A multi-dimensional data fusion battery cluster dynamic reconstruction method

Abstract

This invention belongs to the field of energy storage technology for new energy power systems, specifically relating to a method for dynamic reconfiguration of battery clusters based on multi-dimensional data fusion. The method includes: acquiring multi-dimensional data information of battery modules and dynamically allocating weight coefficients for the multi-dimensional data, wherein the multi-dimensional data information includes voltage, current, and temperature; calculating the centroid of the fused data of the battery cluster, and, in conjunction with the multi-dimensional data information and weight coefficients of the battery modules, calculating the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster; adjusting the dynamic reconfiguration access duration of each battery module based on the weighted distance and offset direction of each battery module; and iteratively executing the above steps to control the access and cut-off durations of each battery module in the battery cluster, thereby controlling the multi-dimensional data information of the battery modules to tend towards consistency during charging and discharging.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology for new energy power systems, and specifically relates to a method for dynamic reconfiguration of battery clusters through multi-dimensional data fusion. Background Technology

[0002] With the continuous expansion of installed capacity of new energy power generation, its proportion in the power grid is increasing. However, new energy power generation is characterized by small unit capacity, large number, and dispersed distribution, and exhibits significant intermittency, volatility, and randomness. A high proportion of new energy grid connection will inevitably bring unprecedented challenges to the power system's supply and demand balance and its safe and stable control.

[0003] As a key component in regulating the supply-demand imbalance between renewable energy generation and the power system, optimizing the performance of energy storage systems is crucial. Among the key challenges is how to accurately assess battery status and achieve efficient balancing to improve the overall performance and reliability of energy storage systems.

[0004] Currently, traditional dynamic reconfiguration schemes have significant drawbacks. Specifically, these schemes often rely solely on voltage or current data as the basis for reconfiguration. Since a single criterion cannot comprehensively assess battery status from multiple dimensions, this leads to inaccurate battery status assessments, highlighting inconsistencies in battery status and ultimately resulting in poor balancing effects, severely impacting the performance and stability of the energy storage system. Summary of the Invention

[0005] To overcome the problems in the prior art, this invention proposes a method for dynamic reconstruction of battery clusters through multi-dimensional data fusion.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides a method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion, comprising the following sub-steps: Step 100: Obtain multi-dimensional data information of the battery module and dynamically allocate weight coefficients for the multi-dimensional data, wherein the multi-dimensional data information includes voltage, current, and temperature; Step 200: Calculate the centroid of the fused data of the battery cluster, and combine the multi-dimensional data information and weight coefficients of the battery modules to calculate the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster. Step 300: Adjust the dynamic reconfiguration access time of each battery module based on the weighted distance and offset direction of each battery module; Step 400: Repeat steps 100-300 to control the access and disconnection time of each battery module in the battery cluster, and control the multi-dimensional data information of the battery module to tend to be consistent during the charging and discharging process.

[0007] Furthermore, in step 100, the weighting coefficients for dynamically allocating multidimensional data include: The voltage weighting coefficient of the battery module is dynamically adjusted based on the SOC of the battery module. The current weighting coefficient of the battery module is dynamically adjusted according to the rated operating rate of the battery module. The temperature weighting coefficient of the battery module is dynamically adjusted based on the temperature of the battery module.

[0008] Furthermore, based on the SOC of the battery module, the voltage weighting coefficient of the battery module is dynamically adjusted, including: When SOC > preset SOC threshold, the voltage weighting coefficient is the preset voltage weighting coefficient; When SOC ≤ preset SOC threshold, the voltage weighting coefficient is calculated based on the current SOC value. for: ; In the above formula, Indicates the current SOC value; This indicates the preset SOC threshold.

[0009] Furthermore, based on the rated operating rate of the battery module, the current weighting coefficient of the battery module is dynamically adjusted, including: When the real-time current magnitude is greater than the rated operating rate, the current weighting coefficient is assigned a preset current weighting coefficient. When the real-time current magnitude is less than or equal to the rated operating rate, the current weighting coefficient is calculated based on the real-time current magnitude. : ; In the above formula, Indicates the rated operating rate; I It represents electric current.

[0010] Furthermore, based on the temperature of the battery module, the temperature weighting coefficient of the battery module is dynamically adjusted, including: When the battery module temperature is higher than the preset temperature, the temperature weighting coefficient is increased to the preset temperature weighting coefficient. When the battery module temperature is lower than the preset temperature, the temperature weighting coefficient is calculated based on the real-time temperature. : ; In the above formula, T represents the temperature of the battery module; This indicates the preset temperature.

[0011] Further, in step 200, the centroid of the fused data of the battery cluster is calculated, including: A battery cluster consists of multiple battery modules. The centroid of the fused data of the battery cluster is calculated. : ; in, This represents the median value of the voltage sorting of all battery modules in the battery cluster. This represents the median value of the current sorting of all battery modules in the battery cluster. This represents the median value among all battery module temperature values ​​sorted by battery cluster.

[0012] Furthermore, in step 200, by combining the multi-dimensional data information and weighting coefficients of the battery modules, the weighted distance from the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster is calculated. ,include: ; In the above formula, This represents the voltage weighting coefficient; This represents the current weighting coefficient; This represents the temperature weighting coefficient; V i Indicates the first i The voltage of each battery module; I i Indicates the first i The current of each battery module; T i Indicates the first i The temperature of each battery module; If the weighted distance of a certain battery module is greater than a preset threshold This battery module is cut out and does not participate in the dynamic reconfiguration strategy.

[0013] Further, in step 200, combining the multi-dimensional data information and weighting coefficients of the battery modules, the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster are calculated: ; Based on the offset direction The positive or negative sign indicates whether the control system should increase or decrease the battery module connection time.

[0014] Furthermore, in the charging and discharging state, In this way, reduce the time required for reconfiguration and access; At the same time, increase the reconfiguration access time of the battery module.

[0015] Further, in step 300, the dynamic reconfiguration access time of each battery module is adjusted based on the weighted distance and offset direction of each battery module, including: Assuming the battery module's previous reconfiguration cycle access time was... Therefore, the access duration for the next reconstruction cycle is: ; In the above formula, Indicates the access duration for the next reconstruction cycle; This represents the adjustment coefficient, used to control the degree of influence of weighted distance and offset direction on access duration adjustment.

[0016] Compared with the prior art, the present invention has the following technical effects: This invention can collect multi-dimensional data of battery module voltage, current, and temperature, dynamically allocate weight coefficients to the multi-dimensional data, and control the dynamic access and cut-off time of each battery module by combining the results of multi-dimensional data. This ensures that the voltage, current, and temperature of the entire battery cluster are in the optimal charging and discharging state, which is conducive to the battery cluster exerting maximum charging and discharging performance and extending the service life of the battery module. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solutions proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. Specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] Traditional dynamic reconfiguration schemes rely on single voltage or current data as the basis for reconfiguration. However, a single criterion cannot evaluate the battery status from multiple dimensions, leading to inconsistent battery status and poor balancing effects. This embodiment provides a multi-dimensional data fusion-based dynamic reconfiguration method for battery clusters. This method can collect multi-dimensional data on the voltage, current, and temperature of battery modules, dynamically allocate weight coefficients to the multi-dimensional data, and comprehensively control the dynamic access and cut-off times of each battery module based on the multi-dimensional data results. This ensures that the overall voltage, current, and temperature of the battery modules in the battery cluster are in the optimal charging and discharging state.

[0021] A multi-dimensional data fusion method for dynamic reconfiguration of battery clusters is based on a dynamic reconfiguration system for battery clusters, referring to... Figure 1 The entire system includes an energy switch and energy network cards (NICs). Each battery pack is controlled by an energy NIC, which can collect voltage, current, and temperature data of the battery modules and control the connection and disconnection of the battery modules. The energy switch and all energy NICs are interconnected via a network communication switch. The topology of the energy NICs is the same as that of the battery packs, allowing for the formation of any number of parallel and series battery clusters. The energy NICs are installed inside the battery packs, and their network ports are led to the outer casing of the battery packs.

[0022] In one embodiment of the present invention, reference is made to... Figure 2 This paper presents a method for dynamic reconstruction of battery clusters using multi-dimensional data fusion, comprising the following steps: Step 100: Obtain multi-dimensional data information of the battery module and dynamically allocate weight coefficients for the multi-dimensional data, wherein the multi-dimensional data information includes voltage, current, and temperature; Step 200: Calculate the centroid of the fused data of the battery cluster, and combine the multi-dimensional data information and weight coefficients of the battery modules to calculate the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster. Step 300: Adjust the dynamic reconfiguration access time of each battery module based on the weighted distance and offset direction of each battery module; Step 400: Repeat steps 100-300 to control the access and disconnection time of each battery module in the battery cluster, and control the multi-dimensional data information of the battery module to tend to be consistent during the charging and discharging process.

[0023] The following is a detailed explanation of each of the above steps: Step 100: Obtain multi-dimensional data information and dynamically allocate weight coefficients for the multi-dimensional data.

[0024] As an example, step 100 specifically includes: Step 110: Define the multi-dimensional data information of the battery module, including voltage, current, and temperature.

[0025] The voltage, current, and temperature of the battery module are respectively measured using... V, I, T In other words, for example, the first i The voltage of each battery module is used V i Indicates that current is represented by I i It means that the first i Battery module temperature T i express.

[0026] Step 120: Obtain multi-dimensional data information, including the battery module's SOC and rated operating rate, and dynamically allocate weighting coefficients for the multi-dimensional data, i.e., determine the weighting coefficients for the battery module's voltage, current, and temperature. .

[0027] Step 120 specifically includes: Step 1201: Dynamically adjust the voltage weighting coefficient of the battery module based on the SOC of the battery module.

[0028] For lithium iron phosphate batteries, the voltage-state-of-charge (SOC) curve exhibits significant nonlinear characteristics: When SOC > 30%, the battery is in a plateau phase, and the voltage changes very little with SOC. At this point, the voltage is difficult to accurately reflect the actual remaining battery capacity. Over-reliance on voltage signals for equalization or state estimation may lead to misjudgments, such as mistaking voltage fluctuations for SOC differences. Therefore, when the battery module voltage is above 30%, the voltage weighting coefficient is reduced to a fixed threshold of 0.1. This fixed threshold is an empirical value summarized from actual project applications of lithium iron phosphate batteries.

[0029] When SOC ≤ 30%: Voltage changes drastically as SOC decreases, and the correlation between voltage and SOC strengthens, making it a more reliable criterion. The voltage weighting coefficient is calculated based on the current SOC value. for: ; In the above formula, This indicates the current SOC value.

[0030] Step 1202: Dynamically adjust the current weighting coefficient of the battery module according to the rated operating rate of the battery module.

[0031] When the real-time current deviates from the rated operating rate, the current weighting coefficient of the battery module is dynamically adjusted. The rated operating rate of the battery module represents the design-allowed continuous charge / discharge rate (1C = rated capacity / 1 hour).

[0032] When the real-time current magnitude is greater than the rated operating rate, the current weighting coefficient is directly assigned a fixed value. When the real-time current magnitude is less than or equal to the rated operating rate, the current weighting coefficient is calculated based on the real-time current magnitude. : ; In the above formula, Indicates the rated operating rate.

[0033] For example, for a 0.5C battery module, when the actual charge / discharge current is >0.5C, the current weight is increased to 0.7; when the charge / discharge current is <0.5C, the current weight coefficient is calculated based on the real-time current magnitude. : .

[0034] Step 1203: Dynamically adjust the temperature weighting coefficient of the battery module according to the temperature of the battery module.

[0035] The temperature weighting coefficient of the battery module is adjusted based on the real-time temperature. For lithium iron phosphate batteries, when the battery module temperature is >50 degrees Celsius, the temperature weighting coefficient is increased to 0.7; when the battery module temperature is <50 degrees Celsius, the temperature weighting coefficient is calculated based on the real-time temperature. : ; In the above formula, T represents the temperature of the battery module. In this embodiment, T is 50 degrees Celsius. This temperature value was determined by considering both the upper limit of the charging temperature for lithium iron phosphate batteries and experience gained from actual projects. Specifically, the upper limit of the charging temperature for lithium iron phosphate batteries is 55 degrees Celsius, but in engineering practice, a 5-degree Celsius margin is usually reserved to ensure the safety and stability of the battery charging process. Taking both factors into account, T was ultimately determined to be 50 degrees Celsius.

[0036] Step 200: Calculate the centroid of the fused data of the battery cluster, and combine the multi-dimensional data information and weight coefficients of the battery modules to calculate the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster.

[0037] As an example, step 200 specifically includes the following sub-steps: Step 210: The battery cluster consists of multiple battery modules. Calculate the centroid of the fused data of the battery cluster: ; in, This represents the median value of the voltage sorting of all battery modules in the battery cluster. This represents the median value of the current sorting of all battery modules in the battery cluster. This represents the median value among all battery module temperature values ​​sorted by battery cluster.

[0038] Statistical analysis of multi-dimensional data for each battery module: ; Step 220: Based on the multi-dimensional data information of the battery module, the centroid of the fused data of the battery cluster, and the weight coefficient of the battery module, calculate the distance from the multi-dimensional data information point of each battery module to the data centroid. Weighted distance : ; If the weighted distance of a certain battery module is greater than a preset threshold The system determines that the multi-dimensional data information of this battery module has too large a deviation (which may be due to abnormal sensor sampling or a confirmed fault in the battery module). The system then switches this battery module out of the dynamic reconstruction strategy.

[0039] Step 230: Based on the multi-dimensional data information of the battery module, the centroid of the fused data of the battery cluster, and the weight coefficient of the battery module, calculate the offset direction between the multi-dimensional data information of the battery module and the centroid of the fused data of the battery cluster. ; Based on the offset direction The positive or negative sign indicates whether the control system should increase or decrease the battery module connection time during charging and discharging. If this occurs, it indicates that the overall charging and discharging voltage, current, and temperature of the battery module are too high, and the reconfiguration access time needs to be reduced. In this case, the reconfiguration access time of the battery module should be increased; At this time, the battery module maintains the same access duration as the previous cycle.

[0040] Step 300: Adjust the dynamic reconfiguration access time of each battery module based on the weighted distance and offset direction of each battery module.

[0041] when season ; when season ; Assuming the battery module's previous reconfiguration cycle access time was... Therefore, the access duration for the next reconstruction cycle is: ; In the above formula, Indicates the access duration for the next reconstruction cycle; This represents the adjustment coefficient, used to control the degree of influence of weighted distance and offset direction on access duration adjustment.

[0042] Step 400: Repeat steps 100-300 to iterate the access and cut-off times of each battery module in the battery cluster, control the voltage, current and temperature of the battery modules to be consistent during the charging and discharging process, and thus maximize the overall charging and discharging performance of the battery cluster.

[0043] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A multi-dimensional data fusion battery cluster dynamic reconfiguration method, characterized in that, Includes the following sub-steps: Step 100: Obtain multi-dimensional data information of the battery module and dynamically allocate weight coefficients for the multi-dimensional data, wherein the multi-dimensional data information includes voltage, current, and temperature; Step 200: Calculate the centroid of the fused data of the battery cluster, and combine the multi-dimensional data information and weight coefficients of the battery modules to calculate the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster. In step 200, the centroid of the fused data of the battery cluster is calculated, including: The battery cluster is composed of a plurality of battery modules, and the fusion data centroid of the battery cluster is calculated : ； wherein, represents the median value of the sorting of all battery module voltages of the battery cluster, represents the median value of the sorting of all battery module currents of the battery cluster, represents the median value of the sorting of all battery module temperature values of the battery cluster; In step 200, combining the multi-dimensional data information and weighting coefficients of the battery modules, the weighted distance from the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster is calculated. ,include: ； In the above formula, This represents the voltage weighting coefficient; This represents the current weighting coefficient; This represents the temperature weighting coefficient; V i Indicates the first i The voltage of each battery module; I i Indicates the first i The current of each battery module; T i Indicates the first i The temperature of each battery module; If the weighted distance of a certain battery module is greater than a preset threshold This battery module is cut out and does not participate in the dynamic reconfiguration strategy; In step 200, combining the multi-dimensional data information and weighting coefficients of the battery modules, the weighted distance and offset direction of the multi-dimensional data information of each battery module to the centroid of the fused data of the battery cluster are calculated: ； Based on the offset direction The positive or negative sign indicates whether the control system should increase or decrease the battery module connection time; Step 300: Adjust the dynamic reconfiguration access time of each battery module based on the weighted distance and offset direction of each battery module; Step 400: Repeat steps 100-300 to control the access and disconnection time of each battery module in the battery cluster, and control the multi-dimensional data information of the battery module to tend to be consistent during the charging and discharging process.

2. The method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 1, characterized in that, In step 100, the weighting coefficients for dynamically allocating multidimensional data include: The voltage weighting coefficient of the battery module is dynamically adjusted based on the SOC of the battery module. The current weighting coefficient of the battery module is dynamically adjusted according to the rated operating rate of the battery module. The temperature weighting coefficient of the battery module is dynamically adjusted based on the temperature of the battery module.

3. The method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 2, characterized in that, Based on the battery module's SOC, the voltage weighting coefficient of the battery module is dynamically adjusted, including: When SOC > preset SOC threshold, the voltage weighting coefficient is the preset voltage weighting coefficient; When SOC ≤ preset SOC threshold, the voltage weighting coefficient is calculated based on the current SOC value. for: ； In the above formula, Indicates the current SOC value; This indicates the preset SOC threshold.

4. The method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 2, characterized in that, Based on the rated operating rate of the battery module, the current weighting coefficient of the battery module is dynamically adjusted, including: When the real-time current magnitude is greater than the rated operating rate, the current weighting coefficient is assigned a preset current weighting coefficient. When the real-time current magnitude is less than or equal to the rated operating rate, the current weighting coefficient is calculated based on the real-time current magnitude. : ； In the above formula, Indicates the rated operating rate; I It represents electric current.

5. The method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 2, characterized in that, The temperature weighting coefficient of the battery module is dynamically adjusted based on the battery module temperature, including: When the battery module temperature is higher than the preset temperature, the temperature weighting coefficient is increased to the preset temperature weighting coefficient. When the battery module temperature is lower than the preset temperature, the temperature weighting coefficient is calculated based on the real-time temperature. : ； In the above formula, T represents the temperature of the battery module; This indicates the preset temperature.

6. The method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 1, characterized in that, In the charging and discharging state, In this way, reduce the time required for re-accessing the system. At the same time, increase the reconfiguration access time of the battery module; At this time, the battery module maintains the same access duration as the previous cycle.

7. A method for dynamic reconstruction of battery clusters based on multi-dimensional data fusion according to claim 1 or 6, characterized in that, In step 300, the dynamic reconfiguration access time of each battery module is adjusted based on the weighted distance and offset direction of each battery module, including: Assuming the battery module's previous reconfiguration cycle access time was... Therefore, the access duration for the next reconstruction cycle is: ； In the above formula, Indicates the access duration for the next reconstruction cycle; This represents the adjustment coefficient, used to control the degree of influence of weighted distance and offset direction on access duration adjustment.

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