internal resistance stability quantitative value and the temperature rise quantitative value of each battery monomer, wherein the internal resistance stability quantitative value is positively correlated with the charging performance, and the temperature rise quantitative value is negatively correlated with the charging performance; and distributing the total power generated by a plurality of power generation ports to each battery monomer according to the charging performance of the battery monomers of the plurality of power storage ports. The method can more reasonably distribute the total power generated by the plurality of power generation ports.

Description

Technical Field

[0001] This application relates to the field of battery energy storage optimization and control technology, specifically to a battery charging control method, processor, multi-port topology energy storage system and its machine-readable storage medium for a multi-port topology energy storage system. Background Technology

[0002] When existing smart grids use battery energy storage, a uniform charging power is typically applied to all battery cells. This approach simplifies charging management, reduces system complexity, and ensures the consistency and predictability of charging strategies. However, using a uniform charging power may result in battery cells with poor charging performance being assigned excessively high charging power, or battery cells with strong charging performance being assigned excessively low charging power, thus failing to achieve efficient charging. Summary of the Invention

[0003] The purpose of this application is to provide a battery charging control method, processor, multi-port topology energy storage system and its machine-readable storage medium for a multi-port topology energy storage system, so as to solve the technical problem of how to achieve multi-battery charging power distribution to achieve efficient charging in the prior art.

[0004] To achieve the above objectives, the first aspect of this application provides a battery charging control method for a multi-port topology energy storage system. The multi-port topology energy storage system includes multiple power generation ports and multiple energy storage ports. The energy storage ports store electricity through batteries, and the multiple power generation ports are used to charge the batteries in the multiple energy storage ports. The battery charging control method includes:

[0005] Acquire the internal resistance fluctuation data and temperature change data of each battery cell at each energy storage port within a preset period.

[0006] For each battery cell, the internal resistance stability quantification value of the battery cell within a preset period is determined based on the battery cell's internal resistance fluctuation data, and the temperature rise quantification value of the battery cell within a preset period is determined based on the battery cell's temperature change data.

[0007] The charging performance of each battery cell is determined based on the quantified values ​​of internal resistance stability and temperature rise of each battery cell. The quantified value of internal resistance stability is positively correlated with the charging performance, while the quantified value of temperature rise is negatively correlated with the charging performance.

[0008] The total current power generation of multiple power generation ports is allocated to each individual battery cell based on the charging performance of the battery cells in multiple energy storage ports.

[0009] In this embodiment, the battery cell internal resistance fluctuation data includes the battery cell static internal resistance and the battery cell internal resistance fluctuation component that changes over time; determining the battery cell internal resistance stability quantification value within a preset period based on the battery cell internal resistance fluctuation data includes: determining the cumulative deviation of the battery cell internal resistance within the preset period based on the battery cell internal resistance fluctuation component; and determining the battery cell internal resistance stability quantification value based on the cumulative deviation of the battery cell internal resistance, the battery cell static internal resistance, and the duration of the preset period.

[0010] In the embodiments of this application, the internal resistance stability quantization value Determined based on formula (1):

[0011] (1)

[0012] in, This represents the cumulative deviation of the internal resistance of a single battery cell. This refers to the static internal resistance of a single battery cell. The duration of the preset period, It is a regulating factor.

[0013] In this embodiment of the application, determining the temperature rise quantization value of a battery cell in a preset period based on the temperature change data of the battery cell includes: determining the temperature change rate of the battery cell at each moment in the preset period based on the temperature change data; determining the cumulative temperature change rate energy of the battery cell in the preset period based on the temperature change rate at each moment; and determining the temperature rise quantization value of the preset period based on the cumulative temperature change rate energy and the maximum temperature change rate among the temperature change rates at each moment.

[0014] In this embodiment of the application, the cumulative temperature change rate energy Determined based on formula (2):

[0015] (2)

[0016] in, , and These are the start and end times of the preset period, respectively. for The rate of temperature change at any given time k This is the weight sensitivity coefficient.

[0017] In the embodiments of this application, the temperature rise quantization value Determined based on formula (3):

[0018] (3)

[0019] in, This represents the maximum rate of temperature change among all time-varying rates.

[0020] In this embodiment, the current total power generation of multiple power generation ports is allocated to each battery cell based on the charging performance of the battery cells in multiple energy storage ports. This includes: determining the charging performance scale range of each battery cell based on the maximum and minimum charging performance of the battery cells in multiple energy storage ports; normalizing the charging performance of each battery cell according to the charging performance scale range to obtain the normalized charging performance coefficient of each battery cell; determining the membership degree of the battery cell with good charging performance and the membership degree of the battery cell with poor charging performance based on the normalized charging performance coefficient and a preset membership function; determining the charging power factor of the battery cell based on the membership degree of the battery cell with good charging performance and the membership degree of the battery cell with poor charging performance; and allocating the current total power generation of multiple power generation ports to each battery cell based on the ratio of the charging power factor of each battery cell to the sum of the charging power factors of all battery cells in multiple energy storage ports.

[0021] In this embodiment, the preset membership function includes a membership function for good charging performance and a membership function for poor charging performance; determining the membership degree of a battery cell with good charging performance and the membership degree of a battery cell with poor charging performance based on the normalized charging performance coefficient and the preset membership function includes: determining the membership degree of a battery cell with good charging performance based on the normalized charging performance coefficient and the membership function for good charging performance. Determined based on formula (4):

[0022] (4)

[0023] in, To normalize the charging performance coefficient, Preset coefficients for membership functions that provide good charging performance;

[0024] The membership degree of good charging performance of individual battery cells is determined based on the normalized charging performance coefficient and the membership function of good charging performance. Determined based on formula (5):

[0025] (5)

[0026] in, Preset coefficients for the membership function of poor charging performance;

[0027] Charging power factor of individual battery cells Determined based on formula (6):

[0028] (6)

[0029] in, and These are the fuzzy logic weight parameters.

[0030] In this embodiment of the application, the battery charging control method further includes: obtaining the total power generation of multiple power generation ports in the first cycle; and if the total power generation in the first cycle is less than a preset power generation threshold, obtaining the battery cell internal resistance fluctuation data and battery cell temperature change data of each battery cell in the multiple energy storage ports during the preset cycle.

[0031] In this embodiment of the application, the battery charging control method further includes: when the total power generation in the first cycle is greater than or equal to a preset power generation threshold, determining a uniform charging power based on the ratio of the current total power generation of multiple power generation ports to the total number of battery cells in multiple energy storage ports; and selecting the smaller of the uniform charging power and the maximum safe charging power of the battery cell as the charging power of the battery cell.

[0032] The second aspect of this application provides a processor configured to retrieve instructions from memory and, when executing the instructions, to implement the battery charging control method for a multi-port topology energy storage system provided in the first aspect of this application.

[0033] A third aspect of this application provides a multi-port topology energy storage system, comprising: multiple power generation ports; multiple energy storage ports, wherein the energy storage ports store electricity via batteries, and the multiple power generation ports are used to charge the batteries of the multiple energy storage ports; and a processor provided in the second aspect of this application.

[0034] The fourth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute a battery charging control method for a multi-port topology energy storage system according to the first aspect of this application.

[0035] The above technical solution considers the stability of a single battery cell during charging by quantifying its internal resistance and the temperature rise during charging by quantifying its temperature rise. By combining these two factors, the charging performance of a single battery cell is obtained. Therefore, the charging power of a single battery cell can be dynamically adjusted based on its stability and temperature rise during charging within a preset cycle. This allows battery cells with good charging performance to make full use of limited power resources, while preventing overload or overheating of battery cells with poor performance. This not only effectively protects the health of the battery cells and extends the lifespan of the battery pack, but also allows for a more rational allocation of the total power output of multiple power generation ports when the total power output of multiple power generation ports is insufficient, thereby improving energy storage efficiency.

[0036] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0038] Figure 1 The illustration shows a schematic flowchart of a battery charging control method for a multi-port topology energy storage system according to an embodiment of this application;

[0039] Figure 2 The schematic diagram illustrates a flow chart of another battery charging control method for a multi-port topology energy storage system according to an embodiment of this application;

[0040] Figure 3 The illustration shows a schematic flowchart of another battery charging control method for a multi-port topology energy storage system according to an embodiment of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0042] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the solution has been or necessarily been used.

[0043] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0044] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0045] Batteries, with their high energy density, reversible charge-discharge capability, and long lifespan, are widely used in grid energy storage systems. Especially in smart grids, battery energy storage can quickly respond to load fluctuations, regulate power supply stability, and balance energy differences between renewable energy sources (such as solar and wind power) and the grid. However, in scenarios like smart grids, all battery cells use the same charging power. When grid power generation is insufficient, continuing to use the same charging power may lead to battery cells with degraded charging performance receiving excessively high charging power, further reducing their lifespan, or battery cells with strong charging performance receiving excessively low charging power, thus failing to achieve efficient charging. This results in wasted electricity, reduced energy storage efficiency, and prevents the overall energy storage system from maximizing its stored power generation. During power shortages, this charging method may not provide sufficient power support, affecting grid stability and operational efficiency.

[0046] Based on the above analysis, this application provides a battery charging control method for a multi-port topology energy storage system. This method can be applied to multi-port topology energy storage systems. A multi-port topology energy storage system may include multiple power generation ports and multiple energy storage ports. The energy storage ports store electricity through batteries, and the multiple power generation ports are used to charge the batteries in the multiple energy storage ports.

[0047] Figure 1 The illustration schematically shows a flowchart of a battery charging control method for a multi-port topology energy storage system according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a battery charging control method for a multi-port topology energy storage system, which may include the following steps:

[0048] S102. Obtain the internal resistance fluctuation data and temperature change data of each battery cell in each energy storage port within a preset period.

[0049] In step S102, the preset period can be set by the staff according to the actual situation of the power grid; this case does not impose specific restrictions. The number of battery cells in each energy storage port can be single or multiple. The internal resistance fluctuation data of the battery cell can be the change of the battery cell's internal resistance over time within the preset period, and the temperature change data of the battery cell can also be the change of the battery cell's internal resistance over time within the preset period.

[0050] S104. For each battery cell, determine the stable internal resistance quantification value of the battery cell within a preset period based on the battery cell's internal resistance fluctuation data, and determine the temperature rise quantification value of the battery cell within a preset period based on the battery cell's temperature change data.

[0051] S106. Determine the charging performance of each battery cell based on the internal resistance stability quantification value and temperature rise quantification value of each battery cell. The internal resistance stability quantification value is positively correlated with the charging performance, and the temperature rise quantification value is negatively correlated with the charging performance.

[0052] S108. Based on the charging performance of the battery cells in the multiple energy storage ports, the current total power generation of the multiple power generation ports is allocated to each battery cell.

[0053] In step S108, the charging performance of the battery cells in the multiple energy storage ports may have a proportional relationship or a normalized relative relationship. The current total power generation of the multiple power generation ports may be allocated to each battery cell according to the proportional relationship or the normalized relative relationship.

[0054] The battery charging control method for multi-port topology energy storage systems provided in this application considers the stability of individual battery cells during charging by using a quantitative value of internal resistance stability and the temperature rise of individual battery cells during charging by using a quantitative value of temperature rise. By combining the two, the charging performance of individual battery cells is obtained. Therefore, the charging power of individual battery cells can be dynamically adjusted based on the stability and temperature rise of individual battery cells during charging within a preset period. This allows battery cells with good charging performance to make full use of limited power resources, while avoiding overload or overheating of battery cells with poor performance. This not only effectively protects the health of individual battery cells and extends the service life of the battery pack, but also allows for a more rational allocation of the current total power generation of multiple power generation ports when the current total power generation of multiple power generation ports is insufficient, thereby improving energy storage efficiency. This enhances the support capability of battery cell-based energy storage systems for grid operation and improves the stability and operating efficiency of the grid.

[0055] like Figure 2 As shown, in some embodiments of this application, steps S102 to S108 of the battery charging control method for a multi-port topology energy storage system can be executed when the total power generation or total power output of multiple power generation ports is insufficient.

[0056] Taking the insufficient total power generation of multiple power generation ports as an example, in some embodiments of this application, the battery charging control method for a multi-port topology energy storage system further includes:

[0057] S202. Obtain the total power generation of multiple power generation ports in the first cycle;

[0058] If the total power generation in the first cycle is less than the preset power generation threshold, proceed to step S102.

[0059] The first cycle can be, for example, a manually set time period for checking the total power generation, such as 5 minutes. The total power generation can be the integral of the total power generation of multiple power generation ports relative to time within the first cycle. The preset power generation threshold can be the sum of the maximum allowable charging capacity of each battery cell in the first cycle. The maximum allowable charging capacity of the battery cell is, for example, the product of the battery's maximum charging power at the time of manufacture and the first cycle.

[0060] As an example, the power generation capacity of the power generation port can be determined based on formula (7):

[0061] (7)

[0062] in, Indicates the first i Each power generation port is in Actual output power at any given time Indicates the first i The basic output power (rated power of the equipment) of each power generation port. This indicates the power offset value after adjustments due to environmental or equipment conditions, such as the adjustment value of photovoltaic power due to changes in solar irradiance.

[0063] Real-time power generation at power generation ports can be obtained through the power grid monitoring system. The power sources for these ports can include renewable energy (such as solar and wind power) and traditional power generation methods (such as thermal and hydropower). The acquired real-time power generation data is recorded in time-series format, representing the power output of each power generation port at each point in time. By monitoring the real-time power generation of multiple power generation ports, the system can ensure an accurate understanding of the current power generation status. This real-time data provides a foundation for subsequent power generation analysis.

[0064] Power offset value after adjustment due to environmental or equipment conditions This data can be obtained through real-time monitoring systems and environmental data integration. Specifically, by utilizing sensors and data acquisition systems (such as temperature sensors, light intensity sensors, and wind speed sensors), data on environmental factors (such as air temperature, light intensity, wind speed, and humidity) and equipment operating status (such as equipment load, health status, and maintenance information) can be collected in real time. This data is compared with the equipment's standard output characteristics and calculated in real time using algorithmic models (such as regression analysis and machine learning models) to adjust the power output of the generator. For example, the power output of a photovoltaic power generation system adjusts with changes in solar radiation intensity, while the power output of a wind power system is directly related to wind speed. In this way, the power deviation caused by changes in the environment and equipment status can be accurately calculated, providing precise data support for subsequent calculations of power generation reference values.

[0065] The total power generation of multiple power generation ports in the first cycle can be determined based on formula (8):

[0066] (8)

[0067] in, This represents the total power generation in the first cycle. N This indicates the total number of power generation ports. and These represent the start and end times of the first cycle, respectively. Formula (8) accurately calculates the power generation within the first short-term monitoring window by integrating the power output curve of each power generation port, ensuring that the fluctuation of power generation is taken into account.

[0068] like Figure 2 As shown, in some embodiments of this application, when the total power generation in the first cycle is greater than or equal to a preset power generation threshold, the charging power of a single battery cell can be determined based on the following steps:

[0069] S204. Determine the unified charging power based on the ratio of the current total power generation of multiple power generation ports to the total number of battery cells in multiple energy storage ports.

[0070] S206. Select the smaller of the uniform charging power and the maximum safe charging power of the battery cell as the charging power of the battery cell.

[0071] When the total power generation of multiple power generation ports in the first cycle is greater than or equal to a preset power generation threshold, the power generation capacity of these ports is sufficient to meet the charging needs of multiple energy storage ports. There may even be surplus power available for other purposes (such as power transmission to the grid or grid load regulation). In this case, using a uniform charging power to charge all individual battery cells reduces management complexity and improves operational consistency. A uniform charging power simplifies charging management, ensuring all battery cells operate under the same charging conditions and avoiding complex power allocation processes. This uniform charging power strategy is suitable when power generation is sufficient, as energy resources are abundant, and each individual battery cell can be charged at near-full power within its safe range, thereby improving charging efficiency and maximizing energy storage.

[0072] As an example,

[0073] During periods of sufficient power generation, a unified charging power strategy should be comprehensively set based on the safe charging range and power generation capacity of individual battery cells to ensure that the charging needs of each individual battery cell are met, while avoiding overload or excessive waste of power resources. The unified charging power is determined by formula (9):

[0074] (9)

[0075] in, A uniform charging power is allocated to each battery cell. The maximum safe charging power for a single battery cell.

[0076] When power generation is sufficient, each battery cell can be charged at the highest possible power, thereby shortening charging time and improving charging efficiency. However, it is essential to ensure that the charging power does not exceed the safe charging limit of the battery cell. In the formula This means that the current total power generation from multiple power generation ports is evenly distributed among each battery cell. If the average power distribution is less than the maximum charging power of the battery cell, the average power distribution is used as the unified charging power; otherwise, the maximum charging power of the battery cell is used as the unified charging power. This setting method ensures that power generation resources are fully utilized without overloading the battery cells, maintaining the consistency of the charging strategy and the safety of system operation.

[0077] In some embodiments of this application, the battery cell internal resistance fluctuation data includes the battery cell static internal resistance and the battery cell internal resistance fluctuation component that changes over time; the step S104 of determining the battery cell internal resistance stability quantization value within a preset period based on the battery cell internal resistance fluctuation data may include: determining the cumulative deviation of the battery cell internal resistance within the preset period based on the battery cell internal resistance fluctuation component; and determining the battery cell internal resistance stability quantization value based on the cumulative deviation of the battery cell internal resistance, the battery cell static internal resistance, and the duration of the preset period.

[0078] Small fluctuations in the internal resistance of a single battery cell during charging generally indicate good cell performance. This is because fluctuations in internal resistance directly reflect the stability of the internal chemical reactions and the efficiency of ion transport. If the internal resistance remains relatively stable during charging, it indicates a strong balance in the electrochemical reactions within the battery, high uniformity of electrode material activity, and undamaged battery structural integrity—all characteristics closely related to good battery performance. Conversely, large fluctuations in internal resistance may indicate problems such as uneven reactions, degradation of active materials, deposit formation, or structural damage, thus affecting charging efficiency and overall performance. Therefore, the quantified value of internal resistance stability determined based on internal resistance fluctuation data can serve as one of the important indicators of a single battery cell's charging performance.

[0079] As an example, at any given moment Battery cell internal resistance fluctuation data For example, formula (10):

[0080] (10)

[0081] in, This refers to the static internal resistance of a single battery cell. for The internal resistance fluctuation component of a single battery cell at any given time reflects the characteristics of internal resistance fluctuation.

[0082] Understandably, static internal resistance refers to the inherent internal resistance characteristic of a single battery cell under constant conditions (e.g., without significant current fluctuations or dynamic load changes). It is primarily determined by the battery's material properties and structure, including the conductivity of the electrode materials, the ion conduction impedance of the electrolyte, and the interfacial impedance between the electrodes and the electrolyte. Static internal resistance is often considered the battery's "reference internal resistance," reflecting its fundamental electrochemical performance during charging or discharging.

[0083] By using the internal resistance fluctuation data of individual battery cells That is, the individual battery cells in The internal resistance value at any given time is decomposed into static and dynamic components, which can clearly capture the transient characteristics of internal resistance changes during charging, providing a basis for subsequent analysis.

[0084] In some embodiments of this application, the cumulative deviation of the battery cell internal resistance within a preset period can be determined based on formula (11):

[0085] (11)

[0086] in, The cumulative deviation represents the overall severity of internal resistance fluctuations during the charging process. The rate of change of dynamic internal resistance reflects the speed of fluctuation. and These are the start and end times of the preset cycle, respectively.

[0087] In some embodiments of this application, the internal resistance stabilization quantization value It can be determined based on formula (1):

[0088] (1)

[0089] in, This represents the cumulative deviation of the internal resistance of a single battery cell. This refers to the static internal resistance of a single battery cell. The duration of the preset period, It is a regulating factor.

[0090] Internal resistance stability quantization value The value range is set within the interval of 0 to 1 (left open, right closed), and the adjustment factor is... Sensitivity used to adjust the quantization value of internal resistance stability.

[0091] As can be seen from the internal resistance stability quantification value, within a preset period, a larger internal resistance stability quantification value, generated after analyzing the fluctuation of the battery cell's internal resistance during charging, indicates better charging performance of the battery cell. This is because a larger internal resistance stability quantification value means a smaller fluctuation in internal resistance during charging, reflecting the high stability of the battery's internal chemical reactions and charge transport processes. This stability is usually closely related to the battery's health, material uniformity, and structural integrity, indicating that the battery cell can efficiently complete energy conversion during charging. Conversely, a smaller internal resistance stability quantification value indicates larger internal resistance fluctuations, which may be caused by battery aging, degradation of active materials, or internal non-uniformity. This leads to reduced charging efficiency, indicating poor charging performance. Therefore, the internal resistance stability quantification value is an important reference indicator for quantifying the charging performance of a battery cell.

[0092] In some embodiments of this application, determining the temperature rise quantization value of a battery cell in a preset period based on the temperature change data of the battery cell in step S104 may include: determining the temperature change rate of the battery cell at each moment in the preset period based on the temperature change data; determining the cumulative temperature change rate energy of the battery cell in the preset period based on the temperature change rate at each moment; and determining the temperature rise quantization value of the preset period based on the cumulative temperature change rate energy and the maximum temperature change rate among the temperature change rates at each moment.

[0093] A rapid temperature change in a battery cell over charging time typically indicates poor cell performance. This is because a rapid temperature change means the battery generates more heat during charging, potentially due to uneven internal electrochemical reactions, high internal resistance, or decreased energy conversion efficiency caused by material degradation. Excessive heat accelerates battery material aging, further reducing battery performance. Furthermore, a rapid temperature rise may indicate poor heat dissipation, leading to a concentrated accumulation of heat in a short period. This not only affects charging efficiency but may also pose safety hazards (such as overheating or thermal runaway). Therefore, a rapid temperature rise is a significant indicator of poor battery performance and warrants attention.

[0094] As an example, the rate of temperature change at any given moment can be, for instance, as follows:

[0095] (12)

[0096] in, Indicates that the battery cell is in Temperature at any moment This indicates the rate of temperature change, that is, the amount of temperature change per unit time.

[0097] In some embodiments of this application, the cumulative temperature change rate energy Based on formula (2):

[0098] (2)

[0099] in, , and These are the start and end times of the preset period, respectively. for The rate of temperature change at any given time k The weight sensitivity coefficient is used to control the amplification effect of the weight. By using the nonlinear weighted integral method as shown in formula (2), the contribution of a large temperature change rate to the overall temperature rise characteristics can be highlighted, and the weaknesses of the battery thermal management capability can be captured.

[0100] In some embodiments of this application, the temperature rise quantization value It can be determined based on formula (3):

[0101] (3)

[0102] in, This represents the maximum rate of temperature change among all time-varying rates.

[0103] In formula (3) This term can perform a nonlinear mapping of the maximum temperature change rate, which is used to enhance the performance in cases with a fast temperature response. The generated temperature rise quantization value... (The range of values ​​is) The closer the value is to 1, the higher the battery's thermal management capability and energy conversion efficiency; conversely, the lower the value, the lower the temperature rise. A lower value indicates a stronger thermal effect during battery charging and poorer heat dissipation.

[0104] As can be seen from the quantified temperature rise value, within a preset period, the larger the quantified temperature rise value generated by analyzing the response rate of the battery cell temperature to the charging time, the worse the charging performance of the battery cell. A larger quantified temperature rise value means that the battery has a higher rate of temperature change during charging, accumulating more heat, reflecting low internal electrochemical reaction efficiency or poor heat dissipation capacity. This may be due to high internal resistance, material degradation, or incomplete energy conversion caused by design defects, resulting in excessive heat generation. Conversely, a smaller quantified temperature rise value indicates a lower rate of temperature change, stronger thermal management capabilities during charging, and higher energy conversion efficiency, indicating better charging performance of the battery cell. Therefore, the quantified temperature rise value is an important indicator for evaluating battery charging performance and thermal stability.

[0105] In some embodiments of this application, the charging performance of a single battery cell can be determined by formula (13):

[0106] (13)

[0107] in, The performance coefficient, which expresses the charging performance of a single battery cell, , These are the internal resistance stability quantization values. and temperature rise quantification value The preset proportional coefficient, and , All are greater than 0. The preset proportionality coefficient here ( and () refers to the coefficient of performance in calculation At that time, it is used to balance or weight different indicators (internal resistance stability quantification value) and temperature rise response quantization value These are weighting factors that contribute to the final performance coefficient. The specific values ​​of these preset proportional coefficients are usually pre-set based on actual application requirements or experimental data to reflect the importance of internal resistance stability and temperature rise response. For example: A larger (corresponding to a stable internal resistance quantification value) indicates that the impact of battery internal resistance stability on charging performance is given more weight when calculating the performance coefficient; if... A larger (corresponding to a larger temperature rise quantification value) indicates a greater focus on the battery's temperature rise characteristics.

[0108] The aforementioned proportional coefficients allow for greater flexibility in evaluating the charging performance of individual battery cells, enabling customized optimization of the charging performance assessment based on actual needs and ensuring the final performance coefficient. This better aligns with the actual needs of specific scenarios. Therefore, the significance of the aforementioned proportional coefficient lies in adjusting the relative importance of the two key indicators, thereby improving the performance coefficient of the calculation. It can comprehensively reflect the actual performance characteristics of the battery. As can be seen from the performance coefficient, within a preset period, the larger the performance value of the internal resistance stability quantification generated after analyzing the fluctuation of the internal resistance of the battery cell during the charging process, and the smaller the performance value of the temperature rise quantification generated after analyzing the response speed of the battery cell temperature change with charging time, the larger the performance coefficient of the battery cell's chargeability, indicating that the battery cell has better charging performance, and vice versa.

[0109] In some embodiments of this application, the charging performance of a single battery cell can be determined by a deep learning model. Taking formula (13) as an example, in step S106, for any single battery cell, the internal resistance stability quantization value and temperature rise quantization value of the battery cell can be input into a pre-trained deep learning model, and the pre-trained deep learning model outputs the preset scaling factor in formula (13). and This allows us to determine the charging performance of individual battery cells.

[0110] Understandably, a pre-trained deep learning model refers to an artificial intelligence model that has been trained on a large amount of historical data. This model is capable of accurately predicting the target variable (i.e., the charging performance of a single battery cell) based on input feature data (such as quantized values ​​of internal resistance stability and temperature rise response). Through supervised learning or other training methods, the model learns the complex nonlinear relationship between input features and output performance from historical data and, after training, possesses the ability to evaluate performance on new data. In this embodiment, the training phase of the pre-trained deep learning model uses a large amount of historical data containing battery charging state, health parameters, and final performance. By continuously optimizing the loss function, the model can extract key patterns from the input features, thereby achieving accurate prediction of battery charging performance.

[0111] In this embodiment, the deep learning model may include a multi-layered neural network (such as a fully connected neural network, a convolutional neural network, or a long short-term memory network) to capture the complex relationship between battery performance characteristics and charging performance. For example, the model may need to identify how the stability of the battery's internal chemical reactions affects charging efficiency from the internal resistance stability quantification value, and how the battery's heat dissipation characteristics determine the battery's thermal stability from the temperature rise quantification value. Through these features, the deep learning model can comprehensively judge the overall charging performance of the battery. During training, this model may also incorporate other auxiliary features during the battery charging process (such as the SOC curve and voltage change rate) to further improve its prediction accuracy and applicability.

[0112] Understandably, the deep learning model in this application embodiment may include multiple training data sets in the training set during the training process. Each training data set includes the internal resistance stability quantization value and temperature rise quantization value of a battery cell, as well as the charging performance of the battery cell. When the charging performance of a battery cell is determined by formula (13), the charging performance of the battery cell in each training data set can be determined by a preset scaling factor. and Alternative. A single training dataset represents a single charge cycle of a battery cell. The training dataset can be selected based on factors such as the SOC curve and voltage change rate of the battery cell during the charging process, choosing a charging cycle in which the battery cell shows the least aging and has the shortest charging time.

[0113] The internal resistance stability quantification value and the temperature rise quantification value are input into the pre-trained deep learning model. The model will calculate the performance coefficient as shown in formula (13) based on the knowledge learned during the training process. The performance coefficient reflects the overall charging performance of the battery cell. The generation process of the performance coefficient reflects the result of feature fusion and comprehensive analysis of the input data by the deep learning model. The internal resistance stability quantification value reflects the electrochemical stability of the battery, while the temperature rise quantification value reflects the thermal management capability. The model can generate a value that can quantitatively characterize the charging performance by combining these two indicators through nonlinear calculation and feature weighting. The advantage of the deep learning model lies in its powerful pattern recognition capability. Even if the relationship between the input data is complex or has certain nonlinear characteristics (for example, the temperature rise quantification value may have a nonlinear relationship with the charging performance in some battery performance degradation stages), the model can still accurately predict the charging performance of each battery cell by learning the patterns in the historical data.

[0114] See Figure 3 In some embodiments of this application, in order to reasonably allocate the current total power generation of multiple power generation ports to the battery cells of multiple energy storage ports, step S108 may include:

[0115] S302. Determine the charging performance scale range of each battery cell based on the maximum and minimum charging performance among the charging performance of the battery cells in multiple energy storage ports.

[0116] S304. Normalize the charging performance of each battery cell according to the charging performance scale range to obtain the normalized charging performance coefficient of each battery cell.

[0117] S306. Determine the membership degree of good charging performance and poor charging performance of battery cells based on the normalized charging performance coefficient and the preset membership function; determine the charging power factor of battery cells based on the membership degree of good charging performance and poor charging performance of battery cells.

[0118] S308. Based on the ratio of the charging power factor of each battery cell to the sum of the charging power factors of all battery cells in multiple energy storage ports, the current total power generation of multiple power generation ports is allocated to each battery cell.

[0119] As an example, the normalized charging performance coefficient can be determined based on formula (14):

[0120] (14)

[0121] in, For use in expressing the first u The performance coefficient of the charging performance of an individual battery cell To normalize the charging performance coefficient, and These are the performance coefficients for the maximum and minimum charging performance among battery cells with multiple energy storage ports. After normalization, the battery cell with better performance... The value of a single battery cell is close to 1, indicating poor performance. Close to 0.

[0122] In some embodiments of this application, the preset membership functions include membership functions for good charging performance and membership functions for poor charging performance; determining the membership degrees of good and poor charging performance of a battery cell based on the normalized charging performance coefficients and the preset membership functions includes: determining the membership degree of good charging performance of a battery cell based on the normalized charging performance coefficients and the membership function for good charging performance. Determined based on formula (4):

[0123] (4)

[0124] in, To normalize the charging performance coefficient, Preset coefficients for membership functions that provide good charging performance;

[0125] The membership degree of good charging performance of individual battery cells is determined based on the normalized charging performance coefficient and the membership function of good charging performance. Determined based on formula (5):

[0126] (5)

[0127] in, Preset coefficients for the membership function of poor charging performance;

[0128] Charging power factor of individual battery cells Determined based on formula (6):

[0129] (6)

[0130] in, and These are the fuzzy logic weight parameters.

[0131] The membership function uses a Gaussian function to perform fuzzy classification of battery performance, separating high-performing battery cells from low-performing ones. Battery cells with good charging performance will receive a larger power adjustment factor. Poor-performing battery cells will be assigned a smaller power adjustment factor. The power adjustment factor provides a basis for subsequent power allocation.

[0132] In some embodiments of this application, the total power generation of multiple power generation ports is allocated to each battery cell based on the ratio of the charging power factor of each battery cell to the sum of the charging power factors of all battery cells in multiple energy storage ports. This can be achieved based on formula (15):

[0133] (15)

[0134] in, To be assigned to the u The charging power of each battery cell This represents the current total power generation capacity of multiple power generation ports. For the first u The charging power factor of each battery cell F This refers to the total number of individual battery cells. j Indicates the index of a single battery cell. For the first j The charging power factor of each individual battery cell. (This is achieved through the charging power factor.) The normalization process ensures that power allocation meets power generation limits, while dynamically allocating more power to high-performance battery cells and less power to low-performance battery cells.

[0135] Through this dynamic adjustment, the system can more precisely control the charging process of each battery cell, improving charging efficiency and extending battery life. Ultimately, this strategy ensures that the energy storage system can maximize the use of electrical resources, improve energy storage capacity, and effectively balance the battery charging process, reducing power waste, especially when grid power generation is insufficient.

[0136] In summary, this application employs a unified charging power strategy to charge all battery cells when power generation is sufficient, effectively simplifying charging management, reducing system complexity, and ensuring the consistency of charging strategies and operational stability. When power generation is insufficient, real-time monitoring of battery cell charging data and extraction of key performance characteristics, combined with deep learning models, allows for accurate prediction of battery performance. Furthermore, fuzzy logic is used to dynamically adjust charging power allocation, ensuring that high-performing battery cells fully utilize limited power resources, while preventing overload or overheating of low-performing cells. This significantly improves energy storage efficiency when power generation is insufficient, maximizes battery charging capacity, effectively protects the health of battery cells, and extends the lifespan of the battery pack. Ultimately, this enhances the energy storage system's support for grid operation and improves grid stability and operational efficiency.

[0137] This application also provides a processor configured to retrieve instructions from memory and, when executing the instructions, to implement the above-described battery charging control method for a multi-port topology energy storage system.

[0138] This application also provides a multi-port topology energy storage system, including: multiple power generation ports and multiple energy storage ports. The energy storage ports store electricity via batteries, and the multiple power generation ports are used to charge the batteries of the multiple energy storage ports; the processor provided in the above embodiment.

[0139] This application also provides a machine-readable storage medium storing instructions for causing a machine to execute the above-described battery charging control method for a multi-port topology energy storage system.

[0140] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0144] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0145] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0146] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0147] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0148] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A battery charging control method for a multi-port topology energy storage system, characterized in that, The multi-port topology energy storage system includes multiple power generation ports and multiple energy storage ports. The energy storage ports store electricity through batteries, and the multiple power generation ports are used to charge the batteries of the multiple energy storage ports. The battery charging control method includes: Acquire the internal resistance fluctuation data and temperature change data of each battery cell at each energy storage port within a preset period. For each battery cell, the internal resistance stability quantification value of the battery cell within the preset period is determined based on the battery cell internal resistance fluctuation data of the battery cell, wherein the battery cell internal resistance fluctuation data includes the battery cell static internal resistance and the battery cell internal resistance fluctuation component that changes with time. The internal resistance stability quantization value Determined based on formula (1): ；（1） in, This represents the cumulative deviation of the internal resistance of the individual battery cell. The static internal resistance of the battery cell is given. The duration of the preset period, The adjustment factor is used to regulate the sensitivity of the internal resistance stability quantization value; The cumulative deviation of the internal resistance of the battery cell is achieved based on formula (11): ；（11） in, The cumulative deviation represents the overall severity of internal resistance fluctuations during the charging process. The rate of change of dynamic internal resistance reflects the speed of fluctuation. and These are the start and end times of the preset cycle, respectively; The temperature rise quantization value of the battery cell in the preset cycle is determined based on the temperature change data of the battery cell; The temperature rise quantification value Determined based on formula (3): ；（3） in, This represents the maximum rate of temperature change among all the rates of temperature change at various times. This represents the energy at the rate of cumulative temperature change. The cumulative temperature change rate energy Determined based on formula (2): ；（2） in, , and These are the start and end times of the preset period, respectively. for The rate of temperature change at any given time k This is the weight sensitivity coefficient; The charging performance of each battery cell is determined based on the quantified value of its internal resistance stability and the quantified value of its temperature rise. The quantified value of its internal resistance stability is positively correlated with the charging performance, and the quantified value of its temperature rise is negatively correlated with the charging performance. The total current power generation of the multiple power generation ports is allocated to each individual battery cell based on the charging performance of the individual battery cells in the multiple energy storage ports.

2. The battery charging control method according to claim 1, characterized in that, The step of allocating the current total power generation of the multiple power generation ports to each of the battery cells based on the charging performance of the battery cells in the multiple energy storage ports includes: The charging performance range of each battery cell is determined based on the maximum and minimum charging performance of the battery cells in the plurality of energy storage ports. The charging performance of each battery cell is normalized according to the charging performance scale range to obtain the normalized charging performance coefficient of each battery cell. The membership degrees of the battery cells with good charging performance and those with poor charging performance are determined based on the normalized charging performance coefficient and the preset membership function. The charging power factor of the battery cell is determined based on the membership degree of the cell with good charging performance and the membership degree of the cell with poor charging performance. The total power generation of the plurality of power generation ports is allocated to each of the battery cells based on the ratio of the charging power factor of each battery cell to the sum of the charging power factors of all battery cells in the plurality of energy storage ports.

3. The battery charging control method according to claim 2, characterized in that, The preset membership functions include membership functions with good charging performance and membership functions with poor charging performance; The step of determining the membership degrees of the battery cells with good charging performance and poor charging performance based on the normalized charging performance coefficient and the preset membership function includes: The membership degree of the battery cell with good charging performance is determined based on the normalized charging performance coefficient and the membership function of good charging performance. Determined based on formula (4): ；（4） in, The normalized charging performance coefficient is... The preset coefficients of the membership function for good charging performance; The membership degree of the battery cell with good charging performance is determined based on the normalized charging performance coefficient and the membership function of good charging performance. Determined based on formula (5): ；（5） in, The preset coefficients for the membership function of the poor charging performance; The charging power factor of the battery cell Determined based on formula (6): ；（6） in, and These are the fuzzy logic weight parameters.

4. The battery charging control method according to claim 1, characterized in that, The battery charging control method further includes: Obtain the total power generation of the multiple power generation ports in the first cycle; If the total power generation in the first cycle is less than a preset power generation threshold, the battery cell internal resistance fluctuation data and battery cell temperature change data of each battery cell in the multiple energy storage ports are obtained within the preset cycle.

5. The battery charging control method according to claim 4, characterized in that, The battery charging control method further includes: If the total power generation in the first cycle is greater than or equal to the preset power generation threshold, the unified charging power is determined based on the ratio of the current total power generation of the multiple power generation ports to the total number of battery cells in the multiple energy storage ports. The smaller of the uniform charging power and the maximum safe charging power of the battery cell is selected as the charging power of the battery cell.

6. A processor, characterized in that, The method is configured to retrieve instructions from memory and, when executing the instructions, implement the battery charging control method for a multi-port topology energy storage system according to any one of claims 1 to 5.

7. A multi-port topology energy storage system, characterized in that, include: Multiple power generation ports; Multiple energy storage ports, wherein the energy storage ports store energy through batteries, and the multiple power generation ports are used to charge the batteries of the multiple energy storage ports; The processor according to claim 6.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the battery charging control method for a multi-port topology energy storage system according to any one of claims 1 to 5.

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