Hierarchical calibration method of energy storage battery and terminal

Abstract

The invention discloses a hierarchical calibration method of an energy storage battery and a terminal, and the method comprises the steps: obtaining an SOC value of each cell in a preset partition of the energy storage battery, and carrying out the real-time statistics of a maximum SOC difference value between the cells in the preset partition; when the maximum SOC difference value is in a first preset interval, controlling the battery cells in the preset subareas to carry out electric quantity balance calibration until the maximum SOC difference value falls into a second preset interval; the SOC difference value upper limit in the second preset interval is smaller than the SOC difference value lower limit in the first preset interval; and when the maximum SOC difference value exceeds a first preset interval, controlling all the battery cells in the preset partition to perform full charge calibration. According to the invention, on the premise that the consistency of the battery is gradually improved, the absorption capability of the energy storage system to the photovoltaic energy and the overall energy utilization efficiency are effectively improved, and the calibration strategy is enabled to better meet the operation requirements in a photovoltaic power generation scene.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a graded calibration method and terminal for energy storage batteries. Background Technology

[0002] In existing technologies, since energy storage batteries are usually composed of a large number of cells connected in series and parallel, during long-term operation, the cells are easily affected by factors such as manufacturing differences, aging rates, temperature environments, and inconsistent charging and discharging conditions, which can easily lead to inconsistent states of charge (SOC). Therefore, it is necessary to maintain the consistency of the cells through calibration operations.

[0003] Existing energy storage battery calibration methods mostly employ periodic full-charge calibration or full-charge calibration strategies triggered by SOC difference thresholds. Their main purpose is to eliminate SOC differences between cells by charging the entire battery pack to a high SOC state. However, in photovoltaic (PV) power generation scenarios, these calibration strategies often neglect the timing characteristics of PV output and the energy storage system's need to absorb PV power. When energy storage batteries frequently undergo full-charge calibration unnecessarily, they prematurely occupy the available capacity of the energy storage system. This results in the energy storage system being unable to absorb PV power during peak PV power generation periods, leading to curtailment and reducing the actual utilization rate of PV energy.

[0004] Furthermore, existing calibration strategies typically use a single SOC difference threshold as the trigger condition, lacking a fine-grained distinction between the degree of SOC differences among cells. This makes it difficult to flexibly select calibration methods based on the severity of inconsistencies, and easily leads to high-energy-consuming and capacity-intensive full-charge calibration operations being performed even when the SOC difference is still within a controllable range. Therefore, how to reduce unnecessary full-charge calibration while ensuring battery consistency and operational safety, and thus reserve more usable energy storage capacity for photovoltaic power generation, thereby improving the absorption capacity of photovoltaic energy, has become a pressing technical problem that needs to be solved in existing technologies. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a graded calibration method and terminal for energy storage batteries, which can improve the utilization rate of photovoltaic energy.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for grading and calibrating energy storage batteries, comprising the following steps: S1. Obtain the SOC value of each cell in the preset partition of the energy storage battery, and calculate the maximum SOC difference between the cells in the preset partition in real time. S2. When the maximum SOC difference is within the first preset range, control the cells within the preset zone to perform power balancing calibration until the maximum SOC difference falls into the second preset range; the upper limit of the SOC difference in the second preset range is less than the lower limit of the SOC difference in the first preset range. S3. When the maximum SOC difference exceeds the first preset range, control all cells in the preset partition to be fully charged for calibration.

[0007] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A grading and calibration terminal for an energy storage battery includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it performs the following steps: S1. Obtain the SOC value of each cell in the preset partition of the energy storage battery, and calculate the maximum SOC difference between the cells in the preset partition in real time. S2. When the maximum SOC difference is within the first preset range, control the cells within the preset zone to perform power balancing calibration until the maximum SOC difference falls into the second preset range; the upper limit of the SOC difference in the second preset range is less than the lower limit of the SOC difference in the first preset range. S3. When the maximum SOC difference exceeds the first preset range, control all cells in the preset partition to be fully charged for calibration.

[0008] The beneficial effects of this invention are as follows: It provides a graded calibration method and terminal for energy storage batteries. By acquiring the SOC value of each cell within a preset zone and statistically analyzing the maximum SOC difference in real time, the SOC differences between cells are divided into different preset intervals. A graded calibration strategy is adopted for each interval. When the maximum SOC difference is within the first preset interval, power equalization calibration is performed first. Full-charge calibration is only performed when the maximum SOC difference exceeds the first preset interval. Simultaneously, by limiting the upper limit of the SOC difference in the second preset interval to be less than the lower limit of the SOC difference in the first preset interval, the convergence process of the SOC difference forms a clear graded boundary and control target. Through the above settings, when the cell SOC difference is still within a controllable range, premature triggering of overall full-charge calibration is avoided, thereby reducing the occupation of the available capacity of the energy storage system during the calibration process. This reserves sufficient storage space for the electricity generated by subsequent photovoltaic power generation, enabling the energy storage system to absorb more clean electricity when photovoltaic output is high, reducing the curtailment of solar power due to insufficient capacity. While ensuring gradual improvement in battery consistency, the system effectively enhances its ability to absorb photovoltaic energy and its overall energy utilization efficiency, making the calibration strategy more aligned with the operational needs of photovoltaic power generation scenarios. Attached Figure Description

[0009] Figure 1 This is a flowchart of a graded calibration method for an energy storage battery according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a graded calibration method for an energy storage battery according to an embodiment of the present invention. Figure 3 This is a schematic diagram of a graded calibration terminal for an energy storage battery according to an embodiment of the present invention; Label Explanation: 1. A grading and calibration terminal for energy storage batteries; 2. A memory; 3. A processor. Detailed Implementation

[0010] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0011] Before detailing the embodiments of this application, some related concepts will first be explained: In existing technologies, energy storage batteries are typically composed of multiple cells connected in series and parallel. Under the influence of long-term charge-discharge cycles, environmental temperature differences, and inconsistent cell aging, inconsistencies in the state of charge (SOC) between cells can easily arise. If the SOC difference continues to widen, it will not only reduce the effective capacity of the battery pack but may also lead to overcharging or over-discharging risks, affecting the safety and lifespan of the energy storage system. To improve cell consistency, existing technologies typically employ full-charge calibration or equalization calibration methods for energy storage batteries. Full-charge calibration, by charging the entire battery pack to a high SOC state to eliminate SOC deviations between cells, is simple to operate but consumes a lot of energy and occupies available capacity of the energy storage system during calibration.

[0012] To at least solve the above problems, please refer to Figure 1 This invention provides a graded calibration method for energy storage batteries, comprising the following steps: S1. Obtain the SOC value of each cell in the preset partition of the energy storage battery, and calculate the maximum SOC difference between the cells in the preset partition in real time. S2. When the maximum SOC difference is within the first preset range, control the cells within the preset zone to perform power balancing calibration until the maximum SOC difference falls into the second preset range; the upper limit of the SOC difference in the second preset range is less than the lower limit of the SOC difference in the first preset range. S3. When the maximum SOC difference exceeds the first preset range, control all cells in the preset partition to be fully charged for calibration.

[0013] As described above, the beneficial effects of this invention are as follows: By acquiring the SOC value of each cell within a preset partition and statistically analyzing the maximum SOC difference in real time, the SOC difference between cells is divided into different preset intervals. A hierarchical calibration strategy is adopted for each interval. When the maximum SOC difference is within the first preset interval, power balancing calibration is performed first. Full-charge calibration is only performed when the maximum SOC difference exceeds the first preset interval. Simultaneously, by limiting the upper limit of the SOC difference in the second preset interval to be less than the lower limit of the SOC difference in the first preset interval, the convergence process of the SOC difference forms a clear hierarchical boundary and control target. Through the above settings, when the cell SOC difference is still within a controllable range, premature triggering of overall full-charge calibration is avoided, thereby reducing the occupation of the available capacity of the energy storage system during the calibration process. This reserves sufficient storage space for the electricity generated by subsequent photovoltaic power generation, enabling the energy storage system to absorb more clean electricity when photovoltaic output is high, reducing the curtailment of solar power due to insufficient capacity. While ensuring gradual improvement in battery consistency, the system effectively enhances its ability to absorb photovoltaic energy and its overall energy utilization efficiency, making the calibration strategy more aligned with the operational needs of photovoltaic power generation scenarios.

[0014] Specifically, when the maximum SOC difference between cells is within the first preset range, the system does not directly trigger full charge calibration, but first performs power balancing calibration and gradually converges the SOC difference to the second preset range; and when the maximum SOC difference exceeds the first preset range, full charge calibration is performed on the cells in the entire preset zone.

[0015] Since the upper limit of the SOC difference in the second preset interval is lower than the lower limit of the SOC difference in the first preset interval, this method forms a clear hierarchical control boundary during the SOC difference change process, allowing different degrees of inconsistency to correspond to different intensity calibration methods. Through this hierarchical judgment and control mechanism, frequent triggering of high-energy-consuming and high-capacity-occupying full-charge calibration operations can be avoided when the cell SOC difference is still within a controllable range. This ensures that battery consistency is gradually improved while reducing excessive occupation of the available capacity of the energy storage system.

[0016] In some embodiments, step S20 is included before step S2: The system obtains the predicted photovoltaic power generation and predicted load for a preset time period. When the predicted load is greater than or equal to the predicted photovoltaic power generation, it controls all the cells in the preset partition to be fully charged for calibration.

[0017] As described above, before performing power balancing calibration, the system introduces a judgment on the predicted photovoltaic power generation and predicted load for a preset time period. When the predicted load is greater than or equal to the predicted photovoltaic power generation, the system directly controls the cells in the preset zone to perform full-charge calibration. This allows the calibration decision to be based not only on the battery's internal SOC state but also on the energy supply and demand relationship outside the system. This enables the system to replenish battery energy in advance and complete the calibration operation simultaneously in scenarios with high load demand, reducing the scheduling complexity in subsequent operation. This makes the calibration behavior of the energy storage battery more in line with actual operating requirements, which is conducive to improving the reliability and operational stability of the energy storage system under high load conditions.

[0018] In some embodiments, step S20 further includes: When the predicted load is less than the predicted photovoltaic power generation, proceed to step S2.

[0019] As can be seen from the above description, the processing path when the predicted load is less than the predicted photovoltaic power generation is further defined. That is, the relationship between the predicted load and the predicted photovoltaic power generation is determined first, and the process of step S2 is only carried out when the predicted load is less than the predicted photovoltaic power generation. This ensures that the energy storage system always prioritizes meeting external demand during operation, and only performs energy-saving regulation in step S2 after the external demand is met.

[0020] Specifically, in this embodiment, the predicted photovoltaic power generation and predicted load are acquired and calculated by a self-developed big data platform. This big data platform is a software system that integrates data acquisition, data storage, data analysis, and predictive calculation functions, and is used to provide a predictive basis for the calibration decision of energy storage batteries.

[0021] When predicting photovoltaic (PV) power generation, the big data platform first acquires historical operating data of the PV power generation system. This historical operating data includes historical power generation data collected by the PV inverter and corresponding historical daily power generation data. Simultaneously, the big data platform obtains weather forecast data for a preset future period through a data interface. This weather forecast data includes at least parameters related to PV power generation capacity, such as solar irradiance, ambient temperature, humidity, cloud cover, and precipitation. In addition, the big data platform also stores static parameter information of the PV power plant, including the installed capacity, tilt angle, azimuth angle, conversion efficiency, and geographical location information of the PV modules.

[0022] Based on this, the big data platform constructs a time series prediction model using historical power generation and meteorological data. This model is then trained to learn the correspondence between meteorological conditions and power generation. During prediction, meteorological forecast data for a preset future time period is input into the trained prediction model, which outputs the predicted power generation curve for that period. The predicted power generation curve is then integrated over time to obtain the predicted photovoltaic power generation for the preset time period. Simultaneously, the prediction results are physically corrected based on the temperature characteristics of the photovoltaic modules and the impact of environmental factors on power generation efficiency, thereby improving the accuracy of the predicted photovoltaic power generation.

[0023] When predicting load, the big data platform acquires historical load power data and historical electricity consumption data collected by user-side smart meters or energy management systems, and analyzes them in conjunction with external correlation information related to electricity consumption behavior, such as date type and ambient temperature. Based on the historical load data and the external correlation information, the big data platform constructs a load prediction model to predict load power changes within a preset time period, and performs time integration processing on the predicted load power to obtain the predicted load amount within the corresponding preset time period.

[0024] In some implementations, step S2 involves controlling the cells within the preset partition to perform a charge balancing calibration until the maximum SOC difference falls into a second preset range, specifically including: Calculate the average SOC value of all cells within the preset partition, and control the cells with actual SOC values ​​higher than the average SOC value to transfer power to cells with actual SOC values ​​lower than the average SOC value until the maximum SOC difference falls into the second preset interval.

[0025] As described above, by calculating the average SOC value of all cells within a preset zone during the power equalization calibration process, and controlling the transfer of power from cells with actual SOC values ​​higher than the average SOC value to cells with actual SOC values ​​lower than the average SOC value, the average SOC is used as the equalization benchmark to achieve energy redistribution among cells. Compared to relying solely on the maximum or minimum value for control, this method can more accurately reflect the overall state of the cell group, effectively reduce the SOC difference between cells without introducing additional external energy, accelerate the process of the maximum SOC difference entering the second preset range, reduce energy loss during the equalization calibration process, improve calibration efficiency, and help reduce stress concentration in individual cells, thereby improving the overall consistency and stability of the battery pack.

[0026] In some embodiments, step S2, which involves controlling the cells within the preset partition to perform a power balancing calibration until the maximum SOC difference falls into a second preset range, further includes: When the average SOC value is lower than a preset threshold, the cells in the preset zone are externally charged until the average SOC value is greater than or equal to the preset threshold.

[0027] As described above, by introducing a judgment between the average SOC value and a preset threshold, external charging is performed on the cells in the preset zone when the average SOC value is lower than the preset threshold. This ensures that when the overall SOC level is low, the equalization calibration no longer relies solely on energy transfer between cells, thus avoiding the problem of limited equalization effect due to insufficient overall energy. By supplementing external energy in a timely manner during the equalization process, it not only ensures that the SOC difference between cells can be effectively converged, but also ensures that the overall SOC level of the energy storage system is within a reasonable range, thereby improving the applicability and reliability of the graded calibration method under different operating conditions.

[0028] A grading calibration terminal 1 for an energy storage battery includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3. When the processor 3 executes the computer program, it implements the steps in a grading calibration method for an energy storage battery.

[0029] Please refer to Figure 1 and Figure 2 Embodiment 1 of the present invention is as follows: This invention provides a graded calibration method for energy storage batteries, applicable to energy storage battery systems containing multiple cells. In the energy storage battery system, each cell is divided into at least one preset partition according to preset rules. Each preset partition can be independently monitored and calibrated. For example, a battery cabinet in the energy storage system is a preset partition, and the cells in this battery cabinet are subject to basically unified scheduling commands.

[0030] The graded calibration method includes the following steps: First, the state of charge (SOC) value of each cell in a preset partition of the energy storage battery is obtained, and the maximum SOC difference between cells in the preset partition is calculated in real time based on the SOC value; then, different calibration strategies are selected for control according to the correspondence between the maximum SOC difference and the preset interval.

[0031] When the maximum SOC difference is within a first preset range, the cells within the preset zone are controlled to perform a power balancing calibration to reduce the SOC difference between cells. This calibration operation continues until the maximum SOC difference falls into a second preset range. The upper limit of the SOC difference in the second preset range is lower than the lower limit of the SOC difference in the first preset range, thus forming a clear hierarchical control boundary during SOC difference changes. This setting ensures that when the cell SOC difference is within a controllable range, energy balancing is prioritized for adjustment, avoiding direct full-charge calibration. For example, the first preset range could be: the maximum SOC difference between cells is between 8% and 10%; the second preset range could be: the maximum SOC difference between cells is between 5% and 8%.

[0032] When the maximum SOC difference exceeds the first preset range, it is determined that the degree of inconsistency between cells in the preset partition is high, and all cells in the preset partition are controlled to perform full charge calibration to quickly eliminate the large SOC difference and restore cell consistency.

[0033] Simultaneously, before performing the power balancing calibration, a prediction and judgment step based on the energy supply and demand relationship is included, namely, obtaining the predicted photovoltaic power generation and predicted load within a preset time period; when the predicted load is greater than or equal to the predicted photovoltaic power generation, the cells in the preset partition are directly controlled to perform full-charge calibration to prioritize meeting the load's power demand; when the predicted load is less than the predicted photovoltaic power generation, the power balancing calibration process is entered, thereby improving the absorption capacity of photovoltaic power generation energy while ensuring the system's external power supply capacity.

[0034] Specifically, the power equalization calibration includes: calculating the average SOC value of all cells in a preset zone, and controlling the cells with actual SOC values ​​higher than the average SOC value to transfer power to cells with actual SOC values ​​lower than the average SOC value, using the average SOC value as the equalization benchmark to achieve energy redistribution among cells until the maximum SOC difference falls into a second preset range.

[0035] Furthermore, during the power equalization calibration process, when the average SOC value of the cells in the preset partition is lower than a preset threshold, the cells in the preset partition are externally charged until the average SOC value reaches or exceeds the preset threshold. This ensures that the equalization calibration can proceed smoothly when the overall SOC level is low, and keeps the overall SOC level of the energy storage battery system within a reasonable range.

[0036] Please refer to Figure 3 Embodiment two of the present invention is as follows: A grading calibration terminal 1 for an energy storage battery includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3. When the processor 3 executes the computer program, it implements the steps in a grading calibration method for an energy storage battery.

[0037] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for grading and calibrating energy storage batteries, characterized in that, Including the following steps: S1. Obtain the SOC value of each cell in the preset partition of the energy storage battery, and calculate the maximum SOC difference between the cells in the preset partition in real time. S2. When the maximum SOC difference is within the first preset range, control the cells within the preset zone to perform power balancing calibration until the maximum SOC difference falls into the second preset range; the upper limit of the SOC difference in the second preset range is less than the lower limit of the SOC difference in the first preset range. S3. When the maximum SOC difference exceeds the first preset range, control all cells in the preset partition to be fully charged for calibration.

2. The graded calibration method for an energy storage battery according to claim 1, characterized in that, Step S20 is included before step S2: The system obtains the predicted photovoltaic power generation and predicted load for a preset time period. When the predicted load is greater than or equal to the predicted photovoltaic power generation, it controls all the cells in the preset partition to be fully charged for calibration.

3. The graded calibration method for an energy storage battery according to claim 2, characterized in that, Step S20 further includes: When the predicted load is less than the predicted photovoltaic power generation, proceed to step S2.

4. The grading and calibration method for an energy storage battery according to claim 1, characterized in that, In step S2, controlling the cells within the preset partition to perform a power balancing calibration until the maximum SOC difference falls into the second preset range specifically includes: Calculate the average SOC value of all cells within the preset partition, and control the cells with actual SOC values ​​higher than the average SOC value to transfer power to cells with actual SOC values ​​lower than the average SOC value until the maximum SOC difference falls into the second preset interval.

5. The graded calibration method for an energy storage battery according to claim 4, characterized in that, In step S2, controlling the cells within the preset partition to perform power balancing calibration until the maximum SOC difference falls into the second preset range also includes: When the average SOC value is lower than a preset threshold, the cells in the preset zone are externally charged until the average SOC value is greater than or equal to the preset threshold.

6. A grading and calibration terminal for an energy storage battery, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: S1. Obtain the SOC value of each cell in the preset partition of the energy storage battery, and calculate the maximum SOC difference between the cells in the preset partition in real time. S2. When the maximum SOC difference is within the first preset range, control the cells within the preset zone to perform power balancing calibration until the maximum SOC difference falls into the second preset range; the upper limit of the SOC difference in the second preset range is less than the lower limit of the SOC difference in the first preset range. S3. When the maximum SOC difference exceeds the first preset range, control all cells in the preset partition to be fully charged for calibration.

7. A graded calibration terminal for an energy storage battery according to claim 6, characterized in that, Step S20 is included before step S2: The system obtains the predicted photovoltaic power generation and predicted load for a preset time period. When the predicted load is greater than or equal to the predicted photovoltaic power generation, it controls all the cells in the preset partition to be fully charged for calibration.

8. A graded calibration terminal for an energy storage battery according to claim 7, characterized in that, Step S20 further includes: When the predicted load is less than the predicted photovoltaic power generation, proceed to step S2.

9. A graded calibration terminal for an energy storage battery according to claim 6, characterized in that, In step S2, controlling the cells within the preset partition to perform a power balancing calibration until the maximum SOC difference falls into the second preset range specifically includes: Calculate the average SOC value of all cells within the preset partition, and control the cells with actual SOC values ​​higher than the average SOC value to transfer power to cells with actual SOC values ​​lower than the average SOC value until the maximum SOC difference falls into the second preset interval.

10. A graded calibration terminal for an energy storage battery according to claim 9, characterized in that, In step S2, controlling the cells within the preset partition to perform power balancing calibration until the maximum SOC difference falls into the second preset range also includes: When the average SOC value is lower than a preset threshold, the cells in the preset zone are externally charged until the average SOC value is greater than or equal to the preset threshold.

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